Separation of contributions to metrology data

Mutual reference to related applications [0001] This application is incorporated herein by reference in its entirety, US Patent Application No. 62 / 298,367, filed February 22, 2016, filed September 1, 2016. Claims the priority of U.S. Patent Application No. 62 / 382,764 filed and U.S. Patent Application No. 62 / 459,327 filed on February 15, 2017.

[0002] The present disclosure relates to methods of improving the performance of device manufacturing processes. The method may be used in connection with a lithographic device or a metrology device.

[0003] A lithographic apparatus is a machine that applies a desired pattern to a target portion of a substrate. Lithographic equipment can be used, for example, in the manufacture of integrated circuits (ICs). In that situation, an alternative patterning device, called a mask or reticle, may be used to generate a circuit pattern corresponding to the individual layers of the IC, and this pattern is of a radiation sensitive material (resist). An image can be formed on a target portion (eg, including a portion of one or several dies) on a substrate having a layer (eg, a silicon wafer). Generally, a single substrate contains a network of adjacent target portions that are continuously exposed. Known lithographic devices use a so-called stepper and a beam to scan a pattern in one direction (the "scan" direction), where each target area is illuminated by exposing the entire pattern to the target area in one go. Includes, so-called scanners, in which each target portion is illuminated by scanning the substrate synchronously or antiparallel to this direction.

[0004] Prior to transferring the circuit pattern from the patterning device to the substrate, the substrate may undergo various procedures such as priming, resist coating, and soft baking. After exposure, the substrate may undergo other procedures such as post-baking (PEB), developing, hard-baking, and measuring / inspection of transferred circuit patterns. This numerous procedure is used as the basis for creating individual layers of devices, such as ICs. The substrate may then undergo various processes such as etching, ion implantation (doping), metallization, oxidation, chemical mechanical polishing, etc., all intended to finish the individual layers of the device. If the device requires several layers, the entire procedure or variants thereof are repeated for each layer. Ultimately, there is a device at each target portion on the board. These devices can then be separated from each other by techniques such as dicing or sewing, so that individual devices can be mounted on carriers, connected to pins, and so on.

[0005] Therefore, manufacturing devices, such as semiconductor devices, generally process substrates (eg, semiconductor wafers) using a number of fabrication processes to form the various features of the device and multiple layers. include. Such layers and features are generally manufactured and processed using, for example, deposition, lithography, etching, chemical mechanical polishing, and ion implantation. A plurality of devices may be manufactured on a plurality of dies on a substrate and then separated into individual devices. This device manufacturing process can be considered as a patterning process. The patterning process involves patterning steps such as optical and / or nanoimprint lithography using the patterning device in a lithography apparatus to transfer the pattern on the patterning device to the substrate, and generally (but optionally) develop. Includes one or more related pattern processing steps such as resist development with an apparatus, baking of a substrate with a baking tool, etching with a pattern with an etching apparatus, and the like.

[0006] Disclosed herein are to obtain the first contribution of a modelable process variable to the pattern metrology data on the substrate and to obtain the unmodeled process variable to the pattern metrology data. It is a method including acquiring the second contribution of the above and acquiring the metrology data by integrating the first and second contributions by a hardware computer.

[0007] According to one embodiment, the metrology data is one selected from critical dimension (CD), critical dimension uniformity (CDU), side wall angle, edge position, overlay, focus, and / or pattern shift. Or includes a plurality.

[0008] According to one embodiment, the metrology data includes pattern group statistics.

[0009] According to one embodiment, the unmodeled process variable is a characteristic of the process downstream of substrate exposure.

[0010] According to one embodiment, the process is the development of a resist layer on a substrate.

[0011] According to one embodiment, the process is etching the substrate.

[0012] According to certain embodiments, a process variable that is not modeled is a characteristic of the substrate.

[0013] According to one embodiment, a process variable that is not modeled is a characteristic of the resist layer on the substrate.

[0014] According to one embodiment, the value of a process variable that is not modeled is unknown.

[0015] According to certain embodiments, integrating the first and second contributions adds up the first and second contributions, or adds the first and second contributions. Including convolution.

[0016] Disclosed herein is the removal of the contribution of the second process variable to the metrology data of the pattern on the substrate and the contribution of the first process variable to the metrology data by the hardware computer. It is a method that involves obtaining by.

[0017] According to one embodiment, the first process variable is modelable.

[0018] According to one embodiment, the acquisition of the contribution of the first process variable is by modeling.

[0019] According to one embodiment, the contribution of the first process variable is a non-linear function of the second process variable.

[0020] According to one embodiment, the first process variable is not modeled.

[0021] According to certain embodiments, the contribution of the first process variable is determined experimentally or empirically.

[0022] Disclosed herein are the contribution of changes in one or more process variables of a second group to the first pattern of metrology data on the substrate, the first to the metrology data. Obtaining the contribution of changes in one or more process variables in a group by removing them from the metrology data, and changes in one or more process variables in a second group to the first pattern of metrology data. Based on the contribution of, the hardware computer obtains the contribution of the change of one or more process variables of the second group to the metrological data of the second pattern on the board, and is a method including. ..

[0023] According to one embodiment, the method is based on the contribution of changes in one or more of the second group of process variables to the second pattern of metrological data, one or more of the first group. It further includes reducing the probability of defects in the second pattern by adjusting multiple process variables.

[0024] According to one embodiment, the contribution of changes in one or more process variables of the second group to the second pattern of metrology data does not require the acquisition of the second pattern of metrology data. To be acquired.

[0025] According to one embodiment, the probability of defects in the second pattern is reduced without making measurements in the second pattern.

[0026] According to one embodiment, the method determines the contribution of changes in one or more process variables of the first group to the second pattern of metrology data, the first to the second pattern of metrology data. It further includes obtaining the contribution of changes in one or more of the two groups of process variables by removing them from the second pattern of metrology data.

[0027] According to one embodiment, the contribution of changes in one or more process variables in the first group to the metrological data in the second pattern is one or more in the first group in the second pattern. Obtained without retrieving the values of multiple process variables.

[0028] According to one embodiment, the method contributes changes to one or more process variables in the first group to the second pattern of metrology data, a second to the second pattern of metrology data. By adjusting one or more of the process variables in one or more of the first group based on the contribution of changes in one or more of the process variables in the first group, or both. It further includes reducing the probability of defects in pattern 2.

[0029] Disclosed herein are the contribution of changes in one or more process variables of a second group to the first pattern of metrology data, the first to the first pattern of metrology data. Substrate based on what is obtained by removing the contribution of changes in process variables in one group and the contribution of changes in one or more process variables in the second group to the metrology data of the first pattern. Obtaining the contribution of changes in one or more of the process variables in the second group to the second pattern of metrology data above, and by the hardware computer, the second to the second pattern of metrology data. A method that includes obtaining a subprocess window (subPW) that the process variables of the first group for the second pattern cover, based on the contribution of changes in one or more process variables of the group.

[0030] According to one embodiment, the first group includes all process variables that can be modeled, and the second group contains all process variables that are not modeled.

[0031] According to one embodiment, the method is defective in the second pattern by adjusting the value of one or more process variables in the first group of process variables based on the sub-PW. Further includes reducing the probability of.

Disclosed herein are the contribution of changes in one or more modelable process variables to the metrology data, and one or more of the second groups to the metrology data. Obtaining estimates of hotspot metrology data by integrating the contributions of changes in multiple unmodeled process variables, and by hardware computers, defects in hotspots based on the estimates of metrology data. It is a method that includes determining whether or not there is.

[0033] According to certain embodiments, estimates are obtained without making measurements on hotspots.

[0034] Disclosed herein are the contribution of changes in one or more process variables in a second group to the metrological data of a pattern of one or more process variables in the first group. Hard to obtain the contribution of change by removing it from the metrology data and to determine the contribution of change to one or more of the process variables in the second group to the metrology data. A method that involves obtaining by a ware computer by fitting a parameter to the contribution of a change in one or more of the process variables in a second group to the metrology data of the pattern.

[0035] According to one embodiment, one or more process variables in the first group include all process variables that are not modeled.

[0036] According to one embodiment, all process variables of one or more process variables in the second group are modelable.

Disclosed herein are estimates of the metrology data of patterns on the substrate, the contribution of changes in all modelable process variables to the metrology data, and all modeling to the metrology data. Obtained by integrating the contributions of changes in process variables that are not, determine whether the estimates of the metrological data meet the criteria, and if the estimates do not meet the criteria, by the hardware computer. A method that includes adjusting the relationship between unmodeled process variables and the contribution of changes in all unmodeled process variables to metrology data.

[0038] According to certain embodiments, adjusting the relationship involves altering the chemical composition of the resist on the substrate.

[0039] According to certain embodiments, adjusting the relationship involves changing the chemicals used to develop the resist on the substrate.

[0040] According to one embodiment, adjusting the relationship involves changing the etching apparatus used to etch the substrate.

[0041] Disclosed herein are the values of the first variable of the pattern of the substrate processed by the patterning process, or the pattern for the substrate processed by the patterning process, of the first variable relating to the substrate. To calculate by integrating the fingerprint with a specific value of the first variable, and to determine the value of the second variable in the pattern, at least in part, based on the calculated value of the first variable. And is a method that includes.

[0042] According to one embodiment, the determination of the value of the second variable of the pattern conforms to the rule with the calculated value of the first variable and the reconstruction or simulation result based at least partially on the second variable. Including adjusting the second variable by the hardware computer until.

[0043] According to one embodiment, the rule indicates that the difference between the measurement result of the pattern on the substrate and the reconstruction / simulation result exceeds or meets the threshold.

[0044] According to one embodiment, the reconstruction / simulation result is the calculated radiation distribution, and the measurement result is the measured radiation distribution.

[0045] According to one embodiment, changes in the first variable make a greater difference in reconstruction or simulation results than the same changes in the second variable.

[0046] According to one embodiment, the first variable pertains to the metrology target on the board.

[0047] According to one embodiment, the first variable is the critical dimension of the pattern of the metrology target on the board.

[0048] According to one embodiment, the second variable relates to the metrology target on the substrate.

According to one embodiment, the second variables are the side wall angle of the pattern of the metrology target, the height of the pattern of the metrology target, the thickness of the layer of the metrology target, and the thickness of the metrology target being developed. Includes one or more selected from pattern resist loss, metrology target footing, index of refraction of layer of metrology target, absorption of layer of metrology target, and / or absorbance coefficient of layer of metrology target. ..

[0050] According to one embodiment, the method further predicts whether or not there is a defect, following the downstream process of transferring the pattern onto the substrate, based on a second variable of the pattern. include.

[0051] According to one embodiment, the variables of the patterning process for one substrate or another are adjusted in response to the prediction of defects in the pattern following the downstream process of pattern transfer onto the substrate.

[0052] According to certain embodiments, the process involves developing a resist layer on a substrate.

[0053] According to one embodiment, the process involves etching the substrate.

[0054] According to one embodiment, the method involves a first contribution from one or more variables in a first group relating to the lithographic apparatus of the patterning process and prior to or before the transfer of the pattern in the lithographic apparatus. A second contribution from one or more variables in the second group involved in the later one or more fabrication processes and one in the third group related to the patterning device used to transfer the pattern. Alternatively, it further includes obtaining a fingerprint of the first variable by integrating with a third contribution from multiple variables.

[0055] According to one embodiment, the variables in the first group are illumination by the lithographic device, projection system of the lithographic device, moving standard deviation of the movement of the substrate stage of the lithography device, moving average of movement of the substrate stage, focus. Includes one or more variables of dose, bandwidth, exposure period, high frequency laser bandwidth change, high frequency laser wavelength change, and / or substrate flatness.

[0056] According to one embodiment, the variables in the second group include one or more variables of spin coating, post-baking, developing, etching, deposition, doping, and / or packaging.

[0057] According to certain embodiments, the variables in the third group include one or more variables of the mask CD, the shape and / or position of the assist pattern, and / or the adjustment applied by the resolution enhancement technique. ..

[0058] According to one embodiment, the integration of the fingerprint of the first variable with a particular value of the first variable is with the fingerprint of the first variable and the particular value of the first variable. Including adding.

[0059] According to one embodiment, a particular value of the first variable is obtained by averaging the measurements of a group of first variables with respect to the substrate.

[0060] According to one embodiment, the particular value of the first variable is the design value of the first variable.

[0061] According to one embodiment, the first variable used when determining the value of the second variable has a value limited within a specific range centered on the calculated value of the first variable. ..

What is disclosed herein is to obtain a board-wide fingerprint of the variables associated with the pattern created by the patterning process and to select from board positions within the fingerprint by a hardware computer system. It is a method that includes predicting the contours of the features of the pattern based on the values of the variables made.

[0063] According to one embodiment, the prediction involves selecting the nominal contour of a particular shape of the feature using the value of a variable.

[0064] According to one embodiment, the nominal contour is obtained by simulation or arithmetic using a mathematical model.

[0065] According to one embodiment, the nominal contour is the expected contour in the resist.

[0066] According to one embodiment, the prediction involves resizing the nominal contours of the features.

[0067] According to one embodiment, the resizing is based on the value of an additional variable associated with the pattern, which value is selected at the same substrate position from the entire substrate fingerprint of the additional variable.

[0068] According to one embodiment, the fingerprint of the entire substrate of additional variables corresponds to the post-etching situation.

[0069] According to certain embodiments, additional variables include critical dimensions.

[0070] According to one embodiment, the variable contains a focal point.

[0071] According to one embodiment, the feature is a determined hotspot.

[0072] According to certain embodiments, the method further comprises determining the edge placement position or error using predictive contours.

[0073] According to one embodiment, the method further comprises using a check on predictive contours to determine if a feature or another feature is likely to be defective.

[0074] According to certain embodiments, the method further comprises calibrating the mathematical model with predictive contours.

[0075] According to one embodiment, the mathematical model includes a light proximity effect correction model.

[0076] Disclosed herein is a computer program that includes a non-transitory computer-readable medium with recorded instructions, any of the methods herein when the instructions are executed by a computer. It is a computer program that implements the above.

