Pattern selection for source mask optimization and target optimization

Abstract

Apparatuses, systems, and methods for selecting a subset of critical patterns from a plurality of patterns of a design layout. In some embodiments, the method includes obtaining diffraction order data based on the plurality of patterns representing features to be formed on at least a portion of a wafer, the diffraction order data including a plurality of peaks corresponding to the plurality of patterns. The method also includes identifying a subset of representative peaks from the plurality of peaks according to one or more grouping criteria, including identifying a first representative peak that covers another peak that is co-linear with the first representative peak, where the first representative peak is a discrete peak having a frequency that is an integer multiple of a frequency of the other discrete peak. The method further includes selecting a subset of critical patterns corresponding to the subset of representative peaks.

Description

Technical Field

[0001] The embodiments provided herein relate to pattern selection for photolithography, and more specifically, to pattern selection for source mask optimization and target optimization. Background Technology

[0002] In the manufacturing process of integrated circuits (ICs), masks can be used for photolithography. These masks may contain circuit patterns corresponding to individual layers of the IC, and these patterns can be imaged onto target portions (e.g., comprising one or more dies) on a substrate (e.g., a silicon wafer) already coated with a radiation-sensitive material (resist). The substrate may undergo various processes, such as primer coating, resist coating, and soft baking. After exposure, the substrate may undergo other processes, such as post-exposure baking (PEB), development, hard baking, and measurement / inspection of the imaged features. This array of processes serves as the basis for patterning individual layers of a device (e.g., an IC). Such patterned layers can then undergo various processes, such as etching, ion implantation (doping), metallization, oxidation, chemical mechanical polishing, etc., to complete the individual layer. If several layers are required, these processes or variations thereof are repeated for each new layer. Ultimately, an array of devices will exist on the substrate (wafer). These devices are then separated from each other using techniques such as sawing or cutting, and then the individual devices can be mounted onto the carrier, connected to pins, and so on.

[0003] As semiconductor manufacturing processes continue to advance, optical lithography projection systems known as scanners can be used to fabricate critical layers of leading-edge devices. These scanners project a mask image onto a substrate using illumination from a deep ultraviolet laser source, thereby forming multiple individual critical features with dimensions well below 100 nm (i.e., less than half the wavelength of the projected light). This process is often referred to as low-k1 lithography, in which features with dimensions smaller than the classical resolution limit of optical projection systems are printed.

[0004] For low-k1 lithography, optimization of both the source and mask is required (e.g., source and mask optimization, or SMO) to ensure a feasible process window for printing critical patterns. Existing algorithms (e.g., Socha et al., Proc. SPIE, Vol. 5853, 2005, p. 180) typically discretize the illumination into independent source points and the mask into multiple diffraction orders in the spatial frequency domain, and formulate cost functions based on process window metrics such as exposure latitude, which can be predicted by an optical imaging model based on source point intensity and mask diffraction order. Standard optimization techniques are then used to minimize these objective functions.

[0005] Conventional source optimization (SMO) techniques are computationally expensive, especially for complex designs. Therefore, it is generally practical to perform source optimization only on simple, repetitive designs such as memory designs (e.g., flash, DRAM, and SRAM). Meanwhile, full-chip designs include other, more complex designs, such as logic and gates. Because SMO source optimization is based on only a limited small area of ​​some designs, it is difficult to ensure that the source will be well-suited for these designs not included in the SMO process. Furthermore, as the physical size of IC components continues to shrink, accuracy and efficiency in pattern selection, as well as SMO of selected critical patterns, become increasingly important. Summary of the Invention

[0006] Embodiments of this disclosure provide apparatus, systems, and methods for pattern selection.

[0007] In some embodiments, a method is provided for selecting a subset of key patterns from a plurality of patterns of a design layout. The method includes: acquiring diffraction order data based on the plurality of patterns representing features to be formed on at least a portion of a wafer, the diffraction order data including a plurality of peaks corresponding to the plurality of patterns; identifying a subset of representative peaks from the plurality of peaks according to one or more grouping criteria, including identifying a first representative peak in the subset of representative peaks, the first representative peak covering another peak collinear with the first representative peak, wherein the first representative peak is a discrete peak having a frequency that is an integer multiple of the frequency of the other discrete peak; and selecting a subset of key patterns corresponding to the subset of representative peaks.

[0008] In some embodiments, a system is provided for selecting a subset of key patterns from a plurality of patterns in a design layout. The system includes a memory storing a set of instructions; and at least one processor configured to execute the set of instructions to cause the system to: acquire diffraction order data based on the plurality of patterns representing features to be formed on at least a portion of a wafer, the diffraction order data including a plurality of peaks corresponding to the plurality of patterns; identify a subset of representative peaks from the plurality of peaks according to one or more grouping criteria, including: identifying a first representative peak in the subset of representative peaks, the first representative peak covering another peak collinear with the first representative peak, wherein the first representative peak is a discrete peak having a frequency that is an integer multiple of the frequency of the other discrete peak; and select a subset of key patterns corresponding to the subset of representative peaks.

[0009] In some embodiments, a non-transitory computer-readable medium storing a set of instructions executable by at least one processor of a system to cause the system to perform a method for selecting a subset of key patterns from a plurality of patterns of a design layout. The method includes: acquiring diffraction order data based on the plurality of patterns representing features to be formed on at least a portion of a wafer, the diffraction order data including a plurality of peaks corresponding to the plurality of patterns; identifying a subset of representative peaks from the plurality of peaks according to one or more grouping criteria, including: identifying a first representative peak in the subset of representative peaks, the first representative peak covering another peak collinear with the first representative peak, wherein the first representative peak is a discrete peak having a frequency that is an integer multiple of the frequency of the other discrete peak; and selecting a subset of key patterns corresponding to the subset of representative peaks.

[0010] In some embodiments, a method for performing design goal optimization. The method includes: performing a first source mask optimization (SMO) on a subset of key patterns of a plurality of patterns of a design layout; optimizing a layout redirection rule based on one or more feature constraints; generating a fragment set based on the optimized layout redirection rule; and performing a second SMO on the subset of key patterns and the resulting fragment set to obtain an optimized source.

[0011] In some embodiments, a system for performing design goal optimization. The system includes a memory storing a set of instructions; and at least one processor configured to execute the set of instructions to cause the system to: perform a first source mask optimization (SMO) on a subset of key patterns of a plurality of patterns of a design layout; optimize layout redirection rules based on one or more feature constraints; generate a set of fragments based on the optimized layout redirection rules; and perform a second SMO on the subset of key patterns and the resulting set of fragments to obtain an optimized source.

[0012] In some embodiments, a non-transitory computer-readable medium storing a set of instructions executable by at least one processor of a system to cause the system to perform a method for optimizing a design objective. The method includes: performing a first source mask optimization (SMO) on a subset of key patterns of a plurality of patterns of a design layout; optimizing layout redirection rules based on one or more feature constraints; generating a set of fragments based on the optimized layout redirection rules; and performing a second SMO on the subset of key patterns and the resulting set of fragments to obtain an optimized source.

[0013] Other advantages of the embodiments of this disclosure will become apparent from the following description taken in conjunction with the accompanying drawings, in which certain embodiments of the invention are illustrated by way of illustration and example. Attached Figure Description

[0014] Figure 1 This is a view of an example photolithography projection apparatus according to an embodiment of the present disclosure.

[0015] Figure 2 This is a block diagram of an example system for performing source mask optimization (SMO) pattern selection according to embodiments of the present disclosure.

[0016] Figure 3 The figure illustrates a plurality of sub-components included in a diffraction order (DO) pattern processing component according to an embodiment of the present disclosure.

[0017] Figure 4A The diagram illustrates an embodiment of the present disclosure and is composed of... Figure 2 Example peaks corresponding to non-periodic patterns in the DO diagram processed by one or more components of the system.

[0018] Figure 4B The figure illustrates the extraction of the peak center and contour of example peaks corresponding to periodic and non-periodic patterns according to embodiments of the present disclosure.

[0019] Figure 5 The figure illustrates an example grouping criterion for grouping peaks in a DO figure according to an embodiment of the present disclosure.

[0020] Figure 6 This is a process flowchart illustrating an example process for pattern selection according to some embodiments of the present disclosure.

[0021] Figure 7 This is a process flowchart illustrating an example process for optimizing SMO design goals according to some embodiments of the present disclosure.

[0022] Figure 8 These are example methods, according to some embodiments of the present disclosure, for selecting a subset of key patterns from a plurality of patterns in a design layout. Detailed Implementation

[0023] Reference will now be made in detail to exemplary embodiments, examples of which are illustrated in the accompanying drawings. The following description refers to the accompanying drawings, in which the same numerals in different figures denote the same or similar elements unless otherwise stated. The embodiments set forth in the following description of the exemplary embodiments do not represent all embodiments. Instead, they are merely examples of apparatuses and methods relating to aspects of the disclosed embodiments as set forth in the appended claims. For example, while some embodiments are described in the context of utilizing electron beams, this disclosure is not intended to be limiting. Other types of charged particle beams can be applied similarly. Furthermore, other imaging systems, such as optical imaging, photoelectric detection, X-ray detection, etc., can be used.

[0024] As feature and transistor sizes continue to shrink, the ability to faithfully recreate the design layout on a substrate becomes increasingly difficult. Manufacturing equipment can introduce artifacts or defects when attempting to deposit such small features onto the substrate. To address the physical challenges of recreating IC layouts at such a microscale, IC manufacturing relies on techniques such as computational lithography to analyze and modify the design to address known manufacