Method and apparatus for determining photolithographic process performance

Abstract

Method and apparatus for determining performance of a photolithographic patterning process, the apparatus or method being configured to or comprising: receiving an image of a portion of a substrate, the portion of the substrate comprising a first region and a second region, the first region comprising a first feature associated with a first photolithographic exposure of the substrate at a first time, and the second region comprising a second feature associated with a second photolithographic exposure of the substrate at a second time, wherein the first region and the second region do not overlap, and wherein the first feature and the second feature form a single feature extending along at least a portion of the first region and at least a portion of the second region; and determining performance of the photolithographic patterning process based on feature characteristics of the exposed first feature and / or the exposed second feature associated with a boundary between the first region and the second region.

Description

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This application claims priority to European applications No. 19195265.4 filed on September 4, 2019, No. 19198917.7 filed on September 23, 2019, No. 19217902.6 filed on December 19, 2019, No. 20157333.4 filed on February 14, 2020, and No. 20169297.7 filed on April 14, 2020, the entire contents of which are incorporated herein by reference. Technical Field

[0003] The present invention relates to an apparatus and method for determining the performance of a lithographic patterning process. In particular, the present invention relates to determining the performance of a lithographic patterning process based on characteristics of a boundary between a first region and a second region. Background Art

[0004] A lithographic apparatus is a machine configured to apply a desired pattern to a substrate. A lithographic apparatus can be used, for example, in the manufacture of integrated circuits (ICs). A lithographic apparatus can project a pattern (often also referred to as a "design layout" or "design") from a patterning device (e.g., a mask) onto a layer of radiation-sensitive material (resist) disposed on a substrate (e.g., a wafer).

[0005] To project a pattern onto a substrate, a lithographic apparatus can use electromagnetic radiation. The wavelength of this radiation determines the minimum size of a feature that can be formed on the substrate. Typical wavelengths currently used are 365 nm (i-line), 248 nm, 193 nm, and 13.5 nm. Compared to lithographic apparatuses that use radiation with a wavelength of, for example, 193 nm, lithographic apparatuses that use extreme ultraviolet (EUV) radiation with a wavelength in the range of 4 nm to 20 nm (e.g., 6.7 nm or 13.5 nm) can be used to form smaller features on a substrate.

[0006] Low k1 lithography can be used to process features smaller than the typical resolution limit of the lithographic apparatus. In this process, the resolution formula can be expressed as CD = k1 × λ / NA, where λ is the wavelength of the radiation used, NA is the numerical aperture of the projection optics in the lithographic apparatus, CD is the "critical dimension" (usually the smallest feature size printed, but in this case it is half the pitch), and k1 is the empirical resolution factor. In general, the smaller the k1, the more difficult it is to reproduce a pattern on the substrate that resembles the shape and size planned by the circuit designer in order to achieve specific electrical functionality and performance. To overcome these difficulties, complex fine-tuning steps can be applied to the lithographic projection apparatus and / or the design layout. These steps include, for example, but are not limited to, optimization of the NA, custom illumination schemes, use of phase-shifted patterning devices, various optimizations of the design layout (such as optical proximity correction (OPC, sometimes also called "optical and process correction") in the design layout), or other methods generally defined as "resolution enhancement techniques" (RET). Alternatively, a strict control loop for controlling the stability of the lithographic apparatus can be used to improve the reproduction of the pattern at low k1.

[0007] Patterning a layer on a substrate can include multiple steps. For example, a patterning device such as a mask may not be large enough to pattern the substrate at a single location. In some cases, the pattern to be exposed may fit on a single mask. The single mask can then be moved across the substrate to expose the same pattern multiple times on the same substrate. In other cases, the pattern to be exposed on the substrate (e.g., the pattern forming the device) may be too large to fit on a single mask. Several masks, each comprising a different portion of the pattern to be exposed, can be moved across the substrate in multiple independent steps. Multiple masks are moved across regions of the substrate to sequentially pattern different portions of the pattern. This decomposition of the pattern across or between different regions on the substrate may result in positioning errors of the exposed portions of the pattern on the substrate relative to each other. The exposed pattern may include, for example, alignment and / or magnification errors. Due to the small size of the patterned features, a higher degree of precision and accuracy may be required when positioning different patterned regions relative to each other. Errors in relative position may be referred to as stitching errors. Stitching errors may affect the quality of the exposed pattern on the substrate and the ultimate yield of the patterning process. Therefore, it would be desirable to provide methods and apparatus for reducing stitching errors and their negative impact on the photolithographic patterning process. Summary of the Invention

[0008] According to a first aspect of the present disclosure, there is provided an apparatus for determining performance of a lithographic patterning process, the apparatus comprising one or more processors configured to receive an image of a portion of a substrate, the portion of the substrate comprising a first region and a second region, the first region comprising a first feature associated with a first lithographic exposure performed on the substrate at a first time, and the second region comprising a second feature associated with a second lithographic exposure performed on the substrate at a second time, wherein the first region and the second region do not overlap. The one or more processors are further configured to determine the performance of the lithographic patterning process based on one or more feature characteristics of the exposed first feature and / or the exposed second feature associated with a boundary between the first region and the second region.

[0009] Alternatively, the boundary may include a portion of an outer edge of the first region and a portion of an outer edge of the second region.

[0010] Optionally, the first feature and the second feature may include at least one of a product feature and a dummy feature, the dummy feature having one or more dimensions identical to the product feature.

[0011] Alternatively, the first feature and the second feature may form a single feature extending along at least a portion of the first region and at least a portion of the second region.

[0012] Optionally, the one or more feature characteristics may include a distance indicator, comprising a distance between one or more axes of symmetry of the first feature and one or more axes of symmetry of the second feature and / or a physical distance between the first feature and the second feature.

[0013] Alternatively, the one or more feature characteristics may include a narrowing or thickening of a single feature at or near a boundary.

[0014] Optionally, the first features and the second features may form part of a patterned layer of photoresist or a material layer after being patterned by an etching process.

[0015] Optionally, determining the property may comprise analyzing the image to determine one or more characteristic properties of the first feature and / or the second feature associated with the boundary between the first region and the second region.

[0016] Optionally, determining the property may comprise performing a comparison of the first feature and / or the second feature of the image with a standard for the first feature and / or the second feature.

[0017] Optionally, determining the performance may further include determining the performance of one or more lithography patterning process characteristics based on the determined one or more characteristic features.

[0018] Optionally, the one or more characteristic features may include a spatial size of the first feature and / or the second feature.

[0019] Optionally, the one or more process characteristics may include one or more of magnification, translation and / or higher order deformation errors associated with patterning of the first region and / or the second region.

[0020] Optionally, the performance of the one or more process characteristics may be determined at least in part using a model that takes as input at least one of the one or more feature characteristics.

[0021] Optionally, the model may comprise a machine learning model.

[0022] Optionally, the model may comprise a neural network.

[0023] Optionally, the model may include vision techniques.

[0024] Optionally, the model can be configured to be trained based on a training set of images of portions of the substrate comprising a first feature and a second feature, wherein the first feature and / or the second feature of the images of the training set have one or more known characteristic properties associated with known performance of the lithography patterning process.

[0025] Optionally, each training set image may include a portion of a training substrate, wherein the portion of the training substrate includes a first feature associated with a first lithographic exposure of the training substrate at a first time and a second feature associated with a second lithographic exposure of the training substrate at a second time.

[0026] Optionally, the known characteristic properties and performance of the lithographic patterning process can be based at least in part on one or more measurements of one or more characteristic properties of the first feature and / or the second feature.

[0027] Optionally, the known performance of the photolithographic patterning process may include a known stitching error.

[0028] Alternatively, determining the performance of the lithographic patterning process may include determining a pre-processed image obtained by removing noise from the image, and identifying the one or more characteristic features based on the pre-processed image.

[0029] Optionally, determining the preprocessed image may include: determining a gradient magnitude of the image.

[0030] Optionally, determining the pre-processed image may include determining a binary image based on the image.

[0031] Optionally, determining the preprocessed image may include: detecting one or more line features in the image and / or the binary image; and rotating the image and / or the binary image so that at least one of the one or more line features is substantially parallel to or substantially perpendicular to the boundary between the first zone and the second zone.

[0032] Optionally, identifying one or more characteristic features from the pre-processed image may include applying a Fourier transform to portions of the pre-processed image for quantifying a stitching quality at a boundary between the first region and the second region.

[0033] Optionally, identifying the one or more characteristic features may further include determining duty cycles of the plurality of portions that are Fourier transformed, and determining the one or more characteristic features based on the duty cycles of the plurality of portions.

[0034] Optionally, identifying the one or more characteristic features may further include determining phases of the plurality of parts that are Fourier transformed, and determining the one or more characteristic features based on the phases of the plurality of parts.

[0035] Alternatively, the plurality of portions may include a plurality of pixel rows, wherein the pixel rows may be aligned with a boundary between the first region and the second region.

[0036] Optionally, determining the performance of the lithographic patterning process may include determining a first binary image based on the image, determining a second binary image based on a binary gradient of the image, and identifying the one or more characteristic features based on a combination of the first binary image and the second binary image.

[0037] Optionally, the one or more characteristic characteristics may include overlay accuracy.

[0038] Optionally, identifying the one or more characteristic features may use a regression model and / or a lookup table.

[0039] Optionally, determining the performance of the photolithographic patterning process may further include determining an indicator of stitching quality at a boundary between the first region and the second region.

[0040] Alternatively, the indicator may represent at least one of a flatness of a splice around a boundary between the first region and the second region and a skewness of a splice around a boundary between the first region and the second region.

[0041] Alternatively, the first region and the second region may form part of the same device on the substrate.

[0042] Alternatively, the first region may be a first field exposed on the substrate, and the second region may be a second field exposed on the substrate. The boundary may include a portion of an edge of the first field and a portion of an edge of the second field.

[0043] Optionally, determining the performance may include determining a splicing error between the first field and the second field.

[0044] Alternatively, the received image may include the substrate between exposures of subsequent layers on the substrate.

[0045] Alternatively, the received image may include a boundary between the first region and the second region extending in at least one direction.

[0046] Optionally, the processor may be configured to receive a plurality of images and determine a quality of the patterning process based on the plurality of images.

[0047] Optionally, the plurality of images include: a first image including a boundary between a first region and a second region in a first direction; and a second image including a boundary between the first region and another region in a second direction. The first direction and the second direction may not be parallel to each other.

[0048] Optionally, the first direction and the second direction may be substantially perpendicular to each other.

[0049] Optionally, the one or more processors may be further configured to: determine the performance of one or more process characteristics of the first image; and determine one or more process characteristics of the second image. The one or more processors may be further configured to combine the one or more process characteristics of the first image and the second image to determine the performance of the patterning process.

[0050] Alternatively, the plurality of images may depict a plurality of separate locations on the substrate.

[0051] Optionally, one or more process characteristics may be determined for the plurality of individual locations on the substrate.

[0052] Optionally, the one or more processors may be further configured to determine one or more corrections to the patterning process based on the performance of the lithographic patterning process.

[0053] Optionally, the one or more processors may be further configured to update the photolithography patterning process with the one or more corrections.

[0054] Optionally, updating the lithographic patterning process may include updating at least one of one or more exposure settings of a lithographic apparatus and a reticle design.

[0055] Alternatively, the photolithographic patterning process may be configured to use a reticle and electromagnetic radiation to pattern the substrate.

[0056] Optionally, the one or more processors may be further configured to control a measurement device to obtain an image.

[0057] Optionally, controlling the metrology device to obtain the image may include guiding the metrology device based on the one or more previously determined characteristic features.

[0058] Optionally, the metrology equipment may comprise an electron beam imager.

[0059] According to another aspect of the present disclosure, a method for determining performance of a photolithographic patterning process is provided. The method includes receiving an image of a portion of a substrate, the portion of the substrate comprising a first region and a second region, the first region comprising a first feature associated with a first photolithographic exposure performed on the substrate at a first time, and the second region comprising a second feature associated with a second photolithographic exposure performed on the substrate at a second time, wherein the first region and the second region do not overlap. The method further includes determining performance of the photolithographic patterning process based on one or more characteristic properties of the exposed first feature and / or the exposed second feature associated with a boundary between the first region and the second region.

[0060] Optionally, the boundary includes a portion of an outer edge of the first region and a portion of an outer edge of the second region.

[0061] Optionally, the first feature and the second feature may include at least one of a product feature and a dummy feature, the dummy feature having one or more dimensions identical to the product feature.

[0062] Alternatively, the first feature and the second feature may form a single feature extending along at least a portion of the first region and at least a portion of the second region.

[0063] Optionally, the one or more feature characteristics may include a distance indicator, comprising a distance between one or more axes of symmetry of the first feature and one or more axes of symmetry of the second feature and / or a physical distance between the first feature and the second feature.

[0064] Alternatively, the one or more feature characteristics may include a narrowing or thickening of a single feature at or near a boundary.

[0065] Alternatively, the first features and the second features may form part of a patterned layer of photoresist or a material layer after being patterned by an etching process.

[0066] Optionally, determining the property may comprise analyzing the image to determine one or more characteristic properties of the first feature and / or the second feature associated with the boundary between the first region and the second region.

[0067] Optionally, determining the property may comprise performing a comparison of the first feature and / or the second feature of the image with a standard for the first feature and / or the second feature.

[0068] Optionally, determining the performance may further include determining the performance of one or more lithography patterning process characteristics based on the determined one or more characteristic features.

[0069] Optionally, the one or more characteristic features may include a spatial size of the first feature and / or the second feature.

[0070] Optionally, the one or more process characteristics may include one or more of magnification, translation and / or higher order deformation errors associated with patterning of the first region and / or the second region.

[0071] Optionally, the performance of the one or more process characteristics may be determined at least in part using a model that takes as input at least one of the one or more feature characteristics.

[0072] Optionally, the model may comprise a machine learning model.

[0073] Optionally, the model may comprise a neural network.

[0074] Optionally, the model may include vision techniques.

[0075] Alternatively, the first region and the second region may form part of the same device on the substrate.

[0076] Alternatively, the first region may be a first field exposed on the substrate, and the second region may be a second field exposed on the substrate. The boundary may include a portion of an edge of the first field and a portion of an edge of the second field.

[0077] Optionally, determining the performance may include determining a splicing error between the first field and the second field.

[0078] Alternatively, the received image may include the substrate between exposures of subsequent layers on the substrate.

[0079] Alternatively, the received image may include a boundary between the first region and the second region extending in at least one direction.

[0080] Optionally, the method may further include receiving a plurality of images, and determining a quality of the patterning process based on the plurality of images.

[0081] Optionally, the plurality of images include the first image and the second image, the first image may include a boundary between a first region and a second region in a first direction, and the second image includes a boundary between the first region and another region in a second direction. The first direction and the second direction may not be parallel to each other.

[0082] Optionally, the first direction and the second direction may be substantially perpendicular to each other.

[0083] Optionally, the method may further include determining the performance of one or more process characteristics of the first image and determining one or more process characteristics of the second image. The method may further include combining the one or more process characteristics of the first image and the second image to determine the performance of the patterning process.

[0084] Alternatively, the plurality of images may depict a plurality of separate locations on the substrate.

[0085] Optionally, one or more process characteristics may be determined for the plurality of individual locations on the substrate.

[0086] Optionally, the method may further include determining one or more corrections to the patterning process based on the performance of the photolithographic patterning process.

[0087] Optionally, the method may further include: updating the photolithography patterning process through the one or more corrections.

[0088] Optionally, updating the lithographic patterning process may include updating at least one of one or more exposure settings of a lithographic apparatus and a reticle design.

[0089] Alternatively, the photolithographic patterning process may be configured to use a reticle and electromagnetic radiation to pattern the substrate.

[0090] Optionally, the method may further include: controlling a measurement device to obtain an image.

[0091] Optionally, controlling the metrology device to obtain the image may include guiding the metrology device based on the one or more previously determined characteristic features.

[0092] Optionally, the metrology equipment may comprise an electron beam imager. BRIEF DESCRIPTION OF THE DRAWINGS

[0093] Embodiments of the invention will now be described, by way of example only, with reference to the accompanying schematic drawings in which:

[0094] Figure 1 A schematic overview diagram depicting a lithographic apparatus;

[0095] Figure 2 A schematic overview depicting a lithography cell;

[0096] - Figure 3 A schematic depicting overall lithography, which represents the collaboration between three key technologies for optimizing semiconductor manufacturing;

[0097] - Figure 4 a flow chart depicting steps in a method of determining performance of a photolithographic patterning process;

[0098] - Figure 5 depicting a schematic diagram of a portion of a substrate including a first region having first and second features and a second region;

[0099] - Figure 6 a schematic diagram depicting a portion of a substrate including a plurality of features;

[0100] - Figure 7 a schematic diagram depicting an image obtained across a portion of a substrate;

[0101] - Figure 8 a flow chart depicting steps in a method of determining performance of a photolithographic patterning process;

[0102] - Figure 9 A flow chart depicting steps in a method of pre-processing an image for determining performance of a lithographic patterning process;

[0103] - FIG. 10( a ) depicts the signal for a row of pixels away from the boundary between the first and second zones;

[0104] - FIG. 10( b ) depicts the signal for a row of pixels close to and / or on the border between the first and second zones;

[0105] - Figure 11 A flow chart depicting steps in a method of determining an indicator for determining splice quality.

[0106] - Figure 12 A flow chart depicting steps in a method of training a machine learning model for analyzing and determining the performance of a lithographic patterning process.

[0107] - Figure 13 Depicts a two-dimensional matrix providing a schematic diagram of the effects of overlay stitching errors introduced in the x-direction as well as the y-direction. DETAILED DESCRIPTION

[0108] In this document, the terms "radiation" and "beam" are used to cover all types of electromagnetic radiation, including ultraviolet radiation (e.g., having a wavelength of 365 nm, 248 nm, 193 nm, 157 nm or 126 nm) and EUV (extreme ultraviolet radiation, e.g., having a wavelength in the range of about 5 nm to 100 nm).

[0109] As used herein, the terms "reticle," "mask," or "patterning device" may be broadly interpreted to refer to a general patterning device that can be used to impart a patterned cross-section to an incident radiation beam that corresponds to the pattern to be produced in a target portion of a substrate. The term "light valve" may also be used herein. In addition to classical masks (transmissive or reflective, binary, phase-shift, hybrid, etc.), examples of other such patterning devices include programmable mirror arrays and programmable LCD arrays.

[0110] Figure 1 A lithographic apparatus LA is schematically depicted. The lithographic apparatus LA comprises an illumination system (also referred to as illuminator) IL configured to condition a radiation beam B (e.g., UV radiation, DUV radiation, or EUV radiation), a mask support (e.g., mask table) T configured to support a patterning device (e.g., mask) MA and connected to a first positioner PM configured to accurately position the patterning device MA according to certain parameters, a substrate support (e.g., wafer stage) WT configured to hold a substrate (e.g., a resist-coated wafer) W and connected to a second positioner PW configured to accurately position the substrate support according to certain parameters, and a projection system (e.g., a refractive projection lens system) PS configured to project a pattern imparted to the radiation beam B by the patterning device MA onto a target portion C (e.g., comprising one or more dies) of the substrate W.

[0111] In operation, the illumination system IL receives a radiation beam from a radiation source SO, for example, via a beam delivery system BD. The illumination system IL may include various types of optical components for directing, shaping, and / or controlling the radiation, such as refractive, reflective, magnetic, electromagnetic, electrostatic, and / or other types of optical components, or any combination thereof. The illuminator IL may be used to condition the radiation beam B to have a desired spatial and angular intensity distribution in its cross-section at the plane of the patterning device MA.

[0112] The term "projection system" PS as used herein should be broadly interpreted as covering various types of projection systems suitable for the exposure radiation used and / or other factors such as the use of an immersion liquid or the use of a vacuum, including refractive, reflective, catadioptric, anamorphic, magnetic, electromagnetic and / or electrostatic optical systems, or any combination thereof. Any use of the term "projection lens" herein should be considered synonymous with the more general term "projection system" PS.

[0113] The lithographic apparatus LA may be of a type in which at least a portion of the substrate may be covered by a liquid having a relatively high refractive index (e.g. water) so as to fill the space between the projection system PS and the substrate W - this is also known as immersion lithography. More information on immersion techniques is given in US Pat. No. 6,952,253, which is incorporated herein by reference.

[0114] The lithographic apparatus LA may also be of a type having two or more substrate supports WT (also known as a "dual stage"). In such a "multi-stage" machine, the substrate supports WT may be used in parallel, and / or steps in preparation for subsequent exposure of a substrate W on one of the substrate supports WT may be performed while another substrate W on another substrate support WT is being used to expose a pattern on the other substrate W.

[0115] In addition to the substrate support WT, the lithographic apparatus LA can include a measurement stage. The measurement stage is configured to hold sensors and / or cleaning devices. The sensors can be configured to measure properties of the projection system PS or properties of the radiation beam B. The measurement stage can hold multiple sensors. The cleaning devices can be configured to clean parts of the lithographic apparatus, such as a portion of the projection system PS or a portion of the system for providing immersion liquid. The measurement stage can be moved beneath the projection system PS while the substrate support WT is away from the projection system PS.

[0116] In operation, a radiation beam B is incident on a patterning device (e.g. a mask MA) held on a mask support T and is patterned by a pattern (design layout) present on the patterning device MA. Having traversed the mask MA, the radiation beam B passes through a projection system PS which focuses the beam onto a target portion C of the substrate W. With the aid of a second positioner PW and a position measurement system IF, the substrate support WT can be accurately moved, for example in order to position different target portions C in the path of the radiation beam B in a focused and aligned position. Similarly, a first positioner PM and possibly a further position sensor (which is not in the position sensor) are provided. Figure 1 The mask alignment marks M1, M2 and the substrate alignment marks P1, P2 can be used to accurately position the patterning device MA relative to the path of the radiation beam B. The mask alignment marks M1, M2 and the substrate alignment marks P1, P2 can be used to align the patterning device MA and the substrate W. Although the substrate alignment marks P1, P2 occupy dedicated target portions as illustrated, the substrate alignment marks P1, P2 can be located in spaces between target portions. When the substrate alignment marks P1, P2 are located between target portions C, they are referred to as scribe lane alignment marks.

[0117] like Figure 2 As shown in FIG, the lithography apparatus LA may form part of a lithography cell LC (sometimes also referred to as a litho cell or (lithography) cluster), which typically also includes equipment for performing pre-exposure and post-exposure processes on a substrate W. Typically, these equipment include a spin coater SC for depositing a resist layer, a developer DE for developing the exposed resist, a chill plate CH for regulating the temperature of the substrate W (e.g., for regulating the solvent in the resist layer), and a bake plate BK. A substrate handler or robot RO picks up substrates W from input / output ports I / O1, I / O2, moves them between the various process equipment, and transfers them to a loading station LB of the lithography apparatus LA. The devices in the litho cell, also often collectively referred to as a coating and developing system (or track), are typically under the control of a coating and developing system control unit TCU, which itself may be controlled by a supervisory control system SCS, which may also control the lithography apparatus LA, for example, via a lithography control unit LACU.

[0118] In order to correctly and consistently expose the substrate W exposed by the lithographic apparatus LA, it is necessary to inspect the substrate to measure properties of the patterned structure, such as overlay error between subsequent layers, line thickness, critical dimension (CD), etc. For this purpose, an inspection tool (not shown) may be included in the lithographic cell LC. In particular, if inspection is performed before other substrates W in the same batch or lot are yet to be exposed or processed, if errors are detected, adjustments can be made to the exposure of subsequent substrates or other processing steps to be performed on the substrates W.

[0119] An inspection apparatus, which may also be referred to as a metrology apparatus, is used to determine properties of a substrate W, and in particular to determine how properties vary between different substrates W or how properties associated with different layers of the same substrate W vary between different layers. The inspection apparatus may alternatively be configured to identify defects on the substrate W and may, for example, be part of the lithography cell LC, or may be integrated into the lithography apparatus LA, or may even be a separate device. The inspection apparatus may measure properties of a latent image (the image in the resist layer after exposure), or properties of a semi-latent image (the image in the resist layer after a post-exposure bake step PEB), or properties of a developed resist image (in which exposed or unexposed portions of the resist have been removed), or even properties of an etched image (after a pattern transfer step such as etching).

[0120] Typically, the patterning process in the lithographic apparatus LA is one of the most important steps in the process, which requires a high degree of accuracy in the dimensioning and placement of structures on the substrate W. To ensure this high accuracy, three systems can be combined in Figure 3 . One of these systems is the lithography apparatus LA, which is (virtually) connected to a metrology tool MT (a second system) and to a computer system CL (a third system). The key to this "holistic" environment is to optimize the cooperation between these three systems to enhance the overall process window and provide a tight control loop to ensure that the patterning performed by the lithography apparatus LA remains within the process window. The process window defines a range of process parameters (e.g., dose, focus, overlay accuracy) within which a particular manufacturing process produces a defined result (e.g., a functional semiconductor device) - typically, within this defined result, variations in process parameters in the lithography process or patterning process are allowed.

[0121] The computer system CL can use (a portion of) the design layout to be patterned to predict which resolution enhancement technology to use, and perform computational lithography simulations and calculations to determine which mask layout and lithographic equipment settings achieve the maximum overall process window (in terms of resolution enhancement technology) for the patterning process. Figure 3 Typically, the resolution enhancement technique is configured to match the patterning possibilities of the lithographic apparatus LA. The computer system CL may also be used (e.g. using input from the metrology tool MT) to detect where within the process window the lithographic apparatus LA is currently operating to predict whether defects due to, for example, suboptimal processing may be present (e.g., in the process window). Figure 3 ) is depicted by an arrow pointing to “0” in the second scale SC2.

[0122] The metrology tool MT may provide input to the computer system CL to enable accurate simulation and prediction, and may provide feedback to the lithographic apparatus LA to identify, for example, possible drift in the calibration state of the lithographic apparatus LA (e.g., Figure 3 ) depicted by multiple arrows in the third scale SC3).

[0123] During photolithographic processes, it is frequently necessary to measure the structures produced, for example, for process control and verification. The tool used to perform such measurements is typically referred to as a metrology tool MT. Different types of metrology tools MT are known, including scanning electron microscopes or various forms of scatterometer metrology tools MT. Scatterometers are versatile instruments that allow measurement of parameters of the photolithographic process by having a sensor in the pupil or a plane conjugate to the pupil of the scatterometer objective (this measurement is typically referred to as pupil-based measurement), or by having a sensor in the image plane or a plane conjugate to the image plane (in which case the measurement is typically referred to as image- or field-based measurement). Such scatterometers and associated measurement techniques are further described in patent applications US20100328655, US2011102753A1, US20120044470A, US20110249244, US20110026032 or EP 1,628,164 A, which are incorporated herein by reference in their entirety. The aforementioned scatterometers can measure gratings using light from soft x-rays as well as light visible to the near IR wavelength range.

[0124] In a first embodiment, the scatterometer MT is an angle-resolving scatterometer. In this scatterometer, reconstruction methods can be applied to the measured signals to reconstruct or calculate the properties of the grating. This reconstruction can be obtained, for example, by simulating the interaction of the scattered radiation with a mathematical model of the target structure and comparing the simulated results with those measured. The parameters of the mathematical model are adjusted until the simulated interaction produces a diffraction pattern similar to the diffraction pattern observed from a real target.

[0125] In a second embodiment, the scatterometer MT is a spectroscopic scatterometer MT. In such a spectroscopic scatterometer MT, radiation emitted by a radiation source is directed onto a target, and reflected or scattered radiation from the target is directed onto a spectrometer detector, which measures the spectrum of the specularly reflected radiation (i.e., a measurement of the intensity as a function of wavelength). From this data, the structure or profile of the target that produced the detected spectrum can be reconstructed, for example, by rigorous coupled wave analysis and nonlinear regression, or by comparison with a library of simulated spectra.

[0126] In a third embodiment, the scatterometer MT is an ellipsometry scatterometer. An ellipsometry scatterometer allows parameters of a lithographic process to be determined by measuring scattered radiation for each polarization state. This metrology device emits polarized light (such as linear, circular, or elliptical) by using, for example, appropriate polarization filters in the illumination section of the metrology device. A source suitable for the metrology device can also provide polarized radiation. Various embodiments of existing ellipsometry scatterometers are described in U.S. patent applications Ser. Nos. 11 / 451,599, 11 / 708,678, 12 / 256,780, 12 / 486,449, 12 / 920,968, 12 / 922,587, 13 / 000,229, 13 / 033,135, 13 / 533,110, and 13 / 891,410, which are incorporated herein by reference in their entireties.

[0127] Examples of known scatterometers typically rely on the provision of dedicated measurement targets, such as underfilled targets (targets in the form of simple gratings or overlapping gratings in different layers, which are large enough so that the measurement beam produces a spot smaller than the grating) or overfilled targets (whereby the illumination spot partially or completely encompasses the target). Furthermore, the use of metrology tools (e.g., angle-resolved scatterometers that illuminate underfilled targets such as gratings) allows the use of so-called reconstruction methods, in which the properties of the grating can be calculated by simulating the interaction of scattered radiation with a mathematical model of the target structure and comparing the simulation results with those of the measurements. The parameters of the model are adjusted until the simulated interaction produces a diffraction pattern similar to that observed from a real target.

[0128] In one embodiment of the scatterometer MT, the scatterometer MT is adapted to measure the overlay accuracy of two misaligned gratings or periodic structures by measuring the reflection spectrum and / or an asymmetry in the detection configuration (the asymmetry being related to the range of overlay accuracy). The two (usually overlapping) grating structures may be applied in two different layers (not necessarily consecutive layers), and the two grating structures may be formed to be at substantially the same position on the wafer. The scatterometer may have a symmetrical detection configuration, such as described in co-owned patent application EP1,628,164A, so that any asymmetry can be clearly identified. This provides a straightforward way to measure misalignment in the gratings. Other examples of measuring overlay error between two layers containing periodic structures when the target is measured through an asymmetry of the periodic structure may be found in PCT Patent Application Publication No. WO2011 / 012624 or U.S. Patent Application No. US20160161863, both of which are incorporated herein by reference in their entirety.

[0129] Other parameters of interest may be focus and dose. Focus and dose may be determined simultaneously by scatterometry (or alternatively by scanning electron microscopy) as described in U.S. Patent Application No. US2011-0249244, which is incorporated herein by reference in its entirety. A single structure may be used that has a unique combination of critical dimension and sidewall angle measurements for each point in the focus energy matrix (FEM, also called the focus exposure matrix). If these unique combinations of critical dimension and sidewall angle are available, focus and dose values ​​may be uniquely determined based on these measurements.

[0130] The metrology target can be the entirety of a composite grating formed primarily in resist by a photolithography process and also formed after, for example, an etching process. Typically, the spacing and linewidth of the structures in the grating depend heavily on the measurement optics (particularly the NA of the optics) to be able to capture the diffraction orders from the metrology target. As previously indicated, the diffraction signal can be used to determine the shift between two layers (also known as "overlay accuracy") or to reconstruct at least a portion of the original grating as produced by the photolithography process. This reconstruction can be used to provide guidance on the quality of the photolithography process and to control at least a portion of the photolithography process. The target can have smaller subsegments configured to mimic the dimensions of a functional portion of the design layout in the target. Due to this subsegmentation, the target will behave more similarly to the functional portion of the design layout, resulting in an overall process parameter measurement that better resembles the functional portion of the design layout. The target can be measured in either an underfill mode or an overfill mode. In underfill mode, the measurement beam produces a spot that is smaller than the overall target. In overfill mode, the measurement beam produces a spot that is larger than the overall target. In this overfill mode, it is also possible to measure different targets simultaneously and thereby determine different process parameters simultaneously.

[0131] The overall measurement quality of a lithography parameter using a particular target is determined at least in part by the measurement profile used to measure the lithography parameter. The term "substrate measurement profile" may include one or more parameters of the measurement itself, one or more parameters of the one or more patterns being measured, or both. For example, if the measurement used in the substrate measurement profile is an optical measurement based on diffraction, one or more of the measured parameters may include the wavelength of the radiation, the polarization of the radiation, the angle of incidence of the radiation relative to the substrate, the orientation of the radiation relative to the pattern on the substrate, and the like. One of the criteria for selecting a measurement profile may, for example, be the sensitivity of one of the measurement parameters to process variations. More examples are described in U.S. patent application US2016-0161863, which is incorporated herein by reference in its entirety, and published U.S. patent application US2016 / 0370717A1.

[0132] Different areas on the substrate can be exposed sequentially. For example, the reticle or mask can include a pattern to be exposed multiple times on the substrate. When exposing a layer on the substrate, the reticle can be moved relative to the substrate so as to expose different areas on the substrate in sequence. As discussed above, the reticle can be associated with a first positioner PM for accurately positioning the reticle within the lithographic apparatus LA. The substrate W can be associated with a second positioner PW for accurately positioning the substrate W within the lithographic apparatus LA. The positioners PM and PW can be used to accurately position the substrate W and the reticle relative to each other so as to set the position of the exposed pattern on the substrate. Other settings and factors that may affect the position of the pattern on the substrate may include, for example: the projection system PS used to project the pattern of the reticle onto the substrate W, the substrate, the wafer stage, the properties of WT (e.g., the topography), and the properties of the radiation used to expose the pattern.

[0133] In an exemplary embodiment, the entire apparatus to be photolithographically exposed may be too large to fit on a single reticle. Therefore, the entire apparatus can be divided into two or more separate zones. These zones can be exposed independently of one another (e.g., sequentially). For the entire apparatus to function, the independently exposed zones need to be accurately and precisely connected at or near the boundaries between the zones.

[0134] In order to accurately position multiple sequentially patterned areas relative to each other, precise parameter control may be required. The settings of the different elements of the lithographic apparatus LA can be optimized to obtain accurate positioning of the exposed areas on the substrate. Measurement data of the exposed substrate can be obtained to determine the positioning of the multiple areas. The measurement data can be used to check whether the exposed substrate has acceptable positioning of the exposed areas, for example, for quality control. The measurements can also be used to determine how to improve the settings for future exposures performed by the lithographic apparatus LA. For example, positioning errors can be determined for multiple areas. The determined positioning errors can indicate that there is an error in the alignment of two adjacent areas in the x-direction. The errors can be analyzed to determine one or more causes of the errors. One or more equipment or option settings can be updated to resolve the errors to avoid errors in future exposures.

[0135] The positioning of sequentially exposed regions relative to each other can be discussed with respect to stitching errors. The performance of a photolithographic patterning process can include one or more stitching errors. A stitching error can be an error in the desired position of an exposed region. Stitching can refer to the connection or relative placement of two regions. A region can be an adjacent region. A region can include features that are associated with each other. For example, a region can belong to the same device exposed on substrate W. Photolithographic exposure can expose a pattern to a two-dimensional region. A region can be rectangular. For example, a region can be square. However, a region can have any two-dimensional shape in the plane of the substrate. Along the edge of a region, there may be a boundary that contacts an adjacent region. In the case of a rectangular region, the direction along which the edge of the region is positioned can be referred to as the x-direction and the y-direction. The direction of the edge can also be referred to as the horizontal direction and the vertical direction.

[0136] As described above, measurement data can be used to control the placement of exposed areas within a plane on the substrate. The measurement data can, for example, be used to determine and / or analyze stitching errors between areas on the substrate W. The measurement data can be obtained based on a measurement target. The measurement target can, for example, be an overlay accuracy measurement target. One or more measurement targets can be positioned on the substrate as part of a pattern design exposed on the substrate. The measurement target can be exposed as part of a lithographic exposure. Structures included in the target (e.g., a diffraction grating) can be analyzed to determine the properties of the exposed pattern. Analysis of the measurement target(s) can include measurements for determining the position of one or more measurement targets relative to one or more other measurement targets on the substrate. The measurements can include, for example, overlay accuracy and / or alignment measurements. The measurement target(s) and the other measurement targets(s) can be positioned in different areas on the substrate. Including measurement targets increases costs by taking up space on the substrate W because including measurement targets reduces the space available for exposing product features. On the other hand, including fewer measurement targets on the substrate can result in less measurement data being available. This in turn may result in a reduction in the quality of the analysis and / or control of the exposed pattern. Another potential disadvantage of using metrology targets to determine the in-plane placement of regions is that the measurement data may not represent the actual stitching error of the exposed features. The metrology target measurements may, for example, be designed and / or the patterns established in different ways, which means that they behave differently. For example, the response of the pattern to aberrations and / or process effects of the exposure process may be different. The limited availability of measurement data and the potential deviation between the stitching error data and the actual stitching error may present disadvantages for using metrology targets for in-plane positioning control. Described herein are methods and apparatus for overcoming at least some of these challenges.

[0137] Figure 4A flowchart depicts steps in a method for determining the performance of a photolithographic patterning process. The performance may relate to the stitching of adjacent exposed regions on a substrate. In step 400, at least one image of a portion of a substrate may be received. The portion of the substrate may include a first region comprising a first feature associated with a first photolithographic exposure performed on the substrate at a first time. The portion of the substrate may also include a second region comprising a second feature associated with a second photolithographic exposure performed on the substrate at a second time. The first region and the second region may each include a portion that does not overlap with the other of the first region and the second region. In the next step 402, one or more characteristic features of the exposed first feature and / or the exposed second feature may be obtained. The exposed first feature and / or the exposed second feature may be associated with a boundary between the first region and the second region. The exposed first feature and / or the exposed second feature may, for example, be located at the boundary between the first region and the second region. In step 404, the performance of the photolithographic patterning process may be determined based on the characteristic features. The methods described above, as well as other methods described herein, may be performed by a device including one or more processors configured to perform the steps of the methods described herein.

[0138] Figure 5 A schematic diagram depicts an image of a portion 500 of a substrate including a first region 502 and a second region 512. The first region 502 and the second region 512 may be separated along a boundary 520. The boundary 520 may include an outer edge of the first region 502 and an outer edge of the second region 512.

[0139] First region 502 may include first feature 504 associated with a first photolithographic exposure. Second region 512 may include second feature 514 associated with a second photolithographic exposure. First region 502 and second region 512 may be a first exposure field and a second exposure field of a photolithographic exposure process. Boundary 520 may include all or a portion of an edge of the first field and all or a portion of an edge of the second field. The first photolithographic exposure and the second photolithographic exposure may be performed sequentially at a first time and a second time. Other exposures may be performed between the first exposure and the second exposure. For example, one or more other regions may be photolithographically exposed between the first exposure and the second exposure of the first and second regions.

[0140] The first zone 502 and the second zone 512 may be adjacent zones. The intended design of the first zone 502 and the second zone 512 may not overlap nominally. For example, a portion of the outer edge of the first zone 502 may abut a portion of the outer edge of the second zone 512. However, in practice, for example due to errors in patterned exposure (such as stitching errors), the first zone 502 and the second zone 512 may partially overlap. In other embodiments, the first zone 502 and the second zone 512 may have a partial overlap on the substrate. The first zone 502 and the second zone 512 may have substantially the same size and / or shape. The shape of the zone may be rectangular. The shape of the zone may be, for example, square. The zone may correspond to an exposure field on the substrate. One or more dimensions of the zone may be in the range of 10 mm to 35 mm. For example, the zone may correspond to an exposure field having a size of 26 mm × 33 mm or 23 mm × 23 mm.

[0141] Determining the performance of the photolithographic patterning process may include determining the quality of the patterning process. The quality may relate to how different zones that are to be exposed independently of one another are positioned relative to one another. Determining the performance may include determining a stitching error between the first exposure field 502 and the second exposure field 512. Determining the performance may include determining one or more properties of the exposed pattern, where the properties may be referred to as process characteristics. Determining the performance may include determining one or more corrections to the patterning process. The corrections may be based on the determined process characteristics and / or performance of the photolithographic patterning process. The determined corrections may be used to update the photolithographic patterning process for future iterations. Determining the performance of the photolithographic patterning process may also include validating the patterning process.

[0142] The image may be a scanning electron microscope image (SEM). The image may be a voltage contrast image. The voltage contrast image may provide a measurement of the electrical contact of the feature with the underlying layer. The image may be obtained after the exposed substrate has been processed (e.g., after performing one or more post-exposure development steps on a patterned substrate). Measurement of the contact of the underlying layer may provide an indication of how well the features of the exposed layer match the features of the underlying layer. This, in turn, may be used to determine whether there are stitching errors. The image may be obtained while the substrate is in the lithography cell LC. The image may be of a patterned layer of photoresist on the substrate. The image may be of a layer of material that has been patterned by an etching process.

[0143] First feature 504 and / or second feature 514 may be product features. In other words, the method may utilize properties of features exposed on the substrate that are not related to the measurement target. Features 504 and 514 may be related to the product structure to be exposed and patterned onto the substrate. For example, the substrate may be patterned by one or more devices. First feature 504 and second feature 514 may form part of the same device patterned on the substrate. First feature 504 and second feature 514 may be located in an area with a common boundary. The common boundary may include some or all of boundary 520 between first region 502 and second region 512. An advantage of this method may be that process performance is determined based on analysis of the product features themselves (as opposed to, for example, measurement target features). Another advantage may be that measurement targets may not be required or may be required in smaller quantities, which can free up space on the substrate for product features. Since more product features may be present on the substrate than measurement targets, using images of product features for analysis can allow for more intensive measurements. This can result in a more detailed analysis of performance, which can lead to improved accuracy. Because the analysis is not limited to areas of the substrate where metrology targets are present, the distribution and / or density of measurements can be customized across the substrate. For example, areas of the exposed pattern where stitching is important for performance, or where historical exposures have experienced stitching errors, can be measured more densely than other areas.

[0144] First feature 504 and / or second feature 514 may be a pseudo feature. The pseudo feature may be exposed on the substrate to have properties and / or dimensions similar to those of the product feature. In this regard, one or more properties and / or dimensions of the pseudo feature may be the same or substantially the same as the corresponding properties and / or features of one or more product features. This can result in analysis of the pseudo feature providing similar results as analysis of the product feature. For example, the pseudo feature may have a size and / or shape similar to that of the product feature. In some cases, the shape of the pseudo feature may be configured so as to obtain characteristic properties that are suitable for analysis to determine performance. For example, the pseudo feature may include multiple different features that may be similar to the product feature across the substrate, so that multiple features can be found within a single image. The properties of the pseudo feature may be designed to increase the feature's sensitivity to stitching errors. For example, the shape, size, position, or dose of the pseudo feature may be configured so that the shape, size, position, or dose of the pseudo feature is sensitive to variations in stitching.

[0145] First feature 504 and second feature 514 may be a first product feature and a second product feature, respectively. In some cases, first feature 504 and second feature 514 may form part of different product features. In other cases, first feature 504 and second feature 514 may nominally comprise a single feature extending along first and second zones. In other words, first feature 504 and second feature 514 may comprise portions of the same product feature extending across the boundary between first zone 502 and second zone 512. An image may include a combination of first and second features comprising individual product features, as well as a combination of first and second features comprising a single product feature.

[0146] Determining the performance of the lithographic patterning process may include performing an analysis of the image to determine one or more feature characteristics. The analysis of the image may be used to determine the feature characteristics of the first feature 504 and / or the second feature 514. The first feature and / or the second feature may be associated with a boundary included in the image. In this context, "associated with" may mean that the feature is positioned at or near the boundary between the first zone and the second zone (near the boundary). The feature characteristics may include visual properties of the first feature and / or the second feature in the image. The feature characteristics may include the spatial dimensions of the first feature and / or the second feature. The feature characteristics may include a distance indicator, which may be between the first feature 504 and the second feature 514. The distance indicator may, for example, include the distance between one or more axes of symmetry of the first feature 504 and one or more axes of symmetry of the second feature 514. In the case where the first feature and the second feature do not constitute a single feature extending along the first zone 502 and the second zone 512, the distance indicator may include the physical distance between the first feature and the second feature.

[0147] Figure 6Schematic diagram depicting several exemplary feature characteristics. The feature characteristics in the first area 502 and the second area 512 include a single exemplary feature consisting of two parallel lines spanning the boundary 520 between the first area 502 and the second area 512. The intended feature design to be exposed on the substrate can be referred to as a design standard. Determining the feature characteristics can involve comparing one or more spatial dimensions and / or other visual properties of the feature exposed on the substrate with the design standard. Feature 600 can represent a feature according to the design standard of the two parallel lines. Feature 600 does not include stitching errors. The single feature spanning the boundary 520 can include a local thickening or narrowing at or near the boundary between the first area 502 and the second area 512. In feature 602, the parallel lines are interrupted at the boundary 520 so that they do not intersect to form a solid line. Although feature 602 shows a complete interruption of the parallel lines, in some cases, the lines can alternatively experience a local narrowing around the boundary area 520. In feature 604, the parallel lines are wider (or thicker) around the boundary area 520. In feature 606, the lines in second region 512 are displaced or offset in a direction parallel to boundary 520 compared to the lines in first region 502. If the stitching of regions includes errors, the performance of the resulting device may be reduced. For example, first feature 504 and second feature 514 can be designed to contact each other across boundary 520 to allow current to flow therebetween. However, due to stitching errors, there may be reduced contact or no contact between first feature 504 and second feature 514. This can result in reduced connection or no connection, thereby inhibiting current flow. In some cases, stitching errors may cause regions to have partial overlap, which may increase the size of the exposed features. This may result in overlapping features that should not be touching, which may, for example, cause a short circuit.

[0148] Determining the performance of the lithographic patterning process may include determining the performance of one or more lithographic patterning process characteristics (also referred to as process characteristics). The determined characteristic characteristics may be used to determine the one or more process characteristics. Exemplary process characteristics include translation in the x and / or y directions, magnification, focus, dose, etc. in the first region 502 and / or the second region 512. Exemplary process characteristics may also include one or more higher order deformation errors associated with patterning the first region 502 and / or the second region 512. Figure 6 In feature 604, a narrowing and / or interruption of the parallel lines at boundary 520 may indicate that the magnification of the exposed features in first region 502 and / or second region 512 is too small. In feature 604, a localized thickening of the lines at boundary 520 may indicate that the magnification of the exposed features in first region 502 and / or second region 512 is too large. Feature 604 may indicate a translation error of first region 502 relative to second region 512 along a dimension parallel to boundary 520.

[0149] A combination of analyses of multiple characteristic features can be used to determine process characteristics. This can include, for example, analyzing characteristic features of differently shaped features in first region 502 and / or second region 512. Example features include straight lines, points, features of larger areas, and the like. The line can be perpendicular to boundary 520, or the line can be positioned at a non-perpendicular angle to boundary 520. The combination of differently shaped features can be obtained, for example, by exposing and imaging dummy features on the substrate.

[0150] Determining process characteristics can also be performed based on characteristic characteristics obtained from multiple images. For example, to determine the quality of the stitching around a region, images of different portions along the border can be used. Different images can provide borders of different in-plane dimensions along the substrate. For example, a first image can be arranged to include a first border between a first region and a second region. A second image can be arranged to include a second border having a different direction than the first border. The second border can be between the first region and another region. The other region can be a second region (same for the first border) or a third region associated with a third lithographic exposure on the substrate at a third time (the third lithographic exposure being separated from the first exposure and the second exposure).

[0151] Multiple images may be received for determining the performance of a patterning process. The first image and the second image may include a boundary in a first direction and a second direction, wherein the first direction and the second direction are not parallel. The first direction and the second direction may be perpendicular. The first image and the second image may each include a boundary that includes a portion of the outer edge of the first region 502. For example, in the case of a rectangular region, the first image may include a portion of the boundary in the x-direction, and the second image may include a portion of the boundary in the y-direction. The x-direction and the y-direction may form the plane of the substrate.

[0152] Figure 7A schematic diagram depicts a portion of a substrate including regions 502, 512, 522, and 532. Each of regions 502, 512, 522, and 532 can be photolithographically exposed at a different time. Each of regions 502, 512, 522, and 532 can be a separate exposure field. A first image 702 can be provided, including the boundary between region 502 and region 512. The boundary of image 702 can extend in the y-direction. A second image 704 can be provided, including the boundary between region 502 and region 522. The boundary of the second image can extend in the x-direction. One or more process characteristics can be determined for each of first image 702 and second image 704. The process characteristics of first image 702 and second image 704 can then be combined to determine the performance of a patterning process. As with two images, process characteristics determined from three or more images can be combined to determine the performance of a patterning process. Alternatively or additionally, an image 706 of a corner portion of region 502 can be provided. This image can include a portion of the boundary in both the x-direction and the y-direction.

[0153] Methods for analyzing and determining the performance of a lithographic patterning process can be performed for multiple layers on the same substrate. Images of the substrate can be acquired between subsequent exposure steps. Performance can be analyzed after each exposure, including regions on a tiled substrate. An advantage of the methods described herein is that they enable measurement of the substrate, for example, by obtaining SEM images. This can increase the speed of the process for determining the performance of a lithographic patterning process compared to diffraction-based measurements (which are slower to obtain than SEM measurements).

[0154] The determined process characteristics can be used to determine the performance of the lithographic patterning process. The performance can include, for example, an assessment of the quality of the process, verification of characteristics exposed by the process, and / or a determined stitching error of the exposure. The method can also determine one or more corrections to the lithographic patterning process. The one or more corrections can be used to update the lithographic patterning process for future iterations. Updating the patterning process can include updating at least one or more exposure settings of the lithographic apparatus LA and the reticle design.

[0155] The method can receive multiple images dispersed across different locations across a substrate for use in determining the performance of a photolithographic patterning process. Determining the performance of the photolithographic patterning process can include determining the overall quality of the exposure and / or can include local assessments of the quality. Process characteristics can be determined for each of the images, and the process characteristics can be combined to determine the overall quality of the photolithographic patterning process. In other embodiments, one or more images located closely together on the substrate can be combined to determine a single local determination of the process performance at that location on the substrate. Because stitching errors can vary across the substrate, feature characteristics and process characteristics can be determined at different locations on the substrate. This can be used to determine stitching errors at different locations across the substrate. This can allow the performance of the photolithographic patterning process to be determined across the entire substrate. If performance is measured on product features, the method can provide flexibility in the number of images analyzed to determine process performance. The density of the measurements can be set depending on the requirements of the performance analysis (e.g., the required precision and accuracy of the product features exposed on the substrate). The method can determine a dense or sparse map of performance across the entire substrate. The substrate can include approximately 100 regions. The method can determine the performance of several zones between five zones and all zones on the substrate. The measurements can be evenly distributed between zones across the substrate. For example, if there are four measurement sites (e.g., 4 measurement targets) in each zone, each of the measurement sites can be used for 25% of the zones in the plurality of zones. For each zone, multiple images can be obtained for determining stitching errors. The images can be related to the same boundary having the same first zone 502 and second zone 512, or to different boundaries between the first zone 502 and the second zone 512, the third zone 522, the fourth zone 523, and the like. The method can use 4 to 20 images at each zone. In some cases, the method can use more than 20 images.

[0156] The same photolithography patterning exposure can be performed on multiple substrates over a period of time. The amount and location of images to be analyzed for determining the performance of the patterning process can change over time. At the beginning of a new exposure pattern, a more intensive performance map can be prepared because the new process may initially require more corrections. Once the process settings have been corrected one or more times, the performance can be improved and / or stabilized. In response to this, the amount of images analyzed for determining process performance can be reduced. The method can also be flexible with respect to the intensity of the performance analysis performed on the entire substrate. The method can determine one or more areas of interest for performance analysis. For example, when the same exposure is performed on another substrate, the determined area with poor performance can be analyzed in more detail. As another example, the substrate may include critical areas in which product features may have more stringent manufacturing requirements (i.e., lower tolerance for deviations from design standards). These critical areas can receive more intensive performance monitoring. This may achieve improved performance of the patterning process at critical areas.

[0157] The method for determining the performance of a photolithography patterning process may be determined in whole or in part using a model. The model may include vision technology, such as machine vision technology. The model may be a machine learning model. The model may be used to determine one or more process characteristics. In an exemplary embodiment, the model may receive one or more feature characteristics as input. In another exemplary embodiment, the model may receive one or more received images of the first region, the second region, and the boundary 520 as input. The method may use multiple models. For example, the method may use two separate models. The first model may be a vision technology model. The vision technology model may be used to interpret the one or more images provided as input to the model. The model that receives the one or more images as input may be a convolutional neural network. The first model may provide one or more process characteristics as output. The second model may receive one or more process characteristics determined by the first model. The second model may receive process characteristics of multiple regions on a substrate. The second model may interpret the received process characteristics to convert them into patterning corrections. The second model may provide correction data as output for adjusting the photolithography patterning process, such as for correcting stitching errors. For example, the correction data may include one or more updated values ​​for photolithography patterning process settings. The model may include a classification model. The classification model can be used, for example, for validation of the patterning process. For example, the model can classify an image as having a region stitching property that falls within one or more set exposure tolerances (pass) or falls outside the one or more set exposure tolerances (failure).

[0158] As described herein, the method can use one or more images to determine characteristic properties of the patterns depicted in these images. The characteristic properties (e.g., overlay accuracy, alignment, or other properties indicative of stitching quality) can be determined directly from the analysis of the images. In order to detect smaller changes or variations in the characteristic properties, it may be advantageous to enhance the quality of the image(s) before analyzing the image(s) used to determine the characteristic properties. Enhancing the image can, for example, include removing noise, filtering out unwanted signals, and / or extracting relevant features for analysis. Advantages of extracting relevant features can include reducing the size of the analysis. As described herein, determining one or more characteristic properties from an image can include preprocessing the image, extracting features from the preprocessed image, and / or determining some or all of the steps of an indicator for stitching quality based on the preprocessed image.

[0159] Characteristic properties may include overlay accuracy. It may be desirable to separate the analysis of overlay accuracy into separate dimensions on the substrate, such as two dimensions in the plane of the patterned substrate. The dimensions may be perpendicular to one another and may be referred to as the x- and y-directions, or the horizontal and vertical directions. These directions may be parallel and / or perpendicular to the direction of the boundary being analyzed.

[0160] Preprocessing of an image may include steps for removing noise from the image. The preprocessing may preserve structural information related to patterns present in the image. Specifically, the preprocessing may be configured to preserve information related to edges and / or stripes present in the image. Edges, stripes, or other edges on an image may also be referred to as line features. Preprocessing of the image may include determining intensity information and / or gradient information of the image. The intensity and / or gradient information may be used to determine segmentation of the image. Segmentation may be able to locate at least some of the edges and / or stripes, thereby removing background and / or noise from the image.

[0161] Figure 9An example of steps in a method for preprocessing an image for determining the performance of a photolithographic patterning process is depicted. In step 900, the image may be upsampled. Upsampling may include interpolation, such as bicubic interpolation. Those skilled in the art will appreciate that any suitable upsampling method may be used. In step 902, the upsampled image may be processed to suppress and reduce noise present in the image. In step 904, the gradient magnitude (also referred to as gradient) of the image processed up to that point may be obtained. The gradient may provide the advantage of highlighting edges present in the image. In step 906, the image processed in steps 902 and / or 904 may be processed to form a binary image. In this context, a binary image may be an image in which data has been compressed to be represented in binary form (i.e., as one of two possible values). A binary image may be an image in which each pixel has one or two possible values, such as 0 or 1, black or white, yes or no, etc. This may be represented as a black and white image, or an image having any other combination of two different colors. In step 908, the binary image may be processed to be cleaned. Cleaning the binary image may include filling holes in the binary image and / or removing islands of pixels, for example, by deleting islands of pixels or connecting islands of pixels to other regions. Cleaning the binary image may, for example, use region growing techniques and / or connected component techniques. In step 910, the pre-processing may perform a rotation of the processed image. The rotation may be determined so that the edges and / or strips in the processed image are parallel and / or perpendicular to the boundary between the first region and the second region in the image. In some cases, multiple binary images may be formed. For example, a first binary image may be formed from an upsampled image, and a second binary image may be formed from the gradient magnitude of the image. Processing steps 908 to 910 may be performed on the two binary images. The processed first binary image and the second binary image may be analyzed in parallel. An advantage of this parallel analysis may be that the parallel analysis achieves a more constant and robust determination of the overlay accuracy than if the image analysis itself were performed. Figure 9 As shown in , the flowchart is split into a first arm representing the formation of a binary image of the upsampled image and a second arm representing the formation of a binary image of the gradient magnitude of the image. In the method disclosed herein, at least one of the arms can be performed as part of pre-processing of the image.

[0162] The noise reduction technique of step 902 can be edge-preserving. The noise reduction technique can, for example, include one or more of bilateral filtering, anisotropic diffusion filtering, and / or an unsupervised wavelet transform. If the amount of noise in the image is not sufficiently reduced, the steps performed to detect and identify line features can, for example, include errors due to noise being mistakenly identified as lines. To improve noise reduction, those skilled in the art can use an autoencoder model. The autoencoder model can be a denoising autoencoder. The autoencoder model can include a machine learning model. Specifically, the autoencoder model can include a convolutional neural network (CNN) and / or a generative adversarial network (GAN). The GAN can include a generator network that produces a noise-free image and a discriminator network that classifies the output of the generator network. The autoencoder can be trained to produce noise-free or reduced-noise images without generating image artifacts. The autoencoder can be trained on pairs of noisy / noise-free images. The training pairs can be obtained using simulation (e.g., by adding different types of noise).

[0163] A binary image can be formed by segmenting the processed image and / or the gradient magnitude determined for the processed image in step 904. Methods that can be used to determine the binary image can include threshold methods, such as global threshold methods. Global threshold methods can, for example, include Otsu image processing methods. Methods for determining the binary image can alternatively or additionally include machine learning methods. Exemplary machine learning methods for forming the binary image can, for example, include clustering. Clustering algorithms can use a mixture of Gaussian components. Machine learning algorithms can, for example, use the processed image and the gradient of the processed image as input.

[0164] In step 910, the pre-processed image can be analyzed to determine whether the edges / line features in the image have a rotation relative to the edge of the image. If the image includes edges and / or strips at multiple different angles, a primary direction can be determined and a rotation can be performed to align the primary direction. The primary direction can be, for example, the direction of the line features that most frequently appear in the image. If the direction of the line features of the image is not perpendicular / parallel to the boundary between the first and second regions in the image, or if the image has another undesirable rotation, the method can determine the rotation to be applied. The method can then apply the rotation to the image. In order to detect the direction of the edge, a line detection technique can be used to detect the line and determine its angle. The line detection technique can, for example, include a Hough transform to detect the line feature and its angle in the image. The method can then use an image rotation algorithm to rotate the image by the amount determined by the line detection technique.

[0165] Image preprocessing can include Figure 9If a plurality of images are used to determine the performance of the lithographic patterning process, pre-processing may be performed on at least one of the plurality of images. Pre-processing may be performed on each of the plurality of images used to determine the performance of the lithographic patterning process.

[0166] The performance of the lithographic patterning process can be determined based on one or more characteristic features. These characteristic features can be identified from an image. One or more features can be extracted from an image and / or from a pre-processed image. Features can be used to determine the stitching quality at the boundary between the first and second areas shown in the image. The stitching quality can be evaluated, for example, in view of the overlay accuracy OVL between the first and second areas at the boundary. The stitching quality in two directions in the plane of the substrate (for example, the x-direction and the y-direction mentioned above) can be determined. The features patterned on the substrate may include line features parallel to one of the two directions and perpendicular to the other of the two directions. Alternatively or in addition, the features may include line features at angles that are not parallel / perpendicular to the directions. The angle may be any angle from 0 degrees to 90 degrees.

[0167] A feature extraction algorithm can be used to extract one or more features from the preprocessed image. The feature algorithm can use a Fourier transform on the preprocessed binary image. If the binary image includes line features parallel to the X direction, the Fourier transform can be applied to the portion of the image parallel to the Y direction. Similarly, if the binary image includes line features parallel to the Y direction, the Fourier transform can be applied to the portion of the image parallel to the X direction. The portion can be a pixel row of the image. The Fourier transform can be a fast Fourier transform (FFT). The pixel rows (along which the Fourier transform can be applied) can be aligned with the direction of the boundary in the image. The pixel rows can be parallel to the boundary, or can be substantially parallel to the boundary. In some cases, for example, if the boundary between the first zone and the second zone is misaligned due to a stitching error, the boundary may not be completely parallel to the pixel rows. As part of the analysis of the difference between the pixel rows, this misalignment can become apparent.

[0168] Figure 10 shows a chart of signal patterns on pixel rows, where the Pr axis represents the pixels in the row. The axis marked B indicates the binary value in the preprocessed binary image input for the row. The pixel row can be substantially perpendicular to the line feature in the image. Figure 10 (a) represents the signal of the pixel row away from the boundary between the first zone and the second zone. The row is sufficiently far away from the boundary between the first zone and the second zone so that there is no splicing effect in the row. Figure 10 (b) represents the signal of the pixel row on or near the boundary between the first zone and the second zone. As can be seen from the figure, the position of the edge / line feature between Figure 10 (a) and Figure 10 (b) is different. This can indicate that there is a splicing effect in the pixel row at the boundary / near the boundary. Although the method described herein mentions pixel rows, the same method can be applied to pixel columns, where the boundary between the first zone and the second zone is not parallel (e.g., perpendicular) to the image column.

[0169] A Fourier transform can be performed on each pixel row. Based on the Fourier transform, one or more of the duty cycle, frequency amplitude, and phase component of each row can be determined. The duty cycle can indicate the position of the line feature in the binary image. Since the line feature can represent the edge of a strip that crosses the boundary, the position of the line can indicate the width of the strip at the pixel row. The width can be expressed as a duty cycle. The change in the duty cycle between different rows can indicate the difference in the width of the line feature along the direction parallel to the line feature. The determined change in the duty cycle around the portion of the boundary between the first zone and the second zone can be used to detect the splicing portion. The duty cycle change can also be used to determine the quality of the splicing between the first zone and the second zone. The period of the signal in the rows at and around the boundary can also be used to detect and evaluate the quality of the splicing portion.

[0170] The change in phase between different lines can be used as an indicator of stitching error in the direction perpendicular to the line features.The change in duty cycle can be used as an indicator of stitching error in the direction parallel to the line features.

[0171] Alternatively or additionally, to perform a Fourier transform of the binary image, a Fourier transform can be performed on a preprocessed image representing the gradients detected in the image. Combining binary image analysis with gradient image analysis can improve the accuracy of determining the characteristic. Using both binary image analysis and gradient image analysis can further enable error detection and / or consistency checking of the characteristic determination.

[0172] In a first example, an image comprises a plurality of vertical strips extending from a first zone to a second zone across a horizontal boundary. Using a Fourier transform, the duty cycle and phase of a plurality of horizontal rows of pixels of the preprocessed image can be determined. The determined phase difference between the rows can then be used to determine the overlay accuracy or other stitching quality indicator in the horizontal direction. The change in the duty cycle between the rows can be used to determine the overlay accuracy or other stitching quality indicator in the vertical direction. In an exemplary embodiment, the maximum phase change between the pixel rows in the first zone (above the horizontal boundary) and the pixel rows in the second zone (below the horizontal boundary) can be used as an indication of the stitching quality in the horizontal direction. The maximum duty cycle change between the duty cycle of the rows in the area close to the boundary and the rows in the area far from the boundary (in the area of ​​the image not affected by the stitching effect) can be used as an indication of the stitching quality in the vertical direction.

[0173] The differences in duty cycle and / or phase can be averaged over several values, which can improve the signal-to-noise ratio of the determined differences. The duty cycle and / or phase measurements can be used for further analysis of the image, such as for performing noise qualification. For example, the location of phase changes between different lines and the stability of the phase can be used as indicators of the degree of confidence in the determined stitching quality. Large changes in phase of line features that are expected to be vertical (especially in areas far from the boundaries) can indicate a lower degree of confidence.

[0174] Stitching quality data can be collected at several discrete areas and / or larger areas along the same boundary, for example, using multiple images. The stitching quality data can all relate to the same stitching performed between a first zone and a second zone. The stitching quality determined at different points along the stitching can be used to determine an average stitching quality for the stitching. The stitching quality data can also relate to multiple different stitchings. The stitching quality determined across different stitchings can be analyzed to identify trends. The analysis can include statistical analysis, such as determining a moving average trend. The stitching quality can be analyzed to qualify local edge placement errors. The stitching quality can be analyzed to qualify overall stitching performance. The overall stitching performance can be used, for example, to determine the performance of a lithographic patterning process over a larger area of ​​a substrate and / or to correct the lithographic patterning process.

[0175] The determined duty cycle difference and / or phase difference may not provide a direct measure of the overlay accuracy in the vertical and / or horizontal directions. Other data processing steps may be required to determine the overlay accuracy based on the duty cycle and / or phase difference. If the line features are not connected at the boundary, there may be pixel rows of the binary image that do not include the line feature values ​​near the boundary. Such rows can be referred to as zero duty cycle rows. The number of zero duty cycle rows can provide an indication of the overlay accuracy in the direction perpendicular to the boundary. The position and / or shape of the changes in duty cycle and / or phase around the boundary region can be used to determine the properties of the splicing quality.

[0176] The determined duty cycle difference and phase difference can be labeled values. For example, for a parallel strip extending across a first region and a second region at a boundary, if the first and second regions are pressed closer together than designed, the duty cycle around the boundary can increase. If the first and second regions are positioned further apart than designed, the duty cycle around the boundary can decrease. The label of the determined duty cycle difference can indicate which situation the difference represents.

[0177] As described above, one or more images comprising a plurality of line features (e.g., a pre-processed binary image of a periodic set of strips) can be used to determine overlay accuracy in directions parallel to and perpendicular to the boundary forming the stitching area between the first and second regions. Overlay accuracy can be determined based on the difference in duty cycle and / or phase between pixel rows. To determine overlay accuracy in directions parallel to the boundary, the determined phase difference can be a direct indicator. An overlay accuracy value can be determined based on the determined phase difference.

[0178] For determining the overlay accuracy in the direction perpendicular to the boundary, additional data processing may be required to determine the overlay accuracy on top of the determined duty cycle difference. After the duty cycle difference, the location and shape of the duty cycle change / difference can be used to determine the overlay accuracy. Material properties can also affect the way in which changes in overlay accuracy affect the duty cycle, so information about the materials used on the patterned substrate can also be used to determine the overlay accuracy. In order to determine the overlay accuracy in the direction perpendicular to the boundary, a model can be used. Alternatively or additionally, a lookup table can be provided to determine the overlay accuracy based on the duty cycle difference.

[0179] A lookup table can be provided that relates duty cycle differences to overlay accuracy values. Other information that can be provided to use the lookup table includes, for example, one or more of the following: the location of the change in duty cycle between pixel rows, the shape of the change in duty cycle between pixel rows, the maximum phase difference, and / or the number of rows with zero duty cycle, which can also be provided. The lookup table can be constructed using test measurements during the setup phase.

[0180] In some cases, the relationship between one or more characteristic features and the overlay accuracy may be nonlinear. In order to qualify this nonlinear relationship, an accurate resolution of the overlay accuracy may be required. In order to provide an accurate resolution of the overlay accuracy, a model may be used. The model may be a machine learning model, such as a neural network. The neural network may be trained during a setup phase to learn the relationship between the overlay accuracy in a direction perpendicular to the boundary and the difference and change in the duty cycle across the pixel rows parallel to the boundary. Alternatively or additionally, a nonlinear regression method may be used for the neural network. Since the features relevant to determining the overlay accuracy have been determined, although preprocessing and / or Fourier transforms are used to determine the phase difference and duty cycle difference, it may not be necessary to provide a large-scale neural network trained on the image itself. Alternatively, a small-scale neural network or other nonlinear regression method may be trained based on relevant data. The relevant data may include one or more of the duty cycle difference, shape, and position, phase difference, and number of zero duty cycle rows.

[0181] In an exemplary embodiment, an image is provided for determining the performance of a photolithographic patterning process. The image is preprocessed, wherein the preprocessing may include a noise reduction step using an autoencoder and a segmentation step for determining a binary and / or gradient version of the image. The binary preprocessed image and / or the binary gradient of the preprocessed image may include line features. The line features may be analyzed to determine the stitching quality. The stitching quality may, for example, include overlay accuracy in directions parallel to and perpendicular to the boundary. A Fourier transform may be performed on pixel rows of the binary image to determine the duty cycle and phase of the line features. The overlay accuracy in the direction parallel to the boundary may be determined directly from the Fourier transform data. To determine the overlay accuracy in the direction perpendicular to the boundary, a model or lookup table may be provided. The model and / or lookup table may be configured to receive input data related to the Fourier transform data and output the overlay accuracy in the direction perpendicular to the boundary. The analysis and processing of the image as described above may be applied to a pattern comprising a plurality of parallel straight structures, which may be perpendicular to the boundary between a first region and a second region. However, the methods and systems described herein may also be applied to other patterns. The properties of the pattern may be taken into account when analyzing and / or interpreting the Fourier transform of the (pre-processed) image. Information about the properties of the pattern may, for example, be used to train a model and / or construct a lookup table to relate duty cycle to overlay accuracy.

[0182] In an exemplary embodiment, the strips connecting the borders between the first and second regions may have different critical dimensions. Information about such differences in design may be provided to the system for use in determining stitching quality. For example, a vector may be provided that includes expected duty cycle values ​​for each row of pixels. The difference between the expected duty cycle and the observed duty cycle may be considered when determining stitching quality. The vector may be provided, for example, to a model or regression method.

[0183] As described above, the quality of the stitching at the boundary between the first zone and the second zone can be determined. This can be in the form of overlay accuracy in the x-direction and the y-direction. Additionally or alternatively, metrics other than overlay accuracy can be defined to evaluate the stitching quality. Such metrics can, for example, take into account the smoothness, flatness, and / or symmetry of the stitching. The metrics can be determined based on one or more of the image, the pre-processed image, or other data associated with the image. The metrics can be determined based on multiples of any of the above.

[0184] In an exemplary embodiment, a metric can be determined based on a binary image. The binary image can be a segmented binary image in which binary contrast is used to indicate edges and boundaries within the image. The binary image can be filtered to reduce noise from the image. A method can be provided for analyzing the binary image to determine a metric for evaluating stitching quality in the image. Figure 11 A flowchart depicting steps in a method for determining an indicator indicating the quality of a splice. In step 1100, a binary image including a splice between a first region and a second region is analyzed to find inflection points before and after the splice. The inflection points can be considered to occur when a large change in the duty cycle around the splice begins. The inflection points can be determined on both sides of the spliced ​​area, in other words, in both the first region and the second region. In step 1102, the area between the inflection points can be copied for separate storage. The binary pixels indicating the edge can be stored as a curve. The horizontal axis of the curve can be the pixel rows along the strip extending across the border. The vertical axis of the curve can represent the duty cycle of the corresponding pixel rows. A function representing the mathematical expression of the curve can be determined.

[0185] The curve determined in step 1102 can be considered to represent the joint between the first region and the second region. In step 1104, the curve can be used to calculate an index indicating the flatness of the joint. To calculate this index, the curve can be considered as a probability distribution function. Flatness index M flat It can be calculated as the fourth-order statistical moment of the function representing the curve. flat The formula can be calculated as follows:

[0186]

[0187] In the above formula, f(x i ) can represent the pixel position x i The pixel rows of the curve can extend in the range of -n to n on the horizontal axis. M and It can be determined as follows:

[0188]

[0189]

[0190] For the above flatness index, a value equal to 0 indicates a flat splice. Flatness index M flat The metrics may be provided as input to a model, for example, to determine the overlay accuracy of the stitched region, as discussed above.

[0191] In step 1106, the curve determined in step 1102 can be used to calculate an indicator indicating the skewness / symmetry of the curve around the center of the splice. The center of the splice can be the location where the splice is designed to be located, in other words, the designed boundary between the first zone and the second zone. As mentioned above, the curve can be viewed as a probability distribution function. The skewness indicator M skew It can be calculated as the third-order statistical moment of the function representing the curve. skew The formula can be calculated as follows:

[0192]

[0193] Among them, M and As defined above. skew The values ​​are labeled, where the label can indicate whether the curve is skewed towards the first or second region. For the skewness indicator formula defined above, a symmetrical splice with no skew would have M skew =0 skewness index value.

[0194] In some cases, one or more images may be obtained by entities that are separately controlled according to the methods described herein. In other cases, the method may include controlling a measurement tool MT to obtain one or more images of a substrate. The one or more images of the exposed areas on the substrate may be, for example, scanning electron microscope (SEM) images or voltage contrast images. The measurement tool MT may be an electron beam imager. The results of previous performance determinations may be used to guide the measurement device to obtain images on the substrate. As described in more detail above, the results of previous performance determinations may be used to determine which images to obtain, for example, based on determined areas of interest. This previous performance information may guide the acquisition of images at those locations on the substrate, and / or the density of images across the substrate.

[0195] Figure 8 Flowchart depicting steps in an exemplary method for determining the performance of a lithographic patterning process. In step 800, one or more images of a portion of a substrate including a first region 502 and a second region 512 are received, as described above. In step 802, the one or more images can be analyzed to determine one or more characteristic features of products and / or dummy features exposed on the portion of the substrate shown in the image. In step 804, the characteristic features can be analyzed to determine one or more process characteristics of the lithographic patterning process. In step 806, the performance of the lithographic patterning process can be determined based on the determined process characteristics. Determining the performance can include validating the patterning process 808. Determining the performance can also include determining one or more process corrections 810 and updating 812 the lithographic patterning process for future iterations. The method can also include controlling and / or directing 814 a metrology tool MT to obtain images for determining the performance of future iterations of the lithographic patterning process.

[0196] The methods as described herein, in particular methods of analyzing and determining the performance of a lithographic patterning process, may alternatively or additionally be implemented by a machine learning model. The machine learning model may be trained based on a training set of images of a portion of a substrate. The portion of the substrate may include a first region and a second region, the first region including a first feature associated with a first lithographic exposure of the substrate at a first time, and the second region including a second feature associated with a second lithographic exposure of the substrate at a second time. The first feature and / or the second feature may have one or more known characteristic properties associated with a boundary between the first region and the second region. The known characteristic properties may be associated with a known performance of the lithographic patterning process. In this way, the machine learning model may be trained to learn how to analyze and determine the performance of the lithographic patterning process based on images containing known characteristic properties associated with the known performance of the lithographic patterning process.

[0197] Figure 12 A flowchart depicts steps in a method for training a machine learning model for analyzing and determining the performance of a lithographic patterning process. The performance of the lithographic patterning process can include one or more stitching errors, as described herein. The machine learning model can be trained based on a training set of images of a portion of a substrate including a first feature and a second feature, wherein the first feature and the second feature have known characteristic properties associated with known stitching errors. The stitching errors can be associated with specific characteristic properties, such as overlay accuracy. In step 1200, a first lithographic exposure and a second lithographic exposure can be performed on a layer on a substrate. In this step, multiple known characteristic properties associated with known performance can be intentionally introduced. For example, multiple known errors can be intentionally introduced. The known errors can be known stitching errors. A training set of images is thus prepared. In step 1202, a stitching error measurement can be performed. The measurement can be an overlay accuracy measurement against an overlay accuracy target, or can be any known measurement used to determine stitching errors on a substrate. This step allows for the identification of additional stitching errors (i.e., not known stitching errors). Such additional stitching errors can be introduced during the lithographic exposure process as described herein. In step 1204, additional stitching error measurements may be used to update the training set of known stitching errors. In step 1206, the substrate may undergo a lithographic patterning process whose performance is to be determined. In step 1208, at least one image of a portion of the substrate may be received. The at least one image may be, for example, Figure 5 . In step 1210, the machine learning model may undergo a training process. The training set of known stitching errors and the images received in step 1208 may be used to train the machine learning model to learn to identify stitching errors based on the received images. Step 1210 may additionally include a validation process in which the known stitching errors and the validation set of received images are used to validate the machine learning model. In step 1212, a measurement scheme is determined for obtaining an overlay accuracy value from the image data.

[0198] In step 1200, each of a plurality of known stitching errors can be introduced by, for example, applying a translation of the second lithographic exposure relative to the first lithographic exposure (or vice versa). The plurality of known stitching errors can be introduced in more than one dimension. For example, the plurality of known stitching errors can include stitching errors introduced in the x and / or y directions. Figure 13 A schematically printed two-dimensional stitching error matrix is ​​shown that illustrates the effects of overlay accuracy OVL stitching errors introduced in the x-direction and the y-direction. Each entry in the matrix shows the same line feature in the x-direction. The matrix entry with x overlay accuracy 0 and y overlay accuracy 0 shows the feature when no stitching error is introduced (equivalent to Figure 6 600 in FIG). Moving the column to the left or right of the 0,0 term introduces an overlay accuracy stitching error in the negative or positive x direction, respectively. Moving the column above or below the 0,0 term introduces an overlay accuracy stitching error in the negative or positive y direction, respectively. It should be noted that the positioning and structure of the line feature are a combined function of both the x and y overlay accuracy. In other words, the x and y overlay accuracy are coupled. This is in contrast to diffraction-based optical measurements of overlay accuracy (or other characteristic properties) in which the x and y overlay accuracy are typically decoupled. This coupling of overlay accuracy may make it difficult to separate (or decouple) the errors associated with the x and y overlay accuracy when using image analysis techniques. By training a machine learning model on images with stitching errors introduced in both the x and y directions, the model will learn to recognize stitching errors introduced in both the x and y directions without regard to the coupling. Advantageously, multiple lithography exposures can be performed for each value of the stitching error. This increases the size of the training set and thereby improves the performance of the machine learning model. Additionally, by performing multiple exposures for each value, stitching errors that are inadvertently introduced during exposure can be “averaged out.” In other words, the impact of stitching errors introduced during the lithographic exposure process is reduced.

[0199] As previously described, any suitable method disclosed herein may be used, based on a measure of splicing error (e.g., based on a comparison of the splicing error of a plurality of images such as Figure 7 The process characteristics are determined by image analysis of the boundary area between two adjacent areas depicted in FIG. 5 . Areas 502-512-522-532 can relate to individual (exposure) fields (the complete image of the patterning device at the substrate level) or to individual sub-fields that are part of a field (e.g., a die area, a cell area, or an area associated with a specific control grid layout). Further background information about sub-fields of a lithographic apparatus and information about sub-field-based control is disclosed in International Patent Application WO2016146217A1, which is incorporated herein by reference in its entirety.

[0200] Specifically, the process characteristics are determined by focusing on the translation errors between features located in adjacent fields or sub-fields (in both the x-direction and the y-direction). The process characteristics may then include one or more lower-order deformation errors and higher-order deformation errors associated with patterning the first region 502 and / or the second region 512. The deformation errors may be characterized by modeling the stitching errors (the translational portion) as a distortion model. The distortion model may be configured to describe intra-field distinguishing features that represent the deformation errors.

[0201] The deformation error is typically expressed as a distortion characterized by a set of distortion model parameters. The distortion model can be based on a 2D polynomial basis function defined within a region (field or subfield) across the substrate. For example, according to the well-known k-parameter-based configuration, the distortion can be specifically expressed as a linear combination of polynomials X^m*Y^n, where each k parameter is associated with a physically relevant type of distortion. More information about k-parameter-based modeling is disclosed in paragraphs

[0084] -

[0085] of international patent application WO2017067752A1, which is incorporated herein by reference in its entirety.

[0202] The selected set of polynomial basis functions can be orthogonal when defined across field regions or sub-field regions, for example the polynomial basis functions can be a set of Legendre polynomials or Chebyshev polynomials, which are disclosed in international patent application WO2011101192A1, which is incorporated herein by reference in its entirety.

[0203] Alternatively, the distortion model can be based on spline (basis) functions, such as non-uniform rational basis spline functions (NURBS), as disclosed in international patent application WO2019219285A1, which is incorporated herein by reference in its entirety.

[0204] Distortion model parameter values ​​are typically obtained by fitting a plurality of measured stitching errors to the distortion model basis functions, each stitching error being associated with, for example, a specific (positional) shift between a first portion of a feature in the first region 502 and a second portion of a feature in the second region 512 (a boundary region along the y-direction within the image 702). The stitching errors may additionally include a plurality of measured stitching errors between a first portion of another feature in the first region 502 and a second portion of another feature in the third region 522 (a boundary region along the x-direction within the image 704).

[0205] The measured stitching errors may be further selected based on the criticality of the associated features. For example, when performing the fitting of the distortion model, stitching error measurements associated with features that are relatively tolerant to stitching errors (e.g., where the features have larger dimensions or are less critical to the electrical properties of the semiconductor device in which the features are included) may be omitted or receive a reduced weighting factor. Alternatively, the stitching errors may be averaged over one or more (different) types of features. In an example, the stitching errors of a) isolated and b) densely distributed features are averaged to obtain a stitching error that is more representative of a series of product features provided to the first region and / or the second region on the substrate.

[0206] Alternatively, the stitching error measurement data may be classified for each feature type or category to obtain multiple sets of stitching error measurement data. Each set of stitching error measurement data may be fitted to a distortion model to obtain multiple sets of distortion model parameters.

[0207] The obtained (set of) distortion model parameters can then be used to configure a lithographic apparatus for patterning a region on a substrate. Where multiple sets of distortion model parameters are available, the configuration can be based on a weighted combination of the individual sets of distortion model parameters (values). The weighting is typically based on the stitching error criticality of the feature(s) associated with the individual sets of distortion model parameters.

[0208] In many cases, information related to intrafield distortion (known intrafield distortion components) is already available due to the availability of previously performed alignment, projection lens aberrations, and / or overlay accuracy measurements. This means that at least some knowledge of the expected stitching errors is often available and can be used for one or more of the following:

[0209] a) Verify the consistency of the measured splicing errors;

[0210] b) enhancement of a set of stitching error measurements, for example to more accurately determine a set(s) of distortion model parameters, thereby improving configuration of a lithographic apparatus;

[0211] c) Decorrecting the stitching error measurements to isolate the stitching error (distortion) components associated with specific contributors. For example, by subtracting the stitching error components induced by projection lens aberrations, the contribution of wafer stage control to the stitching error (and thus the derived distortion model parameters) can be quantified.

[0212] Intra-field distortion components that are known to have no or limited impact on feature placement at the boundary regions between regions 502-512-522-532 may be excluded from consideration. For example, aberration-induced distortion components that are symmetric about the centers of regions 502 and 512 may be excluded from any of purposes a), b), or c) when applied to stitching error measurements associated with image 702.

[0213] In addition to the known intra-field distortion components, field-to-field variations of the distortion components can also be used (inter-field components) and / or field-specific intra-field distortion components can be used (e.g., for use in defining field-specific control of a lithographic apparatus). Field-to-field variations can, for example, result from process influences (e.g., due to CMP polishing steps and layer deposition steps that induce stress components). Field-to-field variations can also occur due to variations in field positioning (translation Tx and Ty) and orientation (rotation Rz) caused by stage positioning limitations (thermal drift, finite repro, sensor noise, etc.). Similar to the use of known intra-field distortion components, knowledge of the inter-field distortion components can also be used to validate, enhance, or de-correct measured stitching errors (or distortion models derived therefrom).

[0214] Distortion model parameters derived from stitching error measurements (either raw measurements or measurements adjusted using knowledge of intra-field and / or inter-field distortion components) can be used to configure control parameters of a lithographic apparatus. These control parameters can be related to actuation of the projection lens, wafer stage, and / or reticle stage during operation of the lithographic apparatus.

[0215] The (modeled) stitching error that occurs at the boundary region may have one or more systematic intra-field components, such as those based on general properties of the projection lens aberration profile across the zones 502-512-522-532 (intra-field distortion components) and / or general properties of one or more processes used in patterning the substrate (e.g., in-field stress distribution). At least a portion of the systematic intra-field components may be pre-corrected during the manufacture of a patterning device (reticle) used in providing features to the zones 502-512-522-532. For example, the expected stitching error may point to a parabolic position shift curve along the upper boundary of the zones (the lower portion of image 704), while no specific position shift curve is expected at the lower boundary (the upper portion of image 704). Currently, the patterning device may be manufactured / designed so that the parabolic position shift curve is pre-corrected by adjusting the position of the (product) feature on the patterning device so that the expected position shift curve of the patterned (product) feature is flat.

[0216] In many cases, it is preferred to modify an existing reticle using a method that locally adapts the density of the reticle substrate material (either by direct measurement or based on already available knowledge), especially near features in one or more boundary regions for which stitching error data is available. Density adaptation can be achieved by locally exposing the reticle substrate to femtosecond laser pulses, as disclosed in International Patent Application WO2017067757A1, which is incorporated herein by reference in its entirety. Based on the stitching error data, local adaptation of the reticle substrate density can be used to correct the position shift profile to a level that can be accepted or corrected by the control system (actuator) of the lithographic apparatus.

[0217] Returning to higher order distortion models, it has been revealed that 2D polynomials are generally used to describe intra-field distortion. Specifically, the k parameter associated with the polynomial representing the physically relevant distortion components (barrel, cushion, etc.) can be used.

[0218] In some cases, stitching error data for both horizontally oriented boundary regions (e.g., between regions 502 and 522) and vertically oriented boundary regions (e.g., between regions 502 and 512) are available. This is particularly relevant when the regions are stitched in both the X and Y directions. Fitting the stitching error data to the distortion model basis functions (2D polynomials) needs to be preferably processed so that no crosstalk occurs between the model parameters. This can be achieved by fitting model parameters associated with higher-order terms that are constant across the entire boundary region in a separate step (thus, Y for horizontally oriented boundaries and X for vertically oriented boundaries). Furthermore, it is recommended to first model the linear terms of the distortion model based on fitting the stitching error data for both boundary (horizontally and vertically) orientations in one step to linear (polynomial) basis functions.

[0219] In summary, the following procedure is suggested: (1) fit the linear portion of the distortion model (e.g., x and y) to the combined stitching error data along both the horizontal and vertical directions; (2) remove the linear content from the stitching error data to obtain higher-order stitching error data; (3) model the higher-order stitching error data associated with horizontally oriented boundary regions as higher-order polynomial basis functions X^m*Y^n, thereby excluding m=0 to prevent the risk of introducing crosstalk between distortion model parameters; (4) model the higher-order stitching error data associated with vertically oriented boundary regions as higher-order polynomial basis functions X^m*Y^n, thereby excluding n=0 to prevent the risk of introducing crosstalk between distortion model parameters; (5) combine the calculated distortion model parameters from steps 1, 3, and 4, and for the definition of the k parameter, determine the following model parameters:

[0220] K3, K4, K5, K6 are determined by step 1;

[0221] K9 and K10 are determined by averaging the results of steps 3 and 4;

[0222] K15 and K18 are determined by step 3;

[0223] K16 and K17 are determined by step 4.

[0224] The order of steps 3 and 4 can be reversed, the suggested order is only an example. The method is not limited to determining K parameters up to K18, and in the case of densely distributed splicing error data, higher order terms (K18+) such as up to 5, 7 or 9 can also be determined.

[0225] Furthermore, the procedure is not limited to determining the K parameter, but may also determine coefficients associated with orthogonal polynomial basis functions (such as Legendre polynomials) according to the methods described above.

[0226] In an embodiment, a method for characterizing a patterning process is provided, the method comprising: obtaining a plurality of values ​​of stitching errors generated along one or more boundaries between at least two adjacent patterned fields or subfields on a substrate; and fitting a distortion model to the plurality of values ​​to obtain a distinctive signature representing a deformation of a field or subfield in the at least two adjacent patterned fields or subfields.

[0227] In an embodiment, the stitching error is a translation error between a first portion of a feature comprised within a first field or subfield of the at least two adjacent patterned fields or subfields and a second portion of a feature comprised within a second field or subfield of the at least two adjacent patterned fields or subfields.

[0228] In an embodiment, the distortion model comprises distortion model parameters associated with 2D polynomial basis functions.

[0229] In an embodiment, the distortion model comprises distortion model parameters associated with a spline function.

[0230] In an embodiment, the multiple values ​​of the stitching error include: a first value of the stitching error generated along a first boundary between a first field or subfield and an adjacent second field or subfield; and a second value of the stitching error generated along a second boundary between the first field or subfield and an adjacent third field or subfield, wherein the orientations of the first boundary and the second boundary are different.

[0231] In an embodiment, the plurality of values ​​of stitching error are associated with features of at least two different types, and the distortion model is fit to a subset of the plurality of values ​​associated with features of one or more key types of stitching error.

[0232] In an embodiment, the method further comprises assigning weighting factors to distortion model parameters associated with the distortion model based on a measure of the criticality of features of the critical type of stitching error.

[0233] In an embodiment, obtaining the plurality of values ​​comprises taking a weighted average of stitching errors associated with different types of features.