<figref num="1">[0077] Schematic drawing of a lithographic apparatus according to an embodiment.</figref><figref num="2">[0078] An example of the category of process variables is shown.</figref><figref num="3A">It is schematically shown that both changes in modelable process variables and changes in non-modelable process variables can have a contribution to metrological data.</figref><figref num="3B">[0080] An example of modeling an integrated contribution to a CD on a substrate is schematically shown, and this contribution is a variation of multiple modelable process variables.</figref><figref num="4A">[0081] Schematic representation of metrology data with two contributions, one of which is due to changes in one or more modelable process variables and the other of which is modelable if not modeled. This is due to changes in one or more other process variables, which may be, or a mixture of them.</figref><figref num="4B">[0082] Schematic representation of metrology data with two contributions, one of which is due to changes in one or more unmodeled process variables and the other of which is modelable if not modeled. This is due to changes in one or more other process variables, which may be, or a mixture of them.</figref><figref num="5">[0083] The flow of the method according to an embodiment is schematically shown.</figref><figref num="6">[0084] The flow of the method according to an embodiment is schematically shown.</figref><figref num="7A">[0085] Schematic representation of the effect of unmodeled process variables on sub-PW.</figref><figref num="7B">[0085] Schematic representation of the effect of unmodeled process variables on sub-PW.</figref><figref num="8">[0086] The flow of the method according to an embodiment is schematically shown.</figref><figref num="9">[0087] The flow of the method according to an embodiment is schematically shown.</figref><figref num="10">[0088] The flow of the method according to an embodiment is schematically shown.</figref><figref num="11">[0089] Schematic representation of the flow of a method according to an embodiment.</figref><figref num="12">[0090] Schematic depiction of exemplary inspection equipment and metrology technology.</figref><figref num="13">[0091] An exemplary inspection device is schematically drawn.</figref><figref num="14">[0092] The relationship between the illumination spot of the inspection device and the metrology target is shown.</figref><figref num="15">[0093] Based on the measurement data, the process of deriving a plurality of variables of interest is schematically drawn.</figref><figref num="16">[0094] The flow of the method according to an embodiment is schematically shown.</figref><figref num="17">[0095] Schematic representation of the flow of a method according to an embodiment.</figref><figref num="18">[0096] An embodiment of contribution / fingerprint example and contour selection is schematically drawn.</figref><figref num="19">[0097] An embodiment of a contribution / fingerprint example and a contour modification is schematically drawn.</figref><figref num="20A">[0098] An embodiment of the defect analysis process is schematically drawn.</figref><figref num="20B">[0098] An embodiment of the defect analysis process is schematically drawn.</figref><figref num="20C">[0098] An embodiment of the defect analysis process is schematically drawn.</figref><figref num="21A">[0099] An embodiment of the defect analysis process is schematically depicted.</figref><figref num="21B">[0099] An embodiment of the defect analysis process is schematically depicted.</figref><figref num="21C">[0099] An embodiment of the defect analysis process is schematically depicted.</figref><figref num="22">[00100] It is a block diagram of a computer system example.</figref>

[00101] Although specific references may be made in the text to the use of lithographic devices in the manufacture of ICs, the lithographic devices described herein are integrated optical systems, guidance and detection patterns for magnetic domain memories, It is understood that other applications such as the manufacture of liquid crystal displays (LCDs), thin film magnetic heads, etc. may also be available. Those skilled in the art will appreciate the use of the terms "wafer" or "die" herein in the context of such alternative applications, respectively, as synonymous with the more general terms "base" or "target portion", respectively. You will recognize that it can be considered to be. The substrate herein may be processed before or after exposure, for example in a track (generally a tool that applies a layer of resist to the substrate and develops an exposed resist) or a metrology or inspection tool. Where applicable, the disclosures herein may apply to the above and other substrate process tools. Further, the substrate may be processed more than once, for example to produce a multilayer IC, so the term substrate as used herein also refers to a substrate that already contains a plurality of processed layers. There is.

[00102] The terms "radiation" and "beam" as used herein refer to ultraviolet (UV) (eg, having wavelengths of 365, 248, 193, 157, or 126 nm) and extreme ultraviolet (EUV) (EUV). Includes all types of electromagnetic radiation, including (for example, having wavelengths in the range of 5-20 nm), as well as particle beams such as ion beams or electron beams.

[00103] The term "patterning device" as used herein is a device that can be used, for example, to generate a pattern on a target portion of a substrate and to impart a radiating beam with a pattern on its cross section. It shall be widely interpreted as referring to. Note that the pattern applied to the radiated beam may not exactly match the desired pattern on the target portion of the substrate. In general, the pattern applied to the radiated beam corresponds to a particular functional layer within the device generated in a target portion such as an integrated circuit.

[00104] The patterning device may be transmissive or reflective. Examples of patterning devices include masks, programmable mirror arrays, and programmable LCD panels. Masks are well known in lithography and include mask types such as binary, alternating phase shift, and attenuated phase shift, as well as various hybrid mask types. An example of a programmable mirror array employs a matrix arrangement of small mirrors, where each small mirror can be individually tilted to reflect an incident radiating beam in different directions, thus reflecting the beam. Is patterned.

[00105] The support structure holds the patterning device. It holds the patterning device in a manner that depends on the orientation of the patterning device, the design of the lithographic device, and other conditions, such as whether the patterning device is held in a vacuum environment. The support can use mechanical clamping, vacuum, or other clamping techniques such as electrostatic clamping under vacuum conditions. The support structure may, for example, be fixed or movable as needed, and, for example, a frame or frame that can ensure that the patterning device is in the desired position with respect to the projection system. It may be a table. The use of the term "reticle" or "mask" herein can be considered synonymous with the more general term "patterning device".

[00106] The term "projection system" as used herein is appropriately refracted, for example, depending on the exposure radiation used or other factors such as the use of immersion liquid or the use of vacuum. It is broadly interpreted to include various types of projection systems, including optical systems, catadioptric systems, and catadioptric systems. The use of the term "projection lens" herein can be considered synonymous with the more general term "projection system".

[00107] The illumination system may also include various types of optical components, including refraction, reflection, and catadioptric components for guiding, shaping, or controlling the emitted beam, such components being described below. , Collectively, or alone, may also be referred to as a "lens."

[00108] FIG. 1 schematically depicts a lithography apparatus according to an embodiment. This device supports an illumination system (illuminator) IL for adjusting the emission (eg UV emission or DUV emission) beam PB, a patterning device (eg mask) MA, and a patterning device for the item PS. Support structure MT connected to the first positioning device PM for accurate positioning, -a second for holding the substrate (eg, resist-coated wafer) W and accurately positioning the substrate with respect to the item PS. A substrate table (eg, a wafer table) WT connected to the positioning device PW, and-a pattern imparted to the radiated beam PB by the patterning device MA on the target portion C (eg, including one or more dies) of the substrate W. Includes a projection system (eg, a refraction projection lens) PS, configured to form an image on the wafer.

[00109] As depicted here, the device is transmissive (eg, using a transmissive mask). Alternatively, the device may be reflective (eg, using a programmable mirror array of the type described above).

[00110] The illuminator IL receives a radiated beam from the source SO. The source and lithographic device may be separate entities, for example if the source is an excimer laser. In such cases, the source is not considered to form part of the lithography equipment, and the radiated beam is radiated, for example, using a beam delivery system BD that includes a suitable induction mirror and / or beam expander. It is passed from the source SO to the illuminator IL. In other cases, the source may be an integral part of the device, for example if the source is a mercury lamp. The source SO and illuminator IL, along with the beam delivery system BD, may be referred to as the radiation system, if desired.

[00111] The illuminator IL can change the intensity distribution of the beam. The illuminator may be arranged to limit the radial range of the radiated beam so that the intensity distribution is non-zero within the annular region within the pupil plane of the illuminator IL. Additional or alternative, the illuminator IL may be operational to limit the distribution of beams within the pupil plane so that the intensity distribution is non-zero across multiple equidistant sectors within the pupil plane. The intensity distribution of the emitted beam within the pupil plane of the illuminator IL is sometimes referred to as the illumination mode.

[00112] The illuminator IL may include an adjuster AM configured to adjust the intensity distribution of the beam. In general, at least the outer and / or inner radial ranges of the intensity distribution within the pupil plane of the illuminator (usually referred to as σ-outer and σ-inner, respectively) can be adjusted. The illuminator IL may be operable to vary the angular distribution of the beam. For example, the illuminator may be operational to change the number of sectors and the angular range in the pupil plane where the intensity distribution is non-zero. Different illumination modes may be achieved by adjusting the intensity distribution of the beam within the pupil plane of the illuminator. For example, by limiting the radius and angular range of the intensity distribution in the pupil plane of the illuminator IL, the intensity distribution may have a multipolar distribution, such as a dipole, four-pole, or six-pole distribution. The desired illumination mode may be obtained, for example, by inserting an optical component that provides that illumination mode into the illuminator IL, or by using a spatial light modulator.

[00113] The illuminator IL may be operational to change the polarization of the beam and may be operational to adjust the polarization using the adjuster AM. The polarization state of the radiated beam over the pupillary surface of the illuminator IL is sometimes referred to as the polarization mode. The use of different polarization modes can allow greater contrast to be achieved in the images formed on the substrate W. The radiated beam may be unpolarized. Alternatively, the illuminator may be arranged to linearly polarize the emitted beam. The polarization direction of the radiated beam may be different across the pupil plane of the illuminator IL. The polarization direction of the radiation may be different in different regions within the pupil plane of the illuminator IL. The polarization state of the radiation may be selected based on the illumination mode. In multipolar illumination mode, the polarization of each pole of the radiated beam may be approximately perpendicular to the position vector of that pole in the pupil plane of the illuminator IL. For example, in the dipole illumination mode, the radiation may be linearly polarized in a direction approximately perpendicular to the line that bisects the two opposing sectors of the dipole. The radiated beam may be polarized in one of two different orthogonal directions, sometimes referred to as the X-polarized state and the Y-polarized state. In the quadrupole illumination mode, the radiation in each pole sector may be linearly polarized in a direction approximately perpendicular to the line that bisects that sector. This polarization mode is sometimes referred to as XY polarization. Similarly, in the hexapole illumination mode, the radiation in each pole sector may be linearly polarized in a direction approximately perpendicular to the line that bisects that sector. This polarization mode is sometimes referred to as TE polarization.

[00114] In addition, the illuminator IL generally includes various other components such as integrator IN and capacitor CO. The illuminator provides a regulated emission beam PB with the desired uniformity and intensity distribution in cross section.

[00115] The radiated beam PB is incident on the patterning device (eg, mask) MA held on the support structure MT. After traversing the patterning device MA, the beam PB passes through a projection system PS that focuses the beam on the target portion C of the substrate W. A second positioning device PW and a position sensor IF (eg, an interfering device) can be used to accurately move the substrate table WT, eg, to position different target portions C within the path of the beam PB. Similarly, patterning the path of the beam PB using a first positioning device PM and another position sensor (not clearly depicted in FIG. 1), for example after a machine search of the mask library or during a scan. The device MA can be accurately positioned. Generally, the movement of the object tables MT and WT is realized by using a long stroke module (coarse movement positioning) and a short stroke module (fine movement positioning) that form a part of the positioning devices PM and PW. However, in the case of steppers (as opposed to scanners), the support structure MT may be connected only to the short stroke actuator or may be fixed. The patterning device MA and the substrate W may be aligned using the patterning device alignment marks M1 and M2 and the substrate alignment marks P1 and P2.

[00116] The projection system PS has an optical transfer function that can be non-uniform (which can affect the pattern imaged on the substrate W). In the case of unpolarized radiation, such effects are two scalars that describe the transmission (apodization) and relative phase (aberration) of the radiation emitting the projection system PS as a function of its position in the pupil. The map can represent it pretty well. These scalar maps, sometimes referred to as transparency maps and relative topology maps, can be represented as linear combinations of the complete set of basis functions. A particularly useful set is the Zernike polynomial, which forms a set of orthogonal polynomials defined on the unit circle. The determination of each scalar map may include determining the coefficients in such an expansion. Since Zernike polynomials are orthogonal on the unit circle, the Zernike coefficient is determined by sequentially calculating the inner product of each Zernike polynomial and the measured scalar map and dividing this by the square of the norm of the Zernike polynomial. May be good.

[00117] Transparency maps and relative topology maps are field and system dependent. That is, in general, each projection system PS has a different Zernike expansion for each field point (ie, for each spatial position in its image plane). The relative phase of the projection system PL in the pupil plane is, for example, by projecting radiation through the projection system PS from a point radiation source in the objective plane of the projection system PS (ie, the plane of the patterning device MA), and shearing interferometry. It may be determined by measuring the wavefront (ie, the locus of points having the same phase) with a meter. The shearing interferometer is a common path interferometer and therefore advantageously does not require a secondary reference beam to measure the wavefront. The shearing interferometer is arranged so as to detect the interference pattern in the image plane of the projection system (that is, the substrate table WT) in the diffraction grating (for example, the two-dimensional grid) and in the plane conjugate with the pupil plane of the projection system PS. The detector may be included. The interference pattern is related to the derivative of the phase of the radiation relative to the coordinates in the pupil in the shearing direction. The detector may include an array of sensing elements, such as a charge coupling device (CCD).

[00118] The grating may be aligned with the axes (x and y) of the coordinate system of the projection system PS, or may be at an angle such as 45 degrees with respect to these axes, two right angles. It may be scanned sequentially in the direction forming the above. The scan may be performed over an integer grid period, for example one grid period. The scan averages the phase variation in one direction and allows the phase variation in the other direction to be reconstructed. This allows the wave front to be determined as a function in both directions.

[00119] The transmission (appointment) of the projection system PS in the pupil plane is, for example, by projecting radiation through the projection system PS from a point-like radiation source in the objective surface (that is, the surface of the patterning device MA) of the projection system PS. , And may be determined by measuring the intensity of radiation in the plane conjugate to the pupil plane of the projection system PS using a detector. The same detectors used to measure the wavefront to determine the aberrations may be used. The projection system PS may include multiple optical (eg, lens) elements and is configured to adjust one or more of the optical elements to compensate for aberrations (phase variation across the pupil plane of the entire field). The adjustment mechanism PA may be further included. To achieve this, the adjustment mechanism PA may be operational to manipulate one or more optical (eg, lens) elements in the projection system PS in one or more different ways. The projection system may have a coordinate system in which the optical axis extends in the z direction. The conditioning mechanism PA may be operational to make any combination of the following: displacement of one or more optics; tilting of one or more optics; And / or deforming one or more optics. The displacement of the optical element may be in any direction (x, y, z or a combination thereof). The tilt of the optic is generally perpendicular to the optical axis by rotating around the axis in the x or y direction (rotation around the z axis may be used for aspherically symmetric aspheric optics). Off the surface. Deformations of the optics may include both low frequency shapes (eg, astigmatism) and high frequency shapes (eg, free-form aspherical surfaces). Deformation of an optical element is, for example, by exerting a force on one or more sides of the optical element using one or more actuators and / or using one or more heating elements of one of the optical elements. This may be done by heating one or more selected areas. In general, it may not be possible to adjust the projection system PS to correct apodization (transmission variation over the pupil plane). A transmission map of the projection system PS may be used when designing a patterning device (eg, mask) MA for the lithography apparatus LA. Using computer lithography techniques, the patterning device MA may be designed to at least partially correct the apodization.

[00120] Variables in the patterning process are called "process variables". The patterning process may include upstream and downstream processes of the actual transfer of the pattern in the lithographic apparatus. Figure 2 shows an example of the category of process variable 370. The first category may be variable 310 of the lithographic device or other device used in the lithographic process. Examples in this category include variables such as lithographic equipment lighting, projection systems, and substrate stages. The second category may be variable 320 of one or more procedures performed in the patterning process. Examples in this category include focus control or focus measurement, dose control or dose measurement, bandwidth, exposure period, development temperature, chemical composition used for development, and the like. The third category may be variable 330 of the design layout and its implementation in or with the patterning device. Examples in this category may include the shape and / or position of assist features, adjustments applied by resolution enhancement techniques (RET), CDs of mask features, and so on. The fourth category may be the variable 340 of the board. Examples include the properties of the structure beneath the resist layer, the chemical composition and / or physical dimensions of the resist layer, and the like. The fifth category may be the temporal variation characteristic 350 of one or more variables in the patterning process. Examples in this category include characteristics of high frequency stage movement (eg, frequency, amplitude, etc.), high frequency laser bandwidth changes (eg, frequency, amplitude, etc.), and / or high frequency laser wavelength changes. These high frequency changes or movements exceed the response time of the mechanism for adjusting basic variables (eg, stage position, laser intensity). The sixth category may be characteristics 360 of upstream or downstream processes of pattern transfer in lithographic equipment such as spin coating, post-baking (PEB), developing, etching, deposition, doping, and / or packaging.

[00121] The values of some or all of the process variables can be determined by an appropriate method. For example, these values may be determined from data acquired using various metrology tools (eg, board metrology tools). These values are used in various sensors or systems of the device in the patterning process (eg, sensors such as leveling or alignment sensors of the lithography device, control systems of the lithography device (eg, substrate or patterning device table control system), track tools. It may be obtained from a sensor, etc.). These values may come from the operator of the patterning process.

[00122] Process variables may be "modelable" or "not modeled". The fact that a process variable is modelable means that the contribution of changes in the process variable (eg, focus, dose, etc.) to the metrology data can be determined by or by the computational model. .. In a simple example, the contribution of a change in a modelable process variable is the product of that change and the sensitivity of the metrology data to the modelable process variable. Non-limiting examples of metrological data can include critical dimension (CD), critical dimension uniformity (CDU), side wall angles, edge positions, overlays, focal points, pattern shifts, and the like. The metrology data may be the value of the characteristic of each pattern or the statistical value (for example, average) of the characteristic of the pattern group. The fact that process variables are not modeled means that the contribution of changes in process variables (eg, development, etching, etc.) to metrology data cannot or is accurately determined at the appropriate time or by computational models. It means that it is not computationally modeled because it cannot. Process variables may not be modeled due to lack of knowledge of how they affect metrological data. For example, some process variables in the fourth category (eg, the composition of the resist layer) and some process variables in the sixth category (eg, PEB, development, etching, deposition, or doping properties) are It may not yet have a fully understood relationship with the metrological data, so the contribution of those changes to the metrological data cannot be easily determined by computational modeling. Process variables may not be modeled because their values are unknown. The fact that process variables are not modeled does not necessarily mean that computational modeling process variables are not possible. A process variable is a model because the relationship between the process variable and the metrology data is not yet understood.

[00123] Figure 3A shows that changes in the modelable process variable 311 can have a contribution 312 to the metrology data 352 of the pattern on the substrate (eg, the metrology data determines CD, overlay error, focus, dose, etc.). It is shown schematically that changes in the unmodeled process variable 321 can have a contribution 322 to the metrology data 352. That is, the metrology data 352 can have an integrated contribution of changes in both modelable and non-modelable process variables. These contributions 312 and 322 may be simply additive (including weighted addition) or integrated by other functions (eg, convolution). The metrology data 352 may have the contribution of changes in other variables that may or may not be modelable. For example, the modelable process variable 311 may be the focal point used to expose the pattern. The effect of changes in focus on the pattern, and thus the metrological data of the pattern, is usually known and can be predicted by modeling. The unmodeled process variable 321 may be a setting in the etching chamber used to etch the substrate to form a pattern (eg, a heater setpoint). The effect of changes in etching settings on the pattern, and thus the metrological data of the pattern, may not yet be fully understood, and the effect may not be determined by modeling.

[00124] Figure 3B schematically shows an example of modeling the integrated contribution to CD on the substrate, which contribution is the movement standard deviation (MSD) of the movement of the substrate in the focal (F) 400, normal direction of the substrate._z) 410, and the movement standard deviation of the movement of the board in the direction parallel to the board (MSD)_x) It is a change (eg error) of multiple modelable process variables such as 420. Therefore, in this example, an example of the contribution of focus (F) 400 to CD is shown as contribution 430 and the movement standard deviation (MSD)._zAn example of the contribution of) 410 to CD is shown as contribution 440, and the movement standard deviation (MSD)._xAn example of the contribution of) 420 to CD is shown as contribution 440. Each of these contributions is then integrated (460). Element 460 (and other elements in the figure) indicates a plus sign, but the operation at 460 does not have to be addition, for example it may be multiplication, convolution, and so on. In one example, the integrated contribution is CD (x, y) = a₁* CD (F)²(x, y) + b₁* CD (MSD)_x) (X, y) + c₁* CD (MSD)_z) (X, y) + ... In one embodiment, contributions 430, 440, and 450 are focal (F) 400 and moving standard deviation (MSD, respectively)._z) 410, and moving standard deviation (MSD)_x) 420 distributions, in which case a CD model is used to integrate them into the CD distribution. In addition, there may be intersection terms not shown here (eg CD as a function of F × MSD). The nominal or simulated value of the CD may be integrated with the contribution to obtain the absolute value of the CD. a₁, B₁, C₁Coefficients such as are the sensitivities of metrology data CDs to modelable process variables or their functions. The MSD is the moving standard deviation (MSD) of the substrate positioning error during pattern transfer in the lithography equipment and thus represents the radio frequency portion of the positioning error. In this example, the contribution spans one substrate, but in some embodiments, one or more of the contributions may be die / field per (in this case, eg, depending on applicable conditions in each case, the substrate. May be repeated over). The contribution (or its conversion to an absolute value) may be characterized as a fingerprint as it is spatially definable across the substrate / die / field.

[00125] Figure 4A schematically shows that the metrology data 550 has two contributions 512 and 522. Contribution 512 is due to changes in one or more modelable process variables 511. Contribution 522 is due to changes in one or more other process variables that may be unmodeled, modelable, or a mixture thereof. Contribution 522 may be obtained by removing contribution 512 from metrology data 550. By definition, the contribution 512 may be determined by modeling one or more modelable process variables 511. Contribution 512 may be represented as a function of one or more modelable process variables 511. This function may be linear or non-linear.

[00126] Figure 4B schematically shows the aforementioned metrology data 570 with two contributions 542 and 532. Contribution 542 is due to changes in one or more unmodeled process variables 541. Contribution 532 is due to changes in one or more other process variables that may be unmodeled, modelable, or a mixture thereof. Contribution 532 may be obtained by removing contribution 542 from metrology data 570. One or more process variables 541 are not modelable, but contributions 542 may be determined experimentally or empirically.

[00127] The contribution of one or more modelable variables may be specific to the lithographic device, i.e., the contribution of one or more modelable variables is specific to a particular actual lithographic device. In certain embodiments, the contribution of one or more modelable variables is not specific to a particular substrate (and thus can be used across multiple substrates). Therefore, the contributions of one or more modelable variables may be pre-characterized and later retrieved from the database for various integration processes. The contribution of this one or more modelable variables can be applied to a particular substrate by integrating it with the data of one or more variables and sensitivity relationships of that particular substrate. The contribution of one or more modelable variables may be substrate specific. For example, the contribution may be determined for each substrate or a particular substrate group. Examples of such variables are, but are not limited to, illumination, projection system, focus, dose, bandwidth, exposure duration, radio frequency stage movement characteristics (eg, substrate stage movement standard deviation (MSD), substrate stage). It may include moving averages of movement), high frequency laser bandwidth changes (eg frequency, amplitude, etc.), high frequency laser wavelength changes, and / or substrate flatness.

[00128] The contribution of one or more modelable variables may be specific to the pattern or patterning device, i.e. the contribution to a particular pattern provided by a particular actual patterning device or patterning device. It is peculiar. This contribution does not have to be further substrate dependent. Therefore, the contributions specific to the pattern or patterning device may be pre-characterized and later retrieved from the database for various integration processes. Examples of such variables may include, but are not limited to, mask CDs, the shape and / or position of assist features, adjustments applied by resolution enhancement techniques (RET), and the like.

[00129] The contribution of one or more non-modeled variables may be substrate-independent or substrate-dependent. In certain embodiments, the contributions of one or more unmodeled variables may be used repeatedly across each substrate. Therefore, the contributions of one or more unmodeled variables may be pre-characterized and retrieved from the database for various integration processes. The contribution of one or more unmodeled variables may be specific to the device (as a whole or to a particular part of the device (eg, etching chamber)). Examples of such variables may include, but are not limited to, various properties of upstream or downstream processes of pattern transfer in lithographic equipment such as spin coating, PEB, development, etching, deposition, doping, and / or packaging. ..

[00130] The contribution of changes in different process variables may have different spatial uniformity across the substrate. For example, the contribution of change in one or more non-modelable process variables can be essentially uniform across the substrate, while the contribution of change in one or more modelable process variables is less uniform across the substrate. May not be. This difference can be due to the different nature of the process variables. For example, the contribution of changes in one or more process variables related to the resist layer, the development of the resist layer, and / or the etching of the substrate is that the entire substrate is usually coated with the resist layer simultaneously and under the same conditions. As it is developed, developed, or etched, it tends to be essentially uniform, or during many of these processes, due to the rotation of the substrate, it tends to be rotationally symmetric. For example, the contribution of changes in one or more process variables associated with an exposure or lithographic device tends to be less uniform because the exposure tends to be done on a die-by-die basis, and one or more processes. The variables can vary between the exposure of one die and the exposure of another die. Therefore, if the essentially non-uniform contributions across the substrate can be removed from the metrology data of one pattern, then the essentially uniform contributions across the substrate can be removed from the metrology data of this pattern and other positions on the substrate. Can be obtained from.

[00131] Therefore, in one particular example, data such as leveling information, moving average (MA) of substrate movement, MSDxyz, dose, laser bandwidth, pupil shape, etc. are collected from the lithography equipment for each point exposed on the wafer. can do. This data is generally already available in lithographic equipment, such as diagnostic files. From this data, the above contributions, which can also be expressed as virtual boards or board fingerprints in which the contributions are spatially distributed across the board, are the CD model and CDU sensitivity of the feature of interest (CD is , Which is a metrological variable whose evaluation is desired). Sensitivity can be obtained from simulations or experiments. The contributions thus generated should be seen during the post-resist development measurements, assuming the track and resist model calibration is perfect. When the substrate is measured, for example after development or etching, contributions (eg, virtual substrate or virtual fingerprint) are removed from equivalent measurements (eg, CDU measurements). The remaining contributions (eg, fingerprints) are, at this point, contributions from pre- or post-pattern transfer processes (eg, by tracks and / or etching equipment, which are typically between fields) and patterning devices (in-field). Lithography equipment errors will have been corrected from the measured data. Similarly, the contribution of the patterning device may be measured and the contribution removed, leaving the contribution from the pre- or post-pattern transfer process.

[00132] Therefore, contributions are removed per process flow (eg, a combination of patterning parameters and / or devices used) or per particular device or part of it (eg, etching device or etching chamber). be able to. For in-field fingerprints, it should be possible to obtain a much better correlation between patterning device-to-board measurements when eliminated from the systematics represented by lithographic equipment disturbances and / or their contributions. ..

[00133] In addition, removal of contributions from, for example, lithographic equipment can result in a significant reduction in inter-feature delta fingerprints. This can be useful if the fingerprint measured for one feature is used to predict defects in another feature (eg, this is a pattern feature where the pattern feature itself is not measured or does not have to be measured. Useful when measurements are made on target features (eg, grids) rather than functional device features so that measurements can be used to predict defects in.

Thus, for example, removal of the contribution of a lithographic device reduces the noise of the measurement result (because the variability of the lithographic device is corrected), makes the measurement of the fingerprint of the non-lithographic device more accurate, and / or causes. Measurements can be made more suitable for troubleshooting lithographic device fingerprints for which is not well understood.

[00135] FIG. 5 schematically shows the flow of the method according to an embodiment. The contribution 612 of changes in one or more process variables in the first group is removed from the first pattern of metrology data 650 on the substrate, resulting in one of the second groups for the metrology data 650. Alternatively, a contribution 622 of changes in a plurality of process variables is obtained, for example, the contribution 622 has a higher spatial uniformity than the contribution 612. The contribution of changes in one or more process variables of the second group to the second pattern of metrology data on the same or different boards 642 does not require the actual acquisition of the metrology data from the second pattern. Can be obtained based on contribution 622. For example, contribution 642 may be estimated to be the same as contribution 622, as changes in one or more process variables in the second group produce a contribution with high spatial uniformity to the metrology data. .. Optional procedure 660 adjusts one or more process variables in one or more process variables in the first group based on contribution 642 without making measurements on the second pattern. This can reduce the probability of defects in the second pattern. In one example, the first pattern may be the pattern for testing purposes (ie, the target), and making measurements on the first pattern does not affect the functional pattern on the board, the second pattern is 1 There are one or more functional patterns, and making measurements on the second pattern can therefore adversely affect it and should be avoided. This flow in Figure 5 provides a way to get the effect of at least some process variable (ie, here the second group) on the second pattern without making measurements on the second pattern. In one example, one or more process variables in the first group may include focus, dose, substrate positioning, and / or other variables of the lithographic device during substrate exposure. Since the first pattern and the second pattern may not be exposed at the same time, these variations The numbers (eg, their values) can vary depending on the first pattern and the second pattern. One or more process variables in the second group may include PEB temperature and / or duration, developer concentration and / or duration, etching rate, etc., which are substantially uniform throughout the substrate. Since one or more process variables in the second group apply to the entire substrate (eg, the entire substrate is subject to the same baking and development conditions), the impact on the first and second patterns is , Tends to be similar.

[00136] FIG. 6 schematically shows the flow of the method according to an embodiment. The contribution 612 of changes in one or more process variables in the first group is removed from the first pattern of metrology data 650 on the substrate, resulting in one of the second groups for the metrology data 650. Alternatively, the contribution 622 of the change of a plurality of process variables is acquired, and the contribution 622 has a higher spatial uniformity than, for example, the contribution 612. The contribution 642 of the change in one or more process variables of the second group to the second pattern of metrology data 655 on the board can be obtained based on the contribution 622. For example, contribution 642 may be estimated to be the same as contribution 622, as changes in one or more second groups of process variables produce a contribution with high spatial uniformity to the metrology data. .. The contribution of change in one or more of the second groups of process variables 642 was then actually measured after the process involved in the metrology data 655 (eg, the process variables of the first and second groups). One of the process variables for metrology data 655) that is removed from the data in the second pattern) and as a result without actually getting the value of one or more of the first groups of process variables in the second pattern. Alternatively, a plurality of first group change contributions 632 can be obtained. Procedure 670 of a defect in the second pattern by adjusting one or more of the variables in one or more of the first group of process variables based on contributions 632, 642, or their integration. You may reduce the probability. This flow in Figure 6 shows the metrological data for the second pattern.Another use of the estimation of contribution 642 if available. In one example, one or more first groups of process variables may include focus, dose, substrate positioning, and / or other variables of the lithographic device during substrate exposure, and one or more of the process variables. The second group may include, for example, a PEB temperature and / or period, developer concentration and / or period, etching rate, etc. that are substantially uniform throughout the substrate.

[00137] The various patterns on or provided by the patterning device may have different process windows, i.e., spaces of process variables in which the patterns are generated within the specifications. Examples of pattern specifications related to potential systematic defects include necking, line pullback, line thinning, CD, edge placement, overlapping, resist top loss, resist undercut, and / or bridging. The process windows for all patterns on the patterning device or one area thereof may be obtained by merging (eg, overlapping) the process windows for each individual pattern. The process window boundaries of all patterns include the process window boundaries of some of the individual patterns. That is, these individual patterns limit the process window for all patterns. These patterns are sometimes referred to herein as "hotspots" or "process window-only patterns (PWLP)" as used as alternatives. It is possible and economical to focus on hotspots when controlling the patterning process. If the hotspot is not defective, it is most likely that all patterns are free.

[00138] From a mathematical point of view, a process window is a region within a vector space that covers all process variables. In a patterning process, the process window of the pattern is defined only by the specifications of the pattern and the physical characteristics involved in the patterning process. That is, if the specifications and physical properties do not change during the patterning process, the process window does not change.

[00139] However, some of the process variables may not be modeled, so the boundaries of the values of these non-modeled process variables as defined by the specification of one or more patterns may not be obtained by modeling. Therefore, it may not be convenient to use the area in the vector space that all process variables cover as the process window. Instead of the area covered by all process variables ("full PW"), a subspace area (ie, space covered by less process variables than all process variables) ("sub PW") may be used. For example, in a patterning process with many process variables, the area of the subspace covered by the focus and dose may be used as the sub-PW. All process variables that cover a range of subspaces may be modelable. If these process variables that fall outside the sub-PW range contain one or more unmodeled process variables, then the values of the process variables that fall within the sub-PW range are all possible for one or more unmodeled process variables. The sub-PW can be small because the value must be a value that creates a pattern within the specifications. In one example, process variables that fall outside the sub-PW range are the properties of the structure under the resist layer, the chemical composition and / or physical dimensions of the resist layer, and / or spin coating, PEB, development, etching, deposition, doping. , And / or may include one or more non-modeled process variables, such as the characteristics of one or more processes upstream or downstream of pattern transfer in a lithography system such as packaging. Since such variables are not modeled, their impact on metrological data is difficult to determine.

[00140] Figures 7A and 7B schematically show the effect of one or more unmodeled process variables on the sub-PW. For convenience, in Figure 7A, the PW is depicted as a region of space in three dimensions (eg, focus (f), dose (d), and another process variable X that is not modeled), although it may actually have other dimensions. Is done. The sub-PW covered by two of the process variables (focus (f) and dose (d)) is shown as a cross-section of the PW (eg, cross-sections 701-705). In FIG. 7B, where the possible value of X is between cross sections 701 and 705, the sub-PW spanned by f and d is a shaded portion that is the overlap of all cross sections between cross sections 701 and 705. (Because the values of f and d in the shaded area ensure that the pattern is within the specifications for any value of X between cross sections 701 and 705). show. Examples of unmodeled process variables X are the properties of the structure under the resist layer, the chemical composition and / or physical dimensions of the resist layer, and / or spin coating, PEB, development, etching, deposition, doping, and / or packaging. It may include the characteristics of one or more processes upstream or downstream of pattern transfer in a lithography system such as etching.

[00141] FIG. 8 schematically shows the flow of the method according to an embodiment. Contribution 812 of change in one or more first groups of process variables is removed from the first pattern of metrology data 850, resulting in one or more second process variables for metrology data 850. Get the contribution of change in the group of 822. The first group contains all modelable process variables and the second group contains all non-modeled process variables. The contribution 842 of a change in one or more of the second groups of process variables to the second pattern of metrology data on the board can be obtained based on the contribution 822. The second pattern of metrology data does not have to be available to obtain contributions 842. For example, Contribution 842 may be presumed to be the same as Contribution 822 if changes in one or more second groups of process variables result in a highly spatially uniform contribution to the metrology data. .. In procedure 870, the sub-PW covered by the first group of process variables for the second pattern is obtained based on contribution 842. In optional procedure 880, one or more values of the process variables of the first group of process variables are in the second pattern based on the sub-PW (and optionally with contribution 812). It may be adjusted to reduce the probability of defects. Sub-PWs may be used to determine if there are defects in the second pattern based on the values of the process variables in the first group. Examples of process variables in the second group are the properties of the structure under the resist layer, the chemical composition and / or physical dimensions of the resist layer, and / or spin coating, PEB, development, etching, deposition, doping, and /. Alternatively, it may include the characteristics of one or more processes upstream or downstream of pattern transfer in a lithography system such as packaging. Examples of process variables in the first group are focus, dose, bandwidth, dew, such as lithographic equipment lighting, projection system, substrate stage, etc.

[00142] FIG. 9 schematically shows the flow of the method according to an embodiment. For example, the contribution 912 of changes in one or more modelable process variables 911 in the first group to hotspot metrology data is determined, for example, by modeling. The contribution 922 of the change in one or more unmodeled process variables 921 in the second group is obtained. Contribution 922 may be obtained experimentally and from a database. Contribution 922 may be obtained, for example, using the flow for obtaining contribution 642 in FIG. 6 (ie, contribution 922 is one of a second group for metrological data with a pattern different from the hotspot or It may be obtained based on the contribution of changes in multiple process variables). Contribution 912 may be determined on a substrate-by-board basis, while contribution 922 may be used repeatedly across multiple substrates. By integrating Contribution 912 and Contribution 922, the hotspot metrology data estimate 950 is obtained without actually measuring the hotspot. Procedure 970 determines whether a hotspot is defective or not based on estimates from metrology data. Examples of process variables in the second group are the properties of the structure under the resist layer, the chemical composition and / or physical dimensions of the resist layer, and / or spin coating, PEB, development, etching, deposition, doping, and /. Alternatively, it may include the characteristics of one or more processes upstream or downstream of pattern transfer in a lithography system such as packaging. Examples of process variables in the first group are applied by focus, dose, bandwidth, exposure duration, assist feature shape and / or position, resolution enhancement technology (RET), such as lithography equipment illumination, projection system, substrate stage, etc. It may include variables such as adjustments made, characteristics of high frequency stage movement (eg frequency, amplitude, etc.), high frequency laser bandwidth changes (eg frequency, amplitude, etc.), and / or high frequency laser wavelength changes.

[00143] FIG. 10 schematically shows the flow of the method according to an embodiment. The contribution of changes in one or more process variables in the first group, 1012, is removed from the pattern's metrology data 1050, resulting in one or more of the second group's to the pattern's metrology data. Get the contribution 1022 of the change of the process variable. One or more process variables in the first group include at least all unmodeled process variables, but may or may not contain modelable process variables. All process variables in one or more process variables in the second group are modelable. In procedure 1070, one or more parameters of the model for determining the contribution of changes in one or more process variables in a second group to metrology data are fitted to contribution 1022, and as a result, Gets the value 1080 for one or more parameters of the model. Examples of unmodeled process variables are the properties of the structure under the resist layer, the chemical composition and / or physical dimensions of the resist layer, and / or spin coating, PEB, development, etching, deposition, doping, and / or packaging. It may include the characteristics of one or more processes upstream or downstream of pattern transfer in such lithographic equipment. Examples of modelable process variables are applied by lithographic equipment illumination, projection system, substrate stage, etc., focus, dose, amplitude, exposure duration, assist feature shape and / or position, resolution enhancement technology (RET). It may include variables for adjustment, high frequency stage movement characteristics (eg frequency, amplitude, etc.), high frequency laser bandwidth changes (eg frequency, amplitude, etc.), and / or high frequency laser wavelength changes.

[00144] Therefore, as an example, it is desirable to have a model for adapting to changes in the process and / or substrate stack and the sensitivity represented by that model. Thus, in certain embodiments, a predictive CDU (or overlay) substrate fingerprint of each substrate to be measured can be generated from the lithographic apparatus. To this end, a pre-established CD model (eg during the original model calibration), eg CD = a1 * D + a2 * F as described above.²You can use + a3 * MSDx + a4 * MSDy + a5 * MSDz + ... When measuring the substrate, pre- and / or post-process (eg, track / etching) fingerprints and patterning device fingerprints can be removed from the measured CD. The remaining CDU fingerprint measurements can be compared to the predicted CD values. In this situation, at this point there is a set of equations (the same number of equations as the measurement points) for which CD, dose, focus, MSD-xyz, aberrations, etc. are known. This set of equations can be solved for the desired sensitivities (a1, a2, ...). If this is done for a large number of substrates measured for a particular product, there is a lot of data to re-estimate the desired CD sensitivity. If data from multiple lithography equipment is used and / or data from the exposure of the focus exposure matrix is used in the same way, there will be sufficient variation in the input signal (eg, focus, dose, MSD, CD, etc.). , Can enable accurate estimation of sensitivity. In addition, this technique can be applied to other metrological data such as depth of focus and exposure tolerance.

[00145] In certain embodiments, training algorithms may be used to add further accuracy. For example, when desensitizing aberrations, the boundary condition is taken into account that the aberrations fluctuate only on the slits and that the CD (or overlay) data can be prefiltered before fitting these sensitivities. May be good.

[00146] In certain embodiments, sensitivities can be robust to variations in the patterning process by reassessing them from time to time or continuously.

[00147] FIG. 11 schematically shows the flow of the method according to an embodiment. The contribution of changeable process variables to a pattern of metrology data 1112 and the contribution of non-modelable process variable changes to metrology data 1122 are integrated, resulting in an estimate of metrology data of 1150. .. Procedure 1160 determines whether the metrology data estimate 1150 meets the criteria. If the estimate 1150 does not meet the criteria, procedure 1170 adjusts the relationship between the unmodeled process variable and the contribution 1122. For example, if the unmodeled process variables include the characteristics of the resist coating process, the adjustment of the relationship may include changes in the chemical composition of the resist. For example, if the unmodeled process variables include resist development characteristics, the adjustment of the relationship may include changes in the chemicals used in the development. For example, if the unmodeled process variables include the etching characteristics of the substrate, the adjustment of the relationship may include changes in the etching equipment. Using this flow, it is possible to select from several etching devices based on the characteristics of the etching device and the characteristics of the substrate. For example, this relationship can be established for each etching device.

[00148] In certain embodiments, after etching the CD (or other variable), given the limited possibility of ensuring a proper match of performance between etching chambers and the presence of performance drift over time. Performance can vary from tool to tool and from chamber to chamber. This can result in yield loss. Therefore, the etched CD can be measured to obtain a process fingerprint. This process fingerprint can then be used to predict what the post-etch performance will be based on the information available before etching. If the CD is measured both after development and after etching, a pure etching fingerprint (LEB) can be derived. If LEB fingerprints are determined for each etcher and / or etching chamber, they can be used to predict post-etch CD performance and post-etch defect probabilities for each etcher / etching chamber. Which etching tool / etching chamber to use to process the substrate (eg, a large number of substrates) can then be pre-selected based on the minimum defect probability.

[00149] FIG. 12 depicts an example inspection device (eg, a scatometer). It includes a wideband (white light) emission projector 2 that projects radiation onto the substrate W. The re-guided radiation is passed to a spectrometer detector 4 that measures spectrum 10 (intensity as a function of wavelength) of specular radiation, for example as shown in the lower left graph. From this data, the structures or profiles that give rise to the detected spectra can be compared by the processor PU, for example by tightly coupled wave analysis and non-linear regression, or by comparison with a library of simulation spectra as shown in the lower right of FIG. , May be reconstructed. In general, for reconstruction, the general form of the structure is known, and some variables are inferred from the knowledge of the process in which the structure was created and several variables of the structure are determined from the measured data. Only left. Such an inspection device may be configured as a normal incident inspection device or an oblique incident inspection device.

[00150] Another inspection device that can be used is shown in FIG. In this device, the radiation emitted by the source 2 is collimated using the lens system 12, transmitted through the interference filter 13 and the polarizer 17, reflected by the partially reflecting surface 16, and preferably at least 0.9 or at least 0.95. The spot S on the substrate W is focused through the objective lens 15 having a high numerical aperture (NA). Immersion inspection devices (using fluids with a relatively high index of refraction, such as water) may have a numerical aperture greater than one.

[00151] In the case of a lithography apparatus LA or the like, one or more substrate tables may be provided to hold the substrate W during the measurement operation. The substrate table may be similar in form or the same as the substrate table WT in FIG. In some examples where inspection equipment is integrated with lithographic equipment, they may be the same board table. Coarse and fine movement positioners may be provided for a second positioner PW configured to accurately position the substrate in relation to the measurement optics. For example, various sensors and actuators may be provided to obtain the position of the target of interest and bring it to a position below the objective lens 15. In general, many measurements are made with respect to the target at different locations across the substrate W. The substrate support can be moved in the X and Y directions to obtain different targets and in the Z direction to obtain the desired position of the target with respect to the focal point of the optics. For example, if the optics actually remain substantially stationary (generally in the X and Y directions, but in some cases even in the Z direction) and only the substrate moves, the objective lens will move to the substrate. On the other hand, it is convenient to think as if it were moved to a different place and to express the movement. Assuming that the relative positions of the substrate and the optics are correct, in principle either of them are moving, or both are moving, or part of the optics is the rest of the optics. Is a combination of stationary movement (eg, in the Z and / or tilt direction) and movement of the substrate (eg, in the X and Y directions, but optionally in the Z and / or tilt direction)? does not matter.

[00152] The radiation re-guided by the substrate W then passes through the partially reflective surface 16 to the detector 18 to have the detected spectrum. The detector 18 may be located on the rear projection focal length 11 (ie, at the focal length of the lens system 15), or the surface 11 may be reassembled onto the detector 18 using auxiliary optics (not shown). It may be imaged. The detector may be a two-dimensional detector so that the two-dimensional angular scattering spectrum of the substrate target 30 can be measured. The detector 18 may be, for example, an array of CCD or CMOS sensors, and may use, for example, an integration time of 40 ms per frame.

[00153] A reference beam may be used, for example, to measure the intensity of incident radiation. To do this, when a radiating beam is incident on the partially reflecting surface 16, a portion of it passes through the partially reflecting surface 16 as a reference beam toward the reference mirror 14. The reference beam is then projected onto different parts of the same detector 18, or alternative to different detectors (not shown).

[00154] One or more interference filters 13 are available to select wavelengths of interest in the range, for example 405 to 790 nm, or even lower, 200 to 300 nm. Interference filters may be adjustable rather than containing a set of different filters. A grid may be used instead of the interference filter. Aperture diaphragms or spatial light modulators (not shown) may be provided in the illumination path to control the range of angles of incidence of radiation with respect to the target.

[00155] The detector 18 may measure the intensity of reinduced emission at a single wavelength (or a narrow wavelength range), the intensity at multiple wavelengths, or the intensity integrated over a range of wavelengths. In addition, the detector may separately measure the intensity of TM (transverse magnetic) and TE (transverse electric) polarized radiation and / or the phase difference between TM polarized radiation and TE polarized radiation.

[00156] The target 30 on the substrate W may be a 1D grid printed so that the bars are formed from solid resist lines after development. The target 30 may be a 2D grid printed so that after development the grid is formed from solid resist pillars or vias in the resist. Bars, pillars, or vias may be etched into or on the substrate (eg, in one or more layers on the substrate). Patterns (eg, bars, pillars, or vias) are sensitive to process changes in the patterning process (eg, optical aberrations, focal changes, dose changes, etc. in lithography projection equipment (especially projection system PS)) and print. Appears as a variation of the lattice. Therefore, the grid is reconstructed using the measurement data of the print grid. One or more parameters of the 1D grid such as line width and / or shape, or one or more parameters of the 2D grid such as width, length, or shape of pillars or vias, print steps and / or other From the knowledge of the inspection process, it may be input to the rebuild process performed by the processor PU.

[00157] In addition to measuring parameters by reconstruction, angle-resolved scatterometry is useful for measuring feature asymmetry in products and / or resist patterns. One particular application of asymmetry measurement relates to the measurement of overlays containing a set of periodic features with targets 30 superimposed on each other. The concept of asymmetry measurement using the instrument of FIG. 12 or 13 is described, for example, in US Patent Application Publication No. 2006-066855, which is incorporated herein by reference in its entirety. Simply put, the position of the diffraction order of the target's diffraction spectrum is determined solely by the periodicity of the target, but the asymmetry of the diffraction spectrum indicates the asymmetry of the individual features that make up the target. In the device of FIG. 13, where the detector 18 may be an image sensor, such asymmetry in diffraction order appears directly as an asymmetry in the pupil image recorded by the detector 18. This asymmetry may be measured by digital image processing on the unit PU and calibrated to known values for the overlay.

[00158] FIG. 14 shows a plan view of a typical target 30 and a range of illumination spots S for the device of FIG. In order to obtain a diffraction spectrum that is not interfered with by the surrounding structure, the target 30 is, in certain embodiments, a periodic structure (eg, a grid) that is larger than the width (eg, diameter) of the illumination spot S. The width of the spot S may be smaller than the width and length of the target. That is, the target is "insufficiently filled" by illumination, and the diffracted signal is essentially outside the target itself and does not include signals from product features or the like. Illumination arrangements 2, 12, 13, and 17 may be configured to provide uniform intensity illumination over the posterior focal plane of objective system 15. Alternatively, lighting may be restricted in the on-axis or off-axis direction, for example by including an aperture in the lighting path.

[00159] FIG. 15 schematically illustrates the process of determining the value of one or more variables of interest in the target pattern 30'based on measurement data obtained using metrology. The radiation detected by the detector 18 provides the measured radiation distribution 108 of the target 30'.

[00160] For a target 30', the radiation distribution 208 can be calculated / simulated from the parameterized model 206, for example using the numerical Maxwell solution 210. Parameterized model 206 provides examples of layers of various materials that make up and relate to the target. The parameterized model 206 may include one or more variables relating to the features and layers of the target portion under consideration that may vary and may be derived. As shown in FIG. 15, one or more of the variables are the thickness t of one or more layers, the width w of one or more features (eg CD), the height of one or more features. h and / or may include side wall angles α of one or more features. Although not shown, one or more of the variables are, but not limited to, the index of refraction of one or more layers (eg, real or complex index of refraction, index of refraction tensor, etc.), the extinction coefficient of one or more layers, It may further include absorption of one or more layers, resist loss during development, footing of one or more features, and / or line edge roughness of one or more features. The initial value of the variable may be what is expected of the target being measured. The measured radiation distribution 108 is then compared at 212 with the calculated radiation distribution 208 to determine the difference between the two. If there is a difference, there is a possibility that one or more of the variables in the parameterized model 206 will fluctuate, and the new calculated radiation will exist until there is sufficient agreement between the measured radiation distribution 108 and the calculated radiation distribution 208. The distribution 208 may be calculated and compared to the measured radiation distribution 108. At that point, the values of the variables in the parameterized model 206 provide a good or optimal match for the geometry of the actual target 30'. In some embodiments, there is sufficient agreement when the difference between the measured radiation distribution 108 and the calculated radiation distribution 208 is within the permissible threshold.

[00161] One or more variables in the parameterized model may have a stronger effect on the calculated radiation distribution than one or more other variables. For example, a small rate of change of one or more variables can make a big difference in the calculated radiation distribution. Such variables are sometimes called strong variables. Strong variables can include, but are not limited to, critical dimensions of features. On the other hand, a large rate of change of one or more other variables can make a small difference in the calculated radiation distribution. Such variables are sometimes referred to as weak variables. Weak variables are, but are not limited to, feature side wall angles, feature height, layer thickness, resist loss during development, feature footing, layer refractive index, layer absorption, and / or layer absorption coefficient. May include.

[00162] Both strong and weak variables in the parameterized model may be used by the user (eg, person, control system, etc.) to evaluate one or more steps in the patterning process. Specifically, one that can be used in one or more processes upstream or downstream of pattern transfer in lithographic equipment such as spin coating, PEB, development, etching, deposition, doping, and / or packaging. Alternatively, multiple weak variables can be useful in determining whether or not defects are present on the target 30'or other patterns. For example, if the side wall angles of the pattern on the substrate exceed a certain threshold, etching can increase the likelihood of one or more defects. Therefore, it is possible to predict possible defects due to etching prior to the etching process, based on the value of the side wall angle of the pattern on the substrate. Such examples are non-limiting.

[00163] However, it is difficult to determine the values of weak variables based on direct measurements. This is because the signal that can be used to determine the value of a weak variable in an inspection system is usually very weak, sometimes too weak to be detected. Moreover, it is difficult to provide the accuracy to determine the value of a weak variable at a certain level, especially when there are many variables in the parameterized model and the value of the strong variable is unknown.

[00164] In order to obtain more accurate values of one or more weak variables for various uses (eg, defect prediction) by the rebuilding process as shown in Figure 15, the rebuilding process itself. Outside, it is desirable to determine the value of one or more strong variables (eg CD) in the parameterized model.

[00165] FIG. 16 shows the flow of a method of determining the value of a strong variable with respect to a substrate according to an embodiment, which can avoid making measurements of a strong variable with respect to the substrate. This process can be applied to get the values of several different strong variables. As shown, the strong variable fingerprint 1640 for the substrate is one or more of the first group of variables related to the lithographic apparatus (focus, dose, MSD, aberrations, pupil shape, etc., and so on). Is spatially identified per die / field and can be repeated across substrates under applicable conditions, or spatially identified across substrates) with a first contribution of 1610 and in a lithographic apparatus. One or more variables in the second group involved in one or more fabrication processes prior to or after pattern transfer (etching, developing, etc., and they) shall be spatially identified across the substrate. The second contribution 1620 from (possible) and one or more of the third group of variables related to the patterning device used in the patterning process (mask CDs, etc., and their) are die / Obtained by integrating with a third contribution 1630 from (which can be spatially identified per field and repeated across substrates under applicable conditions, or spatially identified across substrates). May be good. The strong variable fingerprint 1640 can be obtained for any particular part of the board, such as the part of the target 30'.

[00166] One or more variables in the first group related to the lithographic apparatus may be one or more modelable variables as described above. The first contribution 1610 may be specific to the lithographic device, i.e., the first contribution 1610 is specific to a particular actual lithographic device. In certain embodiments, the first contribution 1610 is not specific to a particular substrate (and thus can be used across multiple substrates). Therefore, the first contribution 1610 may be pre-characterized and later retrieved from the database for the rebuilding process. This first contribution 1610 can be applied to a particular substrate by integrating it with data on one or more variables and sensitivity relationships of that particular substrate. The first contribution 1610 may be substrate specific. For example, the first contribution 1610 may be determined for each substrate or specific substrate group such that the strong variable fingerprint 1640 is specific to the substrate or substrate group. Examples of variables in the first group are, but are not limited to, illumination, projection system, focus, dose, bandwidth, exposure duration, radio frequency stage movement characteristics (eg, substrate stage movement standard deviation (MSD), substrate). It can include moving averages of stage movements), high frequency laser bandwidth changes (eg frequency, amplitude, etc.), high frequency laser wavelength changes, and / or substrate flatness.

[00167] One or more variables in the third group related to the patterning device may be one or more modelable variables as described above. The third contribution 1630 may be specific to a pattern or patterning device, i.e., the third contribution 1630 is specific to a particular pattern provided by a particular actual patterning device or patterning device. The third contribution 1630 does not have to be substrate dependent. Therefore, the third contribution 1630 may be pre-characterized and later retrieved from the database for the rebuilding process. Examples of variables in the third group may include, but are not limited to, mask CDs, assist feature shapes and / or positions, adjustments applied by resolution enhancement techniques (RET), and the like.

[00168] One or more variables in the second group involved in one or more fabrication processes prior to or after pattern transfer in the lithographic apparatus may be unmodeled variables as described above. Like the first contribution 1610, the second contribution 1620 may be substrate-independent or substrate-dependent. In certain embodiments, the second contribution 1620 may be used repeatedly across each substrate. Therefore, the second contribution 1620 may be pre-characterized and retrieved from the database during the rebuilding process. Examples of one or more variables in the second group are, but are not limited to, upstream or downstream processes of pattern transfer in lithographic instruments such as spin coating, PEB, development, etching, deposition, doping, and / or packaging. Can include various properties of.

[00169] The first contribution 1610, the second contribution 1620, and / or the third contribution 1630 may be experimentally characterized using, for example, any of the flows of FIGS. 4-6, or previously. It may be taken from the manufacturing data of. In certain embodiments, the third contribution 1630 (ie, the patterning device fingerprint) may be obtained by measuring the patterning device directly with a patterning device metrology tool.

[00170] After the strong variable fingerprint 1640 is determined, the value of the strong variable 1660 may be obtained by integrating the strong variable fingerprint 1640 with the strong variable specific value 1650. In certain embodiments, the particular value of the strong variable may be a nominal design value. In some embodiments, the strong variable may be the average of measurements of a set of strong variables with respect to the substrate. Measurements of a set of strong variables may be made for the same feature on two or more dies on the substrate.

Given the values of one or more strong variables in the parameterized model (eg, fixing the values of one or more strong variables, or each value of one or more strong variables Within a given range (eg, by limiting to within 10%, within 5%, within 3%, within 1%), until the calculated radiation distribution is substantially similar to the measured radiation distribution, or with the calculated radiation distribution. One or more weak variables may be adjusted until the difference from the measured radiation distribution is within a predetermined tolerance threshold. If the calculated radiation distribution is substantially similar to the measured radiation distribution, or if the above differences are within a predetermined tolerance threshold, then the values of one or more weak variables may be output.

[00172] As described above, using the values of one or more weak variables, one or more downstream processes after exposure (eg, PEB, resist layer development, etching, deposition, doping, and / or packaging). It is possible to predict whether or not there is a defect due to etching. If defects are predicted, one or more variables in the first group, one or more variables in the second group, and / or variables in one or more variables in the third group One or more values may be adjusted to get a new fingerprint of strong variables. This is done so that a new set of values for one or more weak variables can be reconstructed using the above process. Such adjustments can be made iteratively until defects are unpredictable, based on a new set of values for one or more weak variables.

[00173] In further applications of the contributions / fingerprints described above, one or more contributions / fingerprints can be used to predict the contour of the pattern. In this context, contours are the contours of the shape of the features of the pattern that are transferred to the substrate. The contours can be visualized, for example, by processing an image of the substrate (eg, a scanning electron microscope image) to extract the outer boundaries of the shape of the pattern features transferred to the substrate. However, since contours are expected to be transferred to the substrate, they can also be generated by a mathematical process (eg, simulation) that creates an electronic representation of the pattern features. Generally, contours are linear, but contours used herein can be generalized by data representing feature boundaries. The contours need not be contiguous, that is, the contours and / or data may be discontinuous around the features if the discontinuous contours and / or data sufficiently represent the boundaries of the features. In some embodiments, the contour may be two-dimensional (ie, defining a plane) or three-dimensional. In certain embodiments, the contour may extend to a plane substantially parallel to the plane of the substrate on which the pattern is formed. In certain embodiments, the contour may extend to a plane that is substantially perpendicular to the plane on which the pattern is formed, in which case it can be characterized as a profile and is two-dimensional. Alternatively, it may be in a three-dimensional form.

[00174] To predict contours, one or more contributions / fingerprints are obtained as described herein, and a particular nominal contour is selected to reach the predicted contours. , And / or can be used to modify the nominal contour. With reference to FIG. 17, an embodiment of a flowchart of a contour prediction method is drawn. The 1700 determines the nominal contours of the features of the pattern. In certain embodiments, the nominal contour is the expected contour in the resist. In certain embodiments, the nominal contour is the expected contour in the developed resist.

[00175] In certain embodiments, nominal contours can be obtained by mathematical operations using models and / or simulations. In one embodiment, the simulator or model (eg, a simulator such as ASML's Tachyon product) determines the expected contours of a pattern's features with applicable design values for one or more process variables in the feature's patterning process. can do. This contour obtained by calculation can be specified as a nominal contour. Similarly, a simulator or model may have one or more process variables (eg, different focus settings) to get multiple contours of a feature (each contour is at a different value in one or more process variables). Alternatively, it is possible to determine the contours at various different values of one or more optical settings or conditions, such as conditions, different dose settings or conditions, different projection aberration settings or conditions, different illumination pupil shape settings or conditions). .. These contours obtained by the operation can be designated as nominal contours in which each nominal contour is associated with a different value of one or more process variables. In certain embodiments, nominal contours are set for different focus settings or conditions, so each nominal contour can be associated with different focus settings or conditions, such as constant variance from optimal focus. .. The following description focuses on focus settings or conditions, but nominal contours can be determined for settings or conditions that differ from the focus, and for various combinations of settings or conditions.

[00176] In certain embodiments, the impact of one or more process variables for which nominal contours are determined on one or more other process variables is assessed. In certain embodiments, the one or more other process variables evaluated are one or more process variables that have an effect on the shape and / or size of the contour. Thus, in certain embodiments where the nominal contour is determined with respect to focus setting, the effect of focus on one or more other process variables (such as CD) is determined. In certain embodiments, this effect can be determined using a Focus Exposure Matrix (FEM) type process in which a pattern is evaluated at least with different focus settings or conditions. The CDs at these different focus settings or conditions can then be determined to assess how much CD change is due to focus. In certain embodiments, the effects can be determined experimentally using one or more processing boards that are then measured by a metrology tool. In certain embodiments, a simulator can be used to determine the above effects.

[00177] In addition to, or in lieu of, arithmetically determining one or more nominal contours, in certain embodiments, one or more nominal contours can be obtained experimentally. For example, features are created on one or more substrates with applicable design values for one or more process variables in the patterning process, and then measured (eg, scanning) to derive contours for the features. It is possible (using an electron microscope). The contour obtained by this measurement can be designated as the nominal contour. Additional or alternative, features are created on one or more substrates with various different values of one or more process variables (eg, one or more optical settings or conditions as described above). And then can be measured (eg, using a scanning electron microscope) to derive multiple contours of the feature, each contour being at a different value in one or more process variables. be. The contours obtained by these measurements can be designated as the nominal contours in which each nominal contour is associated with a different value of one or more process variables. Thus, as a particular example, each nominal contour can be associated with different focus settings or conditions, such as constant variance from optimal focus.

[00178] Thus, in certain embodiments, one or more nominal contours can represent the shape of the contours. For example, one or more nominal contours can represent the shape of the contour under certain focal conditions. Therefore, in the case of a plurality of nominal contours acquired under a plurality of different focal conditions, each nominal contour can represent the shape of the contour predicted under the applicable focal conditions.

[00179] Thus, in certain embodiments, and as described further, by reference to nominal contours, each with a nominal contour associated with a different value of one or more process variables (eg, focal points). , The shape of the contour can be predicted for any value of one or more process variables. If nominal contours are not available for a particular value of one or more process variables, contours can be generated by interpolation, extrapolation, etc. Thus, as an example, the contour shape of one or more applicable pattern features of interest can be predicted for any focal value at a location on the substrate.

[00180] Thus, in 1710, a first contribution / fingerprint can be obtained for one or more process variables for which one or more nominal contours have been determined. The first contribution / fingerprint can be obtained using any of the techniques described herein. In certain embodiments, the first contribution / fingerprint corresponds to the pre-etching situation.

[00181] In some embodiments, the first contribution / fingerprint is of focus, and thus in some embodiments, the first contribution / fingerprint is a substrate-wide focus map. In certain embodiments, the first contribution / fingerprint can be generated based on data obtained from a lithographic device used to pattern the substrate. For example, a lithographic apparatus can provide focus information (eg, focus residuals, etc.) and / or characteristics of high frequency stage movement (eg, MSD) to build a focus map. FIG. 18 shows an exemplary focus map 1800 for a patterning process in which different shades schematically represent different variances from a nominal focus (eg, optimal focus, average focus, etc.).

[00182] In 1720, the first contribution / fingerprint is used to select the nominal contour of the feature of interest. For example, the value of one or more process variables for which one or more nominal contours have been determined at a particular location on the substrate and for a particular feature of interest is the first contribution / fingerprint 1710. It can be determined from and then used to select one or more associated nominal contours. Thus, in one example where the first contribution / fingerprint 1710 is the focus map, the focus estimate at a substrate position in the focus map can be used to obtain the associated nominal contour as a prediction of the contour. Therefore, the predictive contour can be determined for each feature and for each substrate position.

[00183] An example of such a selection is drawn with reference to FIG. 18 again. FIG. 18 shows the nominal contours 1810, 1812, 1814 at the optimum focus. Here, at a position on the focal map 1800 associated with the nominal contour 1812, the focal map indicates that the focal point was at or near the optimal focal point. Therefore, for that position, the nominal contour 1812 is selected as the predictive contour. With respect to the position associated with the nominal contour 1810, the focus map shows that the focus was at negative out-of-focus-F. Therefore, in that case, instead of using the nominal contour 1810, the nominal contour determined with respect to the negative out-of-focus-F, the nominal contour 1820, is selected. As can be seen in FIG. 18, the nominal contour 1820 has the characteristics of a feature, but has a different shape due to negative out-of-focus-F. Similarly, with respect to the position associated with the nominal contour 1814, the focus map shows that the focus was at positive out-of-focus + F. Therefore, in that case, instead of using the nominal contour 1814, the nominal contour determined for positive out-of-focus + F, i.e. the nominal contour 1830, is selected. As can be seen in FIG. 18, the nominal contour 1830 has the characteristics of a feature, but has a different shape due to positive out-of-focus + F. Thus, at the -F focus, the nominal contour at the optimum focus changes shape, and similarly, at the + F focus, the nominal contour at the optimum focus changes shape. Thus, for example, if F is the variance from the optimum focus and + F and -F have the same absolute value, the respective contour shape changes can be the same as shown in FIG.

[00184] Returning to FIG. 17, in 1730, for one or more process variables other than one or more process variables for which one or more nominal contours were determined, a second contribution / fingerprint. Can be obtained. The second contribution / fingerprint 1730 can be obtained using any of the techniques described herein. In certain embodiments, the second contribution / fingerprint corresponds to a post-etching situation.

[00185] In some embodiments, the second contribution / fingerprint 1730 is of a critical dimension, so in some embodiments the second contribution / fingerprint is a board-wide critical dimension map (eg, critical). Dimension uniformity map). In certain embodiments, this contribution / fingerprint can be generated using a metrology tool based on data measured on one or more substrates with a pattern after the etching step of the patterning process. FIG. 19 shows an exemplary critical dimension map 1900 of the patterning process, where different shades schematically show different variances from nominal critical dimensions (eg, design critical dimensions, average critical dimensions, etc.). Thus, for example, the critical dimension map 1800 can represent the critical dimensions measured with respect to the anchor aspect and may be plotted as a percentage of the average critical dimensions.

[00186] In certain embodiments, the second contribution / fingerprint is for one or more process variables of the second contribution / fingerprint of one or more process variables associated with the first contribution / fingerprint. The impact is corrected. So, for example, if the first contribution / fingerprint is the focus map and the second contribution / fingerprint is the critical dimension map, then the second contribution / fingerprint is in the second contribution / fingerprint. The effect of focus on critical dimensions (eg, focus information (eg, focus residuals, etc.) and / or characteristics of high frequency stage movement) can be corrected. Details of determining such effects are described above. For example, FEM can be used to isolate the effect of focus on CD that occurs during the pattern transfer step and then remove that effect from the post-etched CD measurement. Additional or alternative, by processing various lithographic device signals, it is possible to obtain an estimate of the focal point during pattern transfer and its effect on the CD and then remove that effect from the post-etched CD measurement. can. As will be appreciated, this modification incorporates the modification directly into the second contribution / fingerprint, after the value has been selected from the second contribution / fingerprint as described in the context of 1740 below. It can be done in a variety of ways, including making corrections.

[00187] In 1740, a second contribution / fingerprint 1730 is used to modify the nominal contours of the feature of interest. In certain embodiments, the nominal contour is the nominal contour selected in 1720. However, the selection in 1720 may be omitted (although modifications may be made) if a nominal contour has already been selected for the feature of interest without using the selection step in 1720. ). Similarly, the modification of 1740 may be omitted if the nominal contour is selected in step 1720 and it does not require modification.

Thus, in 1740, for example, at a particular location on the board, and for a particular feature of interest, the value of one or more other process variables may be from the second contribution / fingerprint 1730. Obtained and then used that value to determine the appropriate modification of the nominal contour. In one embodiment, this modification is a change in the size of the nominal contour. Therefore, in one example where the second contribution / fingerprint 1730 is a critical dimension map, a nominal contour (eg 1720 was selected) as a contour prediction using a critical dimension estimate at a board location in the critical dimension map. The size of the nominal contour) can be modified. Therefore, the predictive contour can be determined for each feature and for each substrate position.

[00189] In one embodiment, from the second contribution / fingerprint 1730, it is assumed that the values of one or more other process variables obtained at a given location apply to the entire feature of interest. Therefore, if the critical dimension of a feature represented in the second contribution / fingerprint 1730 deviates by a certain amount (eg 5%) from a critical dimension (eg average critical dimension), then which feature in the same position All critical dimensions also deviate by the same amount. Therefore, this assumption means that the perfect contour is resized by the same amount as the deviation of the critical dimension. Therefore, if the full contour size of a feature at a location on the board changes by the same amount as the critical dimension of the anchor aspect at the same location measured to obtain a second contribution / fingerprint 1730. Assuming that a critical dimension decreases by x%, the nominal contour decreases by x%, and a critical dimension increases by x%, the nominal contour increases by x%. This change is evenly distributed throughout the contour.

[00190] With reference to FIG. 19 again, an example of such a size change is drawn. In FIG. 19, nominal contours 1910, 1912, 1914 are shown. Here, at a position on the critical dimension map 1900 associated with the nominal contour 1912 (which can be selected using step 1720), the critical dimension map is that the critical dimension is the nominal critical dimension (eg, design critical). Indicates that it matches the dimension, average critical dimension, etc.). Therefore, for that position, the nominal contour 1912 is selected as the predictive contour. With respect to the position associated with the nominal contour 1910, the critical dimension map shows that the critical dimension was a certain amount (eg, -x%) lower. Therefore, in that case, instead of using the nominal contour 1910, the nominal contour 1910 is reduced by the above amount (eg, -x%) in order to obtain the predicted contour 1920. As can be seen in FIG. 19, contour 1920 has the characteristics of a feature, but with a smaller size due to the lower critical dimensions described above. Similarly, with respect to the position associated with the nominal contour 1914, the critical dimension map shows that the critical dimension was higher by some amount (eg + x%). Therefore, in that case, instead of using the nominal contour 1914, the size of the nominal contour 1914 is increased by the above amount (eg + x%) in order to obtain the predicted contour 1930. As can be seen in FIG. 19, the contour 1930 has the characteristics of a feature, but has a larger size due to the higher critical dimensions described above. Therefore, for a -x% critical dimension, the nominal contour is uniformly reduced but still retains its shape, and for a + x% critical dimension, the nominal contour is uniformly expanded, but still its shape. Keep shape.

[00191] Thus, in some embodiments, the values of these two assumptions (ie, one or more process variables (eg, focal points) can predict the shape of the contour, and one or more. Given that the value of another process variable (eg CD) can predict the size of the contour), the shape and size of the contour is based on a value selected from one or more contributions / fingerprints. , Can be predicted about features. In particular, the complete contour of one or more features of interest can be predicted for any location on the substrate. In some embodiments, the feature of interest is a determined hotspot.

[00192] In certain embodiments, contours are predicted from critical dimension and focus measurements. In certain embodiments, contours are estimated using critical dimensions and focus fingerprints.

[00193] In 1750, predictive contours can be used in various applications. For example, a contour can determine whether a defect is predicted by itself, or by checking in relation to one or more other contours (eg, necking, bridging, etc.). One or more checks can be performed for line pullback, line thinning, overlapping, resist top loss, and / or resist undercut). This technique can be particularly useful, for example, for determining bridging defects. These defects are caused by two features that come into contact with each other, which in principle can occur at any point on the contours of the two features (depending on the particular CD, focus, and / or overlay condition at that location). ). The use of contours allows for a more robust assessment of such defects, for example, as compared to the assessment of dimensions at cut lines on adjacent features.

[00194] As another example, contours can be used to evaluate edge placement positions and / or errors, which can also be used to identify defects such as bridging. In practice, this technique can provide a computational metrology for determining edge placement errors. For example, prediction contours can be used to predict "virtual" edge placement errors. This can be used, for example, in predicting multi-patterning defects caused by edge placement errors. Therefore, defects caused by edge placement errors can be estimated using CD and focus fingerprints.

[00195] An example of how predictive contours can be used to determine potential defects is the spacer-and-cut layer process. in process). An example of this is shown in FIGS. 20A-C. Figure 20A depicts an exemplary spacer-and-cut layer process in which Line 2000 is "cut" using pattern 2010. Figure 20A depicts the nominal process in which Line 2000 and Pattern 2010 are in the desired alignment, size, etc. The result of such a process is a clean cut of the central line 2000 by pattern 2010. Here, using the maps 1800 and 1900 schematically shown in FIG. 20, the process of FIG. 17 can be applied to determine the predicted contours of line 2000 and pattern 2010. In this example shown in Figure 20B, the predicted contours for line 2000 are as expected, but the contours for pattern 2010 are larger in size (in this example, their shape has not changed, but it can. ) Is predicted. Here, FIG. 20C shows the results when the predictive contours of pattern 2010 are used in the spacer-and-cut layer process. As seen in 2020, pattern 2010 cuts parts of the upper and lower lines 2000 in an undesired way. Thus, in some embodiments, the predictive behavior of the spacer-and-cut layer process may be flagged as causing potential defects when cutting line 2000 using pattern 2010.

[00196] As another example, predictive contours can be used to determine potential defects in the litho-etch litho-etch (LELE) process. An example of this is shown in FIGS. 21A-C. FIG. 21A depicts an exemplary litho-etch and litho-etch process in which line 2100 is created in the first litho-etch process and pattern 2110 is interleaved in line 2100 by the second litho-etch process. Figure 21A depicts a nominal process in which lines 2100 and pattern 2110 are in the desired alignment, size, etc. The result of such a process is a designed separation of pattern 2110 and line 2100. Here, using the maps 1800 and 1900 schematically shown in FIG. 21, the process of FIG. 17 can be applied to determine the predicted contours of lines 2100 and pattern 2110. In this example shown in FIG. 21B, the predicted contours of line 2100 are as expected, but the contours of pattern 2110 are determined to be predicted to increase in size and change shape. Here, FIG. 21C shows the results when the predictive contours of pattern 2110 are used in the litho-etch and litho-etch process. As seen in 2120, pattern 2110 is undesirably too close to the lower line 2100 (eg, the space between features is below a certain threshold), that is, it becomes an overlapping area or the minimum distance. Violate requirements. This can result in bridging defects. In addition, pattern 2110 may have feature-shaped necking defects 2130, such as features that are too narrow (eg, width below a threshold). Thus, in one embodiment, the predictive behavior of the lithoetch and lithoetch process is flagged as causing one or more potential defects when pattern 2110 is created in conjunction with line 2100. May be good. Therefore, in certain embodiments, defect prediction in the application example of the multiple exposure LELE type

[00197] Another possible use of predictive contours is to use predictive contours in model calibration, especially in calibration of optical proximity effect correction models. Predictive contours allow correction of etching and / or focal fingerprints on the measured gauge (contour). For example, this predictive contour can be used to calibrate the model after mask data preparatory etching.

[00198] Thus, in one embodiment, the technique allows contour prediction from virtual wafers, so the technique combines metrology with computations to generate contours without the need to measure contours. In certain embodiments, the technique allows the prediction of contour and edge placement error variability across substrates given the appropriate fingerprints (eg, post-etching critical dimension fingerprints and lithographic device focus fingerprints). .. In certain embodiments, the technique allows for better predictive power of multi-patterning. In certain embodiments, the technique allows pattern control by using predictive contour-based edge placement optimization.

[00199] The results of the methods herein (or other results derived from the results of the methods herein (eg, defect prediction)) are produced by the process during the patterning process or the control of the equipment within it, the patterning process. It can be used for a variety of purposes, including substrate monitoring, patterning process processes or equipment design. For example, the above results or other results derived from them can be used to modify the equipment or process of the patterning process for further processing of the substrate or for processing another substrate. For example, the above results can be used to predict defects. Defect prediction is a device or process of a patterning process, for example, to control a metrology tool to inspect the affected area and / or for further processing of a substrate, or for processing another substrate. Can be used to change. Further, using the above results, designing a patterning process by, for example, deriving a dose recipe for modifying a lithographic apparatus, enabling the design of a patterning device and its pattern, setting up the process, and the like. Can be done. In addition, the above results include model calibration, eg, light proximity effect correction model, source-mask optimization model, lithography manufacturing check model, resist model, imaging model, measurement model (eg, modeling the process of measurement), etc. Can be used for calibration of. The above results can be used to determine one or more variables of the process that can later be used for various purposes (eg, optimal exposure and / or optimal dose). As will be understood, there can be many other uses.

[00200] FIG. 22 is a block diagram showing a computer system 100 capable of assisting in the implementation of the methods and flows disclosed herein. The computer system 100 includes a bus 102 or other communication mechanism for communicating information, and a processor 104 (or a plurality of processors 104 and 105) coupled with the bus 102 for processing information. Computer system 100 also includes main memory 106 coupled to bus 102 to store information and instructions executed by processor 104, such as random access memory (RAM) or other dynamic storage devices. The main memory 106 may also be used to store temporary variables or other intermediate information during the execution of instructions executed by the processor 104. Computer system 100 further includes read-only memory (ROM) 108, or other static storage device coupled to bus 102 to store static information and instructions for processor 104. A storage device 110 such as a magnetic disk or optical disk for storing information and instructions is provided and coupled to the bus 102.

[00201] The computer system 100 may be coupled via a bus 102 to a display 112, such as a cathode ray tube (CRT), a flat panel, or a touch panel display, for displaying information to a computer user. An input device 114 containing alphanumeric characters and other keys is coupled to bus 102 to communicate information and command selections to processor 104. Another type of user input device is a cursor control unit such as a mouse, trackball, or cursor direction key for communicating direction information and command selection to the processor 104 and for controlling the movement of the cursor on the display 112. It is 116. This input device typically has two axes that allow the device to be located in a plane on two axes (first axis (eg x) and second axis (eg y)). Has a degree of freedom. A touch panel (screen) display may be used as an input device.

[00202] According to one embodiment, a portion of the process is performed by computer system 100 in response to processor 104, which executes one or more sequences of one or more instructions contained in main memory 106. You may. Such instructions may be read into main memory 106 from another computer-readable medium, such as storage device 110. Execution of a sequence of instructions contained in main memory 106 causes processor 104 to perform the process steps described herein. One or more processors in a multiplex configuration may be used to execute a sequence of instructions contained in main memory 106. In some alternative embodiments, a hardwired circuit may be used in place of or in conjunction with the software instructions. Therefore, the description herein is not limited to a particular combination of hardware circuits and software.

[00203] As used herein, the term "computer-readable medium" refers to any medium involved in providing instructions to processor 104 for execution. Such media can take many forms, including, but not limited to, non-volatile media, volatile media, and transmission media. Non-volatile media include, for example, optical or magnetic disks such as storage device 110. Volatile media include dynamic memory such as main memory 106. Transmission media include coaxial cables, copper wires and optical fibers, including wires including bus 102. The transmission medium can also be in the form of sound waves or light waves, such as those generated during radio frequency (RF) and infrared (IR) data communications. Common forms of computer-readable media include, for example, floppy disks, flexible disks, optical discs, magnetic tapes, other magnetic media, CD-ROMs, DVDs, other optical media, punch cards, paper tapes, other with hole patterns, etc. Includes physical media, RAM, PROM, and EPROM, FLASH-EPROM, other memory chips or cartridges, carriers as described below, or other media readable by a computer.

[00204] Various forms of computer-readable media may be involved in carrying one or more sequences of one or more instructions to processor 104 for execution. For example, the instructions may initially be on the remote computer's magnetic disk. The remote computer can load the instructions into its dynamic memory and send the instructions over the telephone line using a modem. A modem local to computer system 100 can receive data on the telephone line and use an infrared transmitter to convert the data into infrared signals. An infrared detector coupled to the bus 102 can receive the data carried by the infrared signal and put that data on the bus 102. The bus 102 transports data to the main memory 106, from which the processor 104 reads and executes instructions. Instructions received by the main memory 106 may optionally be stored in the storage device 110 before or after execution by the processor 104.

[00205] Computer system 100 preferably also includes communication interface 118 coupled to bus 102. Communication interface 118 provides bidirectional data communication coupled to network link 120 connected to local network 122. For example, the communication interface 118 may be a Digital Integrated Services Digital Network (ISDN) card or modem that provides a data communication connection to the corresponding type of telephone line. As another example, the communication interface 118 may be a local area network (LAN) card that provides a data communication connection to a compatible LAN. A wireless link may be implemented. In such an implementation, the communication interface 118 transmits and receives electrical, electromagnetic, or optical signals carrying digital data streams representing various types of information.

[00206] Network link 120 generally provides data communication to other data devices through one or more networks. For example, network link 120 can provide a connection through local network 122 to a data device operated by host computer 124 or Internet service provider (ISP) 126. The ISP126 then provides data communication services over a worldwide packet data communication network (now commonly referred to as the "Internet" 128). Both local networks 122 and Internet 128 use electrical, electromagnetic, or optical signals that carry digital data streams. Signals through various networks that carry digital data to and from computer system 100, and signals on network link 120 and through communication interface 118, are examples of carrier wave forms that carry information. ..

[00207] Computer system 100 can send messages and receive data, including program code, through one or more networks, network links 120, and communication interfaces 118. In the Internet example, the server 130 may send the application program request code through the Internet 128, the ISP 126, the local network 122, and the communication interface 118. Some such downloaded applications can provide, for example, lighting optimizations of embodiments. The received code may be executed by processor 104 when received and / or stored in storage device 110 or other non-volatile storage for later execution. In this way, the computer system 100 may acquire the application code in the form of a carrier wave.

[00208] The embodiments of the present disclosure may be implemented in hardware, firmware, software, or any combination thereof. The embodiments of the present disclosure may be implemented as instructions stored on a machine-readable medium that can be read and executed by one or more processors. The machine-readable medium may include a mechanism for storing or transmitting information in a form readable by a machine (eg, a computing device). For example, machine-readable media can be read-only memory (ROM), random access memory (RAM), magnetic disk storage media, optical storage media, flash memory devices, electrical, optical, sound, or other forms of propagating signals (eg,). It can include carriers, infrared signals, digital signals, etc.). In addition, firmware, software, routines, and instructions may be described herein as performing a particular operation. However, such statements are for convenience only, and such behavior may actually result from computing devices, processors, controllers, or other devices that execute firmware, software, routines, instructions, etc. Should be understood.

[00209] In the block diagram, the illustrated components are depicted as discrete functional blocks, but embodiments are not limited to systems in which the functionality described herein is organized as illustrated. The functionality provided by each component may be provided by software or hardware modules organized differently from those currently depicted, eg, such software or hardware may be mixed, combined. , Reproduction, division, distribution (eg, within a data center or geographically), or may be organized differently. The functionality described herein may be provided by one or more processors on one or more computers that execute code stored on a tangible, non-transitory machine-readable medium. In some cases, a third-party content delivery network may host some or all of the information transmitted over the network, in which case the information (eg, content) is said to be supplied or provided. The information may be provided by sending an instruction to read the information from the content distribution network to the extent that the information is provided.

[00210] Unless otherwise stated, terms such as "process," "calculate," "calculate," and "determine" are used throughout this specification, as will be apparent from the above description. It is recognized that the description refers to the operation or process of a particular device, such as a dedicated computer or similar dedicated electronic processing / computing device.

[00211] The reader should be aware that this application describes some inventions. Rather than splitting these inventions into multiple separate patent applications, the applicant combines these inventions into a single document because their related subject matter is suitable for the economics of the filing process. ing. However, the different advantages and aspects of such inventions shall not be combined into one. In some cases, embodiments address all of the shortcomings described herein, but these inventions are useful independently, and some embodiments are part of such a problem. It shall be understood that it provides other unmentioned benefits that will become apparent to those skilled in the art who only address or scrutinize this disclosure. Due to cost constraints, some inventions disclosed herein may not be claimed at present, and may be claimed in a later application, such as a continuation application, or by amendment of the current claims. There is. Similarly, due to space constraints, neither the abstract part nor the summary part of this document should be considered to include a comprehensive list of all of the above inventions or all aspects of the above invention.

[00212] The present specification and drawings are not intended to limit the present invention to the specified forms disclosed, and conversely, the intent is defined by the appended claims. It shall be understood to include all modified, equivalent, and alternative forms that fall within the spirit and scope.

Modifications and alternative embodiments of various aspects of the invention will become apparent to those skilled in the art in light of the present specification. Accordingly, the specification and drawings should be considered merely as examples and are intended to teach those skilled in the art general aspects of carrying out the present invention. It is understood that the embodiments of the invention illustrated and described herein should be considered as examples of embodiments. Elements and materials may be used in place of those illustrated and described herein, all of which will be apparent to those skilled in the art after benefiting from the present specification of the present invention. Inversions, reordering, or deletions may be made, specific features may be used independently, and features of multiple embodiments or combinations of features may be combined. Modifications may be made to the elements described herein without departing from the spirit and scope of the invention as described in the claims below. The headings used herein are for structural purposes only and are not intended to be used to limit the scope of this specification.

[00214] Throughout this application, the word "may" does not mean obligatory (ie, "must") but allows (ie, "must"). It is used in the meaning of "may"). Words such as "include," "include," and "includes" mean "include without limitation." Throughout this application, the singular forms "a", "an", and "the" include multiple referents unless the content clearly indicates otherwise. So, for example, a reference to an "one (an)" element or an "one (a)" element is "one or more." Includes a combination of two or more elements, despite the use of other terms and phrases for one or more elements such as "more)". The term "or" is non-exclusive, that is, includes both "and" and "or", unless otherwise indicated. For example, terms that express conditional relationships such as "in response to X, Y", "in X, Y", "if X, Y", "in X, Y", etc., are causal conditions that require antecedents. And the antecedent is a sufficient causal condition, or the antecedent is a causal condition that contributes to the result (for example, "when the condition Y is acquired, the state X occurs" is "only when Y, X is Includes causal relationships (including "occurs" and "X occurs at Y and Z"). Such conditional relationships are not limited to the results obtained immediately following the antecedent, as some results can be delayed, and in conditional statements, the antecedent is related to those results, for example, the antecedent. The antecedent is related to the likelihood of an outcome. Statements in which multiple attributes or functions are associated with multiple objects (eg, one or more processors perform steps A, B, C, and D) are all such unless otherwise instructed. Attribute or function is associated with all such objects, and a subset of attributes or functions is associated with a subset of attributes or functions (eg, each of all processors is in step A ~. D is performed, and processor 1 performs step A, processor 2 performs a part of step B and step C, and processor 3 performs a part of step C and step D). Unless otherwise specified, a statement in which one value or action is "based on" another condition or value is that the condition or value is the only factor, and that condition or value is of multiple factors. Includes both when it is one of the factors. Unless otherwise indicated, the statement that "each" example of a population has a trait excludes cases where otherwise identical or similar members of a larger population do not have that trait.

[00215] Embodiments can be further described using the following clauses: 1. Obtaining the first contribution of the first process variable to be modeled to the metrological data of the pattern on the board. Obtain the metrology data by retrieving the second contribution of the second unmodeled process variable to the pattern metrology data and by integrating the first and second contributions with the hardware computer. To do and Including methods. 2. The method described in Clause 1, where the metrological data includes critical dimension (CD), critical dimension uniformity (CDU), side wall angle, edge position, overlay, focus, pattern shift, or a combination selected from them. .. 3. The method described in Clause 1 or 2, where the metrological data contains pattern group statistics. 4. The method according to any one of clauses 1-3, wherein the second process variable is a characteristic of the process downstream of substrate exposure. 5. The method described in Clause 4, where the process is the development of a resist layer on a substrate. 6. The method described in Clause 4, where the process is etching the substrate. 7. The method described in any one of Clauses 1 to 3, wherein the second process variable is the characteristics of the substrate. 8. The method according to any one of clauses 1 to 3, wherein the second process variable is the characteristic of the resist layer on the substrate. 9. The method described in any one of clauses 1 to 8 in which the value of the second process variable is unknown. 10. A clause that integrates the first and second contributions, including adding the first and second contributions, or convolving the first and second contributions. The method according to any one of 1 to 9. 11. Includes obtaining the contribution of the second process variable to the metrology data of the pattern on the board by removing the contribution of the first process variable to the metrology data from the metrology data by a hardware computer. ,Method. 12. The method described in Clause 11, in which the first process variable is modeled. 13. The method described in Clause 12, where the acquisition of the contribution of the first process variable is by modeling. 14. The method described in Clause 12, where the contribution of the first process variable is a non-linear function of the second process variable. 15. The method described in Clause 11, where the first process variable is not modeled. 16. The method described in Clause 15, where the contribution of the first process variable is determined experimentally or empirically. 17. 17. The contribution of changes in one or more process variables in the second group to the first pattern of metrology data on the board, the contribution of changes in one or more process variables in the first group to the metrology data. On the board by a hardware computer based on the contribution of changes in one or more process variables of the second group to the first pattern of metrology data. A method that involves obtaining the contribution of changes in one or more of the process variables of a second group to the second pattern of metrological data. 18. One or more of one or more process variables in the first group based on the contribution of changes in one or more process variables in the second group to the second pattern of metrological data. The method of clause 17, further comprising reducing the probability of defects in the second pattern by adjusting the variables of. 19. The method described in Clause 18, which reduces the probability of defects in the second pattern without making measurements in the second pattern. 20. The contribution of changes in one or more process variables of the second group to the second pattern of metrology data is obtained without obtaining the second pattern of metrology data, as described in Clause 17. the method of. 21. Contribution of changes in one or more process variables in the first group to the second pattern of metrology data, one or more process variables in the second group to the second pattern of metrology data The method described in Clause 17, further comprising obtaining the contribution of the change in the second pattern by removing it from the metrology data. 22. The contribution of changes in one or more process variables in the first group to the metrology data in the second pattern gets the values of one or more process variables in the first group for the second pattern. The method described in Clause 21, which is obtained without doing so. twenty three. Contribution of changes in one or more process variables in the first group to the second pattern of metrology data, changes in one or more process variables in the second group to the second pattern of metrology data Further reducing the probability of defects in the second pattern by adjusting one or more of the process variables in one or more of the first group based on contributions, or both. Including, the method described in Clause 21. 24. The contribution of changes in one or more process variables in the second group to the first pattern of metrology data, the contribution of changes in the process variables in the first group to the first pattern of metrology data. Of the second pattern on the substrate, which is obtained by removing it from the metrology data and based on the contribution of changes in one or more process variables of the second group to the metrology data of the first pattern. Obtaining the contribution of changes in one or more of the second group of process variables to the metrology data and, by hardware computer, one or more of the second group to the second pattern of metrology data. A method that includes obtaining a sub-process window (sub-PW) that the first group of process variables for a second pattern spans, based on the contribution of changes in the process variables. 25. The method of clause 24, wherein the first group contains all process variables that are modeled and the second group contains all process variables that are not modeled. 26. Clause 24 further includes reducing the probability of defects in the second pattern by adjusting the value of one or more of the process variables in the first group based on the sub-PW. the method of. 27. Integrate the contribution of changes in one or more process variables in the first group modeled to the metrology data and the contributions of changes in one or more process variables in the second group not modeled to the metrology data. By doing so, it includes obtaining estimates of the hotspot metrology data and, by a hardware computer, determining whether the hotspot is defective or not based on the estimates of the metrology data. ,Method. 28. The method described in Clause 27, where estimates are obtained without making measurements on the hotspot. 29. Removing the contribution of changes in one or more process variables in the second group to the pattern metrology data and the contribution of changes in one or more process variables in the first group from the metrology data. To obtain the values of the model parameters for determining the contribution of changes in one or more of the process variables in the second group to the metrology data by the hardware computer, the first of the patterns to the metrology data. A method that includes obtaining by fitting parameters to the contribution of changes in one or more of the process variables in two groups. 30. The method described in Clause 29, wherein one or more process variables in the first group include all process variables that are not modeled. 31. The method described in Clause 29, wherein all process variables of one or more process variables in the second group are modeled. 32. Obtaining estimates of the pattern metrology data on the board by integrating the contribution of changes in the modeled process variables to the metrology data and the contribution of changes to the unmodeled process variables to the metrology data. Determining whether the metrology data estimates meet the criteria, and if the estimates do not meet the criteria, the process variables modeled by the hardware computer and the non-modeled process variables for the metrology data. How to adjust the relationship with the contribution of change, including. 33. The method described in Clause 32, wherein adjusting the relationship involves changing the chemical composition of the resist on the substrate. 34. The method described in Clause 32, wherein adjusting the relationship involves changing the chemicals used to develop the resist on the substrate. 35. The method described in Clause 32, wherein adjusting the relationship involves changing the etching equipment used to etch the substrate. 36. The value of the first variable of the pattern of the substrate processed by the patterning process, or the pattern for the substrate processed by the patterning process, the fingerprint of the first variable with respect to the substrate, and the specific of the first variable. A method that includes calculating by integrating with values and determining the value of the second variable in the pattern, at least in part, based on the calculated value of the first variable. 37. The determination of the value of the second variable of the pattern is made by the hardware computer until the calculated value of the first variable and the reconstruction or simulation result based at least partially on the second variable meet the rules. The method described in Clause 36, which involves adjusting a second variable. 38. The method of clause 37, wherein the rule indicates that the difference between the measurement result of the pattern on the substrate and the reconstruction / simulation result exceeds or meets the threshold. 39. The method according to clause 36 or 37, wherein the reconstruction / simulation result is the calculated radiation distribution and the measurement result is the measured radiation distribution. 40. The method of any one of clauses 37-39, wherein a change in the first variable makes a greater difference in reconstruction or simulation results than the same change in the second variable. 41. The method described in any one of Clauses 36-40, wherein the first variable relates to the metrology target on the board. 42. The method described in Clause 41, where the first variable is the critical dimension of the pattern of the metrology target on the board. 43. The method of any one of clauses 36-42, wherein the second variable relates to the metrology target on the board. 44. The second variables are the side wall angle of the pattern of the metrology target, the height of the pattern of the metrology target, the thickness of the layer of the metrology target, the resist loss of the pattern of the metrology target under development, and the metrology target. 43. The method of clause 43, comprising one or more selected from the footing, the index of refraction of the layer of the metrology target, the absorption of the layer of the metrology target, and / or the extinction coefficient of the layer of the metrology target. 45. Any one of clauses 36-44, further including predicting the presence or absence of defects following the downstream process of pattern transfer onto the substrate based on the second variable of the pattern. The method described in. 46. The method of Clause 45, which adjusts the variables of the patterning process for one substrate or another substrate in response to the prediction of defects in the pattern, following the downstream process of transferring the pattern onto the substrate. 47. The method of paragraph 45 or 46, wherein the process comprises developing a resist layer on a substrate. 48. The method of any one of clauses 45-47, wherein the process involves etching the substrate. 49. The first contribution from one or more variables in the first group related to the lithographic apparatus of the patterning process and the first related to one or more fabrication processes prior to or after the transfer of the pattern in the lithographic apparatus. Integrate a second contribution from one or more variables in two groups with a third contribution from one or more variables in a third group related to the patterning device used to transfer the pattern. The method according to any one of paragraphs 36-48, further comprising obtaining a fingerprint of the first variable by doing so. 50. The variables of the first group are illumination by lithographic equipment, projection system of lithographic equipment, moving standard deviation of movement of substrate stage of lithographic equipment, moving average of movement of substrate stage, focus, dose, bandwidth, exposure period. 49. The method of clause 49, comprising one or more variables of high frequency laser bandwidth change, high frequency laser wavelength change, and / or substrate flatness. 51. The method of clause 49 or 50, wherein the second group of variables comprises one or more variables of spin coating, post-baking, developing, etching, deposition, doping, and / or packaging. 52. Any of Clauses 49-51, where the variables in the third group include mask CDs, the shape and / or position of the assist pattern, and / or one or more variables of adjustments applied by resolution enhancement techniques. The method described in paragraph 1. 53. The integration of the fingerprint of the first variable with the specific value of the first variable involves adding the fingerprint of the first variable to the specific value of the first variable. The method according to any one of 36 to 52. 54. The method of any one of clauses 36-53, wherein the particular value of the first variable is obtained by averaging the measurements of a group of first variables with respect to the substrate. 55. The method according to any one of clauses 36-53, wherein the particular value of the first variable is the design value of the first variable. 56. Any one of clauses 36-54, wherein the first variable used when determining the value of the second variable has a value limited within a specific range centered on the calculated value of the first variable. The method described in. 57. Obtaining a board-wide fingerprint of the variables associated with the pattern created by the patterning process and the value of the variable selected by the hardware computer system from the board position in the fingerprint of the pattern. A method of predicting the contours of a feature and including. 58. The method of clause 57, wherein the prediction involves selecting a nominal contour for a particular shape of the feature using the value of a variable. 59. The method of clause 58, wherein the nominal contours are obtained by simulation or arithmetic using a mathematical model. 60. The method according to clause 58 or 59, wherein the nominal contour is the expected contour in the resist. 61. The method of any one of Clauses 57-60, wherein the prediction involves resizing the nominal contour of the feature. 62. The method of clause 61, wherein the resizing is based on the value of an additional variable associated with the pattern, the value of which is selected at the same substrate position from the entire substrate fingerprint of the additional variable. 63. The method described in Clause 62, wherein the entire substrate fingerprint of additional variables corresponds to the post-etching situation. 64. The method described in Clause 62 or 63, where additional variables include critical dimensions. 65. The method described in any one of clauses 57-64, wherein the variable contains focus. 66. The method of any one of clauses 57-65, wherein the feature is a determined hotspot. 67. The method of any one of clauses 57-66, further comprising determining the edge placement position or error using predictive contours. 68. The method of any one of clauses 57-67, further comprising using a check against predictive contours to determine if a feature or another feature is likely to be defective. 69. The method of any one of clauses 57-68, further comprising calibrating a mathematical model using predictive contours. 70. The method described in Clause 69, wherein the mathematical model includes a light proximity effect correction model. 71. A computer program that includes a non-transitory computer-readable medium with recorded instructions that, when the instructions are executed by the computer, implements the method described in any one of paragraphs 1-70. program.

[00216] To the extent that a particular U.S. patent, U.S. patent application, or other material (eg, a dissertation) is incorporated, the text of the U.S. patent, U.S. patent application, and other material is in reference to the above material and the specification. Incorporated only to the extent that there is no conflict with the description and drawings specified in the document. In the event of such inconsistencies, such inconsistent text in the above-incorporated U.S. patents, U.S. patent applications, and other sources is not strictly incorporated herein.

Although specific embodiments of the present disclosure have been described above, it will be appreciated that these embodiments may be implemented in ways other than those described.