[0234] In an embodiment, the weighting is based on a measure of the criticality of the type of feature.

[0235] In an embodiment, the weighting is the same for each type of feature.

[0236] In an embodiment, the method further comprises configuring the lithographic apparatus using parameter values ​​associated with the fitted distortion model.

[0237] In an embodiment, the method further comprises: obtaining intra-field and / or inter-field deformation data; and performing one or more of the following: verifying the consistency of multiple values ​​of the stitching error using the intra-field and / or inter-field data; combining the distinguishing identifier with the intra-field and / or inter-field data to obtain an enhanced distinguishing identifier; and decorrecting the distinguishing identifier to separate one or more contributing factors from the distinguishing identifier.

[0238] In an embodiment, the method further comprises manufacturing, designing or modifying a patterning device used in the patterning process based on the distinctive identifier or a systematic component separate from the distinctive identifier.

[0239] In an embodiment, the modification of the patterning device is based on locally exposing a substrate of the patterning device to laser pulses, wherein the length of the laser pulses is in the femtosecond range and causes a local modification of the density of the material of the substrate of the patterning device.

[0240] In an embodiment, the fitting of the distortion model is performed at least partially in separate steps, comprising at least a first step of fitting the distortion model only to a first value of the stitching error and a second step of fitting the distortion model only to a second value of the stitching error.

[0241] Additional embodiments are disclosed in the following list of numbered aspects:

[0242] 1. An apparatus for determining performance of a lithographic patterning process, the apparatus comprising one or more processors configured to:

[0243] receiving an image of a portion of a substrate, the portion of the substrate comprising a first region and a second region, the first region comprising a first feature associated with a first lithographic exposure of the substrate at a first time, and the second region comprising a second feature associated with a second lithographic exposure of the substrate at a second time, wherein the first region and the second region do not overlap, and wherein the first feature and the second feature form a single feature extending along at least a portion of the first region and at least a portion of the second region; and

[0244] Performance of the lithographic patterning process is determined based on one or more feature characteristics of the exposed first features and / or the exposed second features associated with the boundary between the first region and the second region.

[0245] 2. The apparatus of clause 1, wherein the boundary comprises a portion of an outer edge of the first region and a portion of an outer edge of the second region.

[0246] 3. The apparatus of any one of the preceding aspects, wherein the first feature and the second feature comprise at least one of a product feature and a dummy feature having one or more dimensions that are the same as the product feature.

[0247] 4. The apparatus according to any one of the preceding aspects, wherein the one or more characteristic features include a distance indicator, the distance indicator comprising:

[0248] the distance between one or more axes of symmetry of the first feature and one or more axes of symmetry of the second feature; and / or

[0249] The physical distance between the first feature and the second feature.

[0250] 5. The apparatus of clause 1 , wherein the substrate is a wafer.

[0251] 6. The apparatus of clause 1 , wherein the one or more feature characteristics include narrowing or thickening of a single feature at or near a boundary.

[0252] 7. The apparatus of any preceding aspect, wherein the first feature and the second feature form part of a patterned layer of photoresist or a layer of material after being patterned by an etching process.

[0253] 8. The apparatus of any preceding aspect, wherein determining the performance comprises analyzing the image to determine one or more characteristic features of the first feature and / or the second feature associated with a boundary between the first region and the second region.

[0254] 9. The apparatus of clause 8, wherein determining the performance comprises performing a comparison of the first feature and / or the second feature of the image with a standard for the first feature and / or the second feature.

[0255] 10. The apparatus of clause 8 or 9, wherein determining the performance further comprises determining the performance of one or more lithographic patterning process characteristics based on the determined one or more feature characteristics.

[0256] 11. The apparatus of clause 10, wherein the one or more characteristic properties include a spatial size of the first feature and / or the second feature.

[0257] 12. The apparatus of clause 10 or clause 11, wherein the one or more process characteristics include one or more of magnification, translation, and / or higher order deformation errors associated with patterning the first and / or second regions.

[0258] 13. The apparatus of any of clauses 10 to 12, wherein the performance of the one or more process characteristics is determined at least in part using a model that takes as input at least one of the one or more characteristic characteristics.

[0259] 14. The apparatus of clause 13, wherein the model comprises a machine learning model.

[0260] 15. The apparatus of clause 14, wherein the model comprises a neural network.

[0261] 16. The apparatus of clause 15, wherein the model comprises vision techniques.

[0262] 17. An apparatus according to any one of aspects 14 to 16, wherein the model is configured to be trained based on a training set of images of a portion of a substrate comprising a first feature and a second feature, wherein the first feature and / or the second feature of the images of the training set have one or more known characteristic properties associated with a known performance of a lithography patterning process.

[0263] 18. An apparatus according to aspect 17, wherein each image of the training set includes a portion of a training substrate, the portion of the training substrate including a first feature associated with a first lithographic exposure of the training substrate at a first time and a second feature associated with a second lithographic exposure of the training substrate at a second time.

[0264] 19. The apparatus of any one of clauses 17 to 18, wherein the known characteristic properties and performance of the lithographic patterning process are based at least in part on one or more measurements of one or more characteristic properties of the first feature and / or the second feature.

[0265] 20. The apparatus of any one of clauses 17 to 19, wherein the known performance of the lithographic patterning process comprises a known stitching error.

[0266] 21. The apparatus of any preceding aspect, wherein determining the performance of the lithographic patterning process comprises:

[0267] determining a preprocessed image obtained by removing noise from the image; and

[0268] One or more characteristic features are identified from the pre-processed image.

[0269] 22. The apparatus of clause 21, wherein determining the pre-processed image comprises determining the image including gradient magnitudes of the image.

[0270] 23. An apparatus according to any one of clauses 21 to 22, wherein determining the pre-processed image comprises determining a binary image based on the image, the binary image representing data in the image in a binary manner.

[0271] 24. The apparatus of any one of clauses 21 to 23, wherein determining the pre-processed image comprises:

[0272] detecting one or more line features in an image and / or a binary image; and

[0273] The image and / or the binary image is rotated so that at least one of the one or more line features is parallel or perpendicular to a boundary between the first region and the second region.

[0274] 25. An apparatus according to any one of aspects 21 to 24, wherein identifying one or more characteristic features from the preprocessed image includes applying a Fourier transform to multiple portions of the preprocessed image to quantify the stitching quality at the boundary between the first region and the second region.

[0275] 26. The apparatus of aspect 25, wherein identifying one or more characteristic features from the preprocessed image further comprises determining a duty cycle of the plurality of portions that are Fourier transformed, and determining the one or more characteristic features based on the duty cycle of the plurality of portions.

[0276] 27. An apparatus according to any one of aspects 25 to 26, wherein identifying one or more characteristic features from the preprocessed image further comprises: determining the phases of the multiple parts that are Fourier transformed, and determining one or more characteristic features based on the phases of the multiple parts.

[0277] 28. The apparatus of any one of clauses 25 to 27, wherein the plurality of portions comprises a plurality of rows of pixels, wherein the rows are aligned with a boundary between the first region and the second region.

[0278] 29. The apparatus of clause 23, wherein determining the performance of the lithographic patterning process comprises:

[0279] determining a first binary image based on the image;

[0280] determining a second binary image based on the binary gradients of the image; and

[0281] One or more characteristic features are identified based on a combination of the first binary image and the second binary image.

[0282] 30. The apparatus of any one of clauses 25 to 29, wherein the one or more characteristic features include overlay accuracy.

[0283] 31. The apparatus of any one of clauses 25 to 30, wherein identifying one or more characteristic features uses a regression model and / or a lookup table.

[0284] 32. The apparatus of any preceding aspect, wherein determining the performance of the lithographic patterning process further comprises determining an indicator of stitching quality at a boundary between the first region and the second region.

[0285] 33. The apparatus of clause 32, wherein the indicator represents at least one of a flatness of a stitching around a boundary between a first zone and a second zone and a skewness of a stitching around a boundary between the first zone and the second zone.

[0286] 34. The apparatus of any preceding clause, wherein the first region and the second region form part of the same device on the substrate.

[0287] 35. The apparatus of any preceding clause, wherein the first region is a first field exposed on the substrate and the second region is a second field exposed on the substrate;

[0288] And wherein the boundary includes a portion of an edge of the first field and a portion of an edge of the second field.

[0289] 36. The apparatus of clause 23, wherein determining the performance comprises determining a splicing error between the first field and the second field.

[0290] 37. The apparatus of any preceding clause, wherein the received image includes the substrate between exposures of subsequent layers on the substrate.

[0291] 38. The apparatus of any one of the preceding aspects, wherein the received image comprises a boundary between the first region and the second region extending in at least one direction.

[0292] 39. The apparatus according to any of the preceding aspects, wherein the processor is configured to receive a plurality of images and to determine the quality of the patterning process based on the plurality of images.

[0293] 40. An apparatus according to aspect 39, wherein the plurality of images include the first image and the second image, the first image includes a boundary between the first zone and the second zone in a first direction, and the second image includes a boundary between the first zone and another zone in a second direction, and wherein the first direction and the second direction are not parallel to each other.

[0294] 41. The apparatus of clause 40, wherein the first direction and the second direction are substantially perpendicular to each other.

[0295] 42. The apparatus of any of clauses 40 to 41, wherein the one or more processors are further configured to determine a performance of one or more process characteristics of the first image, and further configured to determine one or more process characteristics of the second image; and

[0296] The first image and one or more process characteristics of the second image are combined to determine performance of the patterning process.

[0297] 43. An apparatus according to any one of clauses 39 to 42, wherein the plurality of images depicts a plurality of separate locations on the substrate.

[0298] 44. The apparatus of clause 43, wherein the one or more process characteristics are determined for individual locations on the substrate.

[0299] 45. The apparatus of any of the preceding aspects, wherein the one or more processors are further configured to determine one or more corrections to the patterning process based on performance of the lithographic patterning process.

[0300] 46. ​​The apparatus of clause 45, wherein the one or more processors are further configured to update the photolithographic patterning process with one or more corrections.

[0301] 47. The apparatus of clause 46, wherein updating the lithographic patterning process comprises updating at least one of one or more exposure settings of the lithographic apparatus and a reticle design.

[0302] 48. The apparatus of any preceding clause, wherein the lithographic patterning process is configured to pattern the substrate using a reticle and electromagnetic radiation.

[0303] 49. The device according to any of the preceding aspects, wherein the one or more processors are further configured to control the measurement device to obtain the image.

[0304] 50. The apparatus of clause 49, wherein controlling the metrology apparatus to obtain the image comprises guiding the metrology apparatus based on the one or more previously determined characteristic features.

[0305] 51. The apparatus of clause 48, wherein the metrology apparatus comprises an electron beam imager.

[0306] 52. A method for determining performance of a lithographic patterning process, the method comprising:

[0307] receiving an image of a portion of a substrate, the portion of the substrate comprising a first region and a second region, the first region comprising a first feature associated with a first lithographic exposure made to the substrate at a first time, and the second region comprising a second feature associated with a second lithographic exposure made to the substrate at a second time, wherein the first feature and the second feature form a single feature extending along at least a portion of the first region and at least a portion of the second region, and wherein the first region and the second region do not overlap; and

[0308] Performance of the lithographic patterning process is determined based on one or more feature characteristics of the exposed first features and / or the exposed second features associated with the boundary between the first region and the second region.

[0309] 53. A method according to clause 52, wherein the boundary comprises a portion of an outer edge of the first zone and a portion of an outer edge of the second zone.

[0310] 54. The method of any one of aspects 52 to 53, wherein the first feature and the second feature comprise at least one of a product feature and a dummy feature having one or more dimensions that are the same as the product feature.

[0311] 55. The method of any one of clauses 52 to 54, wherein the substrate is a wafer.

[0312] 56. A method according to any one of aspects 52 to 55, wherein the one or more characteristic features include a distance indicator comprising:

[0313] the distance between one or more axes of symmetry of the first feature and one or more axes of symmetry of the second feature; and / or

[0314] The physical distance between the first feature and the second feature.

[0315] 57. The method of clause 55, wherein the one or more feature characteristics include narrowing or thickening of a single feature at or near a boundary.

[0316] 58. The method of any one of clauses 52 to 57, wherein the first features and the second features form part of a patterned layer of photoresist or a layer of material after being patterned by an etching process.

[0317] 59. A method according to any one of aspects 52 to 58, wherein determining the property comprises analyzing the image to determine one or more characteristic features of the first feature and / or the second feature associated with a boundary between the first region and the second region.

[0318] 60. The method of clause 59, wherein determining the performance comprises performing a comparison of the first feature and / or the second feature of the image with a standard for the first feature and / or the second feature.

[0319] 61. The method of clause 59 or 60, wherein determining the performance further comprises determining the performance of one or more lithographic patterning process characteristics based on the determined one or more feature characteristics.

[0320] 62. The method of clause 61, wherein the one or more feature characteristics include a spatial size of the first feature and / or the second feature.

[0321] 63. A method according to clause 61 or 62, wherein the one or more process characteristics include one or more of magnification, translation and / or higher order deformation errors associated with patterning the first region and / or the second region.

[0322] 64. The method of any of aspects 60 to 63, wherein the performance of the one or more process characteristics is determined at least in part using a model that takes as input at least one of the one or more feature characteristics.

[0323] 65. The method of clause 64, wherein the model comprises a machine learning model.

[0324] 66. A method according to clause 65, wherein the model comprises a neural network.

[0325] 67. The method of clause 66, wherein the model comprises vision techniques.

[0326] 68. A method according to any one of aspects 65 to 66, wherein the model is configured to be trained based on a training set of images of a portion of the substrate comprising a first feature and a second feature, wherein the first feature and / or the second feature of the training set images have one or more known characteristic properties associated with a known performance of the lithography patterning process.

[0327] 69. An apparatus according to aspect 68, wherein each image of the training set includes a portion of a training substrate, the portion of the training substrate including a first feature associated with a first lithographic exposure of the training substrate at a first time and a second feature associated with a second lithographic exposure of the training substrate at a second time.

[0328] 70. The apparatus of any one of clauses 68 to 69, wherein the known characteristic properties and performance of the lithographic patterning process are based at least in part on one or more measurements of one or more characteristic properties of the first feature and / or the second feature.

[0329] 71. The apparatus of any one of clauses 68 to 70, wherein the known performance of the lithographic patterning process comprises a known stitching error.

[0330] 72. The method of any one of clauses 52 to 71, wherein the first region and the second region form part of the same device on the substrate.

[0331] 73. A method according to any one of clauses 52 to 72, wherein the first region is a first field exposed on the substrate and the second region is a second field exposed on the substrate;

[0332] And wherein the boundary includes a portion of an edge of the first field and a portion of an edge of the second field.

[0333] 74. The method of clause 73, wherein determining the performance comprises determining a splicing error between the first field and the second field.

[0334] 75. A method according to any of clauses 52 to 74, wherein the received image includes the substrate between exposures of subsequent layers on the substrate.

[0335] 76. A method according to any one of clauses 52 to 75, wherein the received image includes a boundary between the first region and the second region extending in at least one direction.

[0336] 77. The method of any one of aspects 52 to 76, further comprising receiving a plurality of images and determining a quality of the patterning process based on the plurality of images.

[0337] 78. A method according to aspect 77, wherein the multiple images include the first image and the second image, the first image includes a boundary between the first area and the second area in a first direction, and the second image includes a boundary between the first area and another area in a second direction, and wherein the first direction and the second direction are not parallel to each other.

[0338] 79. The method of clause 78, wherein the first direction and the second direction are substantially perpendicular to each other.

[0339] 80. The method according to any one of aspects 78 to 79, further comprising:

[0340] determining a performance of one or more process characteristics of the first image, and determining one or more process characteristics of the second image; and

[0341] The first image and one or more process characteristics of the second image are combined to determine performance of the patterning process.

[0342] 81. A method according to any one of clauses 77 to 80, wherein the plurality of images depicts a plurality of separate locations on the substrate.

[0343] 82. The method of clause 81, wherein one or more process characteristics are determined for individual locations on the substrate.

[0344] 83. The method of any one of clauses 52 to 82, further comprising determining one or more corrections to the patterning process based on performance of the lithographic patterning process.

[0345] 84. The method of clause 83, further comprising updating the photolithographic patterning process by one or more corrections.

[0346] 85. The method of clause 84, wherein updating the lithographic patterning process comprises updating at least one of one or more exposure settings of a lithographic apparatus and a reticle design.

[0347] 86. The method of any one of clauses 52 to 85, wherein the lithographic patterning process is configured to pattern the substrate using a reticle and electromagnetic radiation.

[0348] 87. The method of any one of aspects 52 to 86, further comprising controlling a measurement device to obtain an image.

[0349] 88. The method of clause 87, wherein controlling the metrology device to obtain the image comprises guiding the metrology device based on the one or more previously determined characteristic features.

[0350] 89. The method of clause 87, wherein the metrology device comprises an electron beam imager.

[0351] 90. A method for characterizing a patterning process, the method comprising:

[0352] obtaining a plurality of values ​​of stitching errors generated along one or more boundaries between at least two adjacent patterned fields or sub-fields on the substrate; and

[0353] A distortion model is fitted to the plurality of values ​​to obtain a distinctive signature representing a deformation of a field or subfield of the at least two adjacent patterned fields or subfields.

[0354] 91. A method according to aspect 90, wherein the stitching error is a translation error between a first portion of a feature included in a first field or subfield of the at least two adjacent patterned fields or subfields and a second portion of a feature included in a second field or subfield of the at least two adjacent patterned fields or subfields.

[0355] 92. A method according to clause 90 or 91, wherein the distortion model comprises distortion model parameters associated with 2D polynomial basis functions.

[0356] 93. A method according to clause 90 or 91, wherein the distortion model comprises distortion model parameters associated with a spline function.

[0357] 94. A method according to any one of aspects 90 to 93, wherein the multiple values ​​of the stitching error include: a first value of the stitching error generated along a first boundary between a first field or subfield and an adjacent second field or subfield; and a second value of the stitching error generated along a second boundary between the first field or subfield and an adjacent third field or subfield, wherein the orientations of the first boundary and the second boundary are different.

[0358] 95. A method according to any one of aspects 90 to 94, wherein multiple values ​​of stitching error are associated with features of at least two different types, and the distortion model is fit to a subset of the multiple values ​​associated with features of one or more key types of stitching error.

[0359] 96. The method of clause 95, further comprising assigning weighting factors to distortion model parameters associated with the distortion model based on a measure of the criticality of features of the critical type of stitching error.

[0360] 97. A method according to any one of clauses 90 to 96, wherein obtaining a plurality of values ​​comprises taking a weighted average of stitching errors associated with different types of features.

[0361] 98. The method of clause 97, wherein the weighting is based on a measure of criticality of the type of feature.

[0362] 99. The method of clause 98, wherein the weighting is the same for each type of feature.

[0363] 100. The method of any of clauses 90 to 99, further comprising configuring the lithographic apparatus using parameter values ​​associated with the fitted distortion model.

[0364] 101. The method according to any one of aspects 90 to 100, further comprising:

[0365] obtaining intra-field and / or inter-field deformation data; and

[0366] One or more of the following is performed: utilizing intrafield and / or interfield data to verify consistency of multiple values ​​of stitching error; combining the distinctive mark with the intrafield and / or interfield data to obtain an enhanced distinctive mark; and decorrecting the distinctive mark to separate one or more contributing factors from the distinctive mark.

[0367] 102. The method of any one of clauses 90 to 101, further comprising manufacturing, designing or modifying a patterning device used in the patterning process based on the distinctive identity or a systematic component separate from the distinctive identity.

[0368] 103. The method of clause 102, wherein the modification of the patterning device is based on locally exposing the substrate of the patterning device to laser pulses, wherein the length of the laser pulses is in the femtosecond range and causes a local modification of the density of the material of the substrate of the patterning device.

[0369] 104. A method according to aspect 94, wherein the fitting of the distortion model is performed at least in part in separate steps, the separate steps comprising at least a first step of fitting the distortion model only to a first value of the stitching error and a second step of fitting the distortion model only to a second value of the stitching error.

[0370] 105. A method according to aspect 74, wherein the stitching error includes multiple values ​​of the stitching error generated along one or more boundaries between at least two adjacent patterned fields or subfields on the substrate; and the method further includes: fitting a distortion model to the multiple values ​​to obtain a distinctive signature representing the deformation of the field or subfield in the at least two adjacent patterned fields or subfields.

[0371] 106. A method according to aspect 105, wherein the stitching error is a translation error between a first portion of a feature included in a first field or subfield of the at least two adjacent patterned fields or subfields and a second portion of a feature included in a second field or subfield of the at least two adjacent patterned fields or subfields.

[0372] 107. A method according to clause 105 or 106, wherein the distortion model comprises distortion model parameters associated with 2D polynomial basis functions.

[0373] 108. A method according to clause 105 or 106, wherein the distortion model comprises distortion model parameters associated with a spline function.

[0374] 109. A method according to any one of aspects 105 to 108, wherein the multiple values ​​of the stitching error include: a first value of the stitching error generated along a first boundary between a first field or subfield and an adjacent second field or subfield; and a second value of the stitching error generated along a second boundary between the first field or subfield and an adjacent third field or subfield, wherein the orientations of the first boundary and the second boundary are different.

[0375] 110. A method according to any one of aspects 105 to 109, wherein the multiple values ​​of the stitching error are associated with at least two different types of features, and the distortion model is fit to a subset of the multiple values ​​associated with one or more key types of features of the stitching error.

[0376] 111. The method of clause 110, further comprising assigning weighting factors to distortion model parameters associated with the distortion model based on a measure of the criticality of features of the critical type of stitching error.

[0377] 112. The method of any one of clauses 105 to 111, wherein obtaining the plurality of values ​​comprises taking a weighted average of stitching errors associated with different types of features.

[0378] 113. The method of clause 112, wherein the weighting is based on a measure of criticality of the type of feature.

[0379] 114. The method of clause 113, wherein the weighting is the same for each type of feature.

[0380] 115. The method of any of clauses 105 to 114, further comprising configuring the lithographic apparatus using parameter values ​​associated with the fitted distortion model.

[0381] 116. The method according to any one of aspects 105 to 115, further comprising:

[0382] obtaining intra-field and / or inter-field deformation data; and

[0383] One or more of the following is performed: verifying consistency of multiple values ​​of stitching error using intrafield and / or interfield data; combining the distinctive mark with the intrafield and / or interfield data to obtain an enhanced distinctive mark; and decorrecting the distinctive mark to separate one or more contributing factors from the distinctive mark.

[0384] 117. The method of any one of clauses 105 to 116, further comprising manufacturing, designing, or modifying a patterning device used in the patterning process based on the distinctive identity or a systematic component separate from the distinctive identity.

[0385] 118. A method according to clause 117, wherein the modification of the patterning device is based on locally exposing the substrate of the patterning device to laser pulses, wherein the length of the laser pulses is in the femtosecond range and causes a local modification of the density of the material of the substrate of the patterning device.

[0386] 119. A method according to aspect 109, wherein the fitting of the distortion model is performed at least in part in separate steps, the separate steps comprising at least a first step of fitting the distortion model only to a first value of the stitching error and a second step of fitting the distortion model only to a second value of the stitching error.

[0387] 120. A computer program product comprising computer readable instructions which, when executed on a suitable apparatus, perform the method according to any one of clauses 52 to 119.

[0388] Although specific reference may be made herein to the use of lithographic equipment in IC manufacturing, it should be understood that the lithographic equipment described herein may have other applications. Possible other applications include the manufacture of integrated optical systems, guidance and detection patterns for magnetic domain memories, flat panel displays, liquid crystal displays (LCDs), thin film magnetic heads, and the like.

[0389] Although specific reference may be made herein to embodiments of the present invention in the context of lithographic apparatus, embodiments of the present invention may be used in other apparatus. Embodiments of the present invention may form part of mask inspection equipment, metrology equipment, or any apparatus for measuring or processing objects such as wafers (or other substrates) or masks (or other patterning devices). These apparatuses may generally be referred to as lithographic tools. Such lithographic tools may use vacuum conditions or ambient (non-vacuum) conditions.

[0390] Although the above may have specific reference to the use of embodiments of the invention in the context of optical lithography, it will be appreciated that the invention is not limited to optical lithography and may be used in other applications (e.g., imprint lithography) where the context permits.

[0391] Although specific embodiments of the present invention have been described above, it will be appreciated that the present invention may be practiced in other ways than those described. The above description is intended to be illustrative rather than restrictive. Therefore, it will be apparent to those skilled in the art that modifications may be made to the present invention as described without departing from the scope of the claims set forth below.

[0392] Although specific reference is made to "measurement equipment / tools / systems" or "inspection equipment / tools / systems," these terms may refer to the same or similar types of tools, equipment, or systems. For example, an inspection or measurement equipment including embodiments of the present invention may be used to determine characteristics of structures on a substrate or on a wafer. For example, an inspection or measurement equipment including embodiments of the present invention may be used to detect defects in a substrate, or defects in a structure on a substrate or on a wafer. In such embodiments, the characteristic of interest of a structure on a substrate may relate to a defect in the structure, the absence of a particular portion of the structure, or the presence of an undesired structure on the substrate or on the wafer.

Claims

1. A method for characterizing a patterning process, the method comprising: obtaining a plurality of values ​​of stitching error along one or more boundaries between at least two adjacent patterned fields or sub-fields on the substrate; fitting a distortion model to the plurality of values ​​to obtain a distinctive signature representing a deformation of a field or subfield within the at least two adjacent patterned fields or subfields; obtaining intra-field and / or inter-field deformation data; as well as Do one or more of the following: verifying consistency of the multiple values ​​of the stitching error using the intra-field and / or inter-field data; combining the distinctive identifier with the intra-field and / or inter-field data to obtain an enhanced distinctive identifier; The distinguishing feature is decalibrated to separate one or more contributing factors from the distinguishing feature.

2. The method according to claim 1, wherein The stitching error is a translation error between a first portion of a feature within a first field or subfield comprised within the at least two adjacent patterned fields or subfields and a second portion of a feature within a second field or subfield comprised within the at least two adjacent patterned fields or subfields.

3. The method according to claim 1, wherein The distortion model includes distortion model parameters associated with 2D polynomial basis functions or spline functions.

4. The method according to claim 1, wherein The multiple values ​​of the stitching error include: a first value of the stitching error generated along a first boundary between a first field or subfield and an adjacent second field or subfield; and a second value of the stitching error generated along a second boundary between the first field or subfield and an adjacent third field or subfield, wherein the orientations of the first boundary and the second boundary are different.

5. The method according to claim 1, wherein The plurality of values ​​of the stitching error are associated with features of at least two different types, and the distortion model is fit to a subset of the plurality of values ​​associated with features of one or more key types of stitching error.

6. The method of claim 5, further comprising: Based on a measure of the criticality of features of the critical type of stitching error, weighting factors are assigned to distortion model parameters associated with the distortion model.

7. The method of claim 1, wherein: Obtaining the plurality of values ​​includes performing a weighted average of stitching errors associated with different types of features.

8. The method of claim 7, wherein: The weighting is based on a measure of the criticality of the different types of features.

9. The method of claim 1 , further comprising: The lithographic apparatus is configured using parameter values ​​associated with the fitted distortion model.

10. The method of claim 1, further comprising: A patterning device used in the patterning process is manufactured, designed, or modified based on the distinctive identifier or a systematic component separate from the distinctive identifier.

11. The method according to claim 10, wherein: The modification of the patterning device is based on locally exposing the substrate of the patterning device to laser pulses, wherein the length of the laser pulses is in the femtosecond range and causes a local modification of the density of the material of the substrate of the patterning device.

12. The method of claim 4, wherein: The fitting of the distortion model is performed at least in part in a separate step comprising at least: A first step of fitting the distortion model only to the first value of the stitching error; and a second step of fitting the distortion model only to the second value of the stitching error.

13. A computer program product comprising computer-readable instructions configured to: obtaining a plurality of values ​​of stitching errors generated along one or more boundaries between at least two adjacent patterned fields or sub-fields on the substrate; fitting a distortion model to the plurality of values ​​to obtain a distinctive signature representative of deformation of fields or subfields within the at least two adjacent patterned fields or subfields; obtaining intra-field and / or inter-field deformation data; as well as Do one or more of the following: verifying consistency of the multiple values ​​of the stitching error using the intra-field and / or inter-field data; combining the distinctive mark with the intra-field and / or inter-field data to obtain an enhanced distinctive mark; The distinguishing feature is decalibrated to separate one or more contributing factors from the distinguishing feature.

14. The computer program product of claim 13, wherein: The stitching error is a translation error between a first portion of a feature within a first field or subfield comprised within the at least two adjacent patterned fields or subfields and a second portion of a feature within a second field or subfield comprised within the at least two adjacent patterned fields or subfields.

15. The computer program product of claim 13, wherein: The distortion model includes distortion model parameters associated with 2D polynomial basis functions or spline functions.

16. The computer program product of claim 13, wherein: The multiple values ​​of the stitching error include: a first value of the stitching error generated along a first boundary between a first field or subfield and an adjacent second field or subfield; and a second value of the stitching error generated along a second boundary between the first field or subfield and an adjacent third field or subfield, wherein the orientations of the first boundary and the second boundary are different.

17. The computer program product of claim 13, wherein: The plurality of values ​​of the stitching error are associated with features of at least two different types, and the distortion model is fit to a subset of the plurality of values ​​associated with features of one or more key types of stitching error.

18. The computer program product of claim 17, further comprising instructions configured to assign weighting factors to distortion model parameters associated with the distortion model based on a measure of criticality of features of the critical type of stitching error.

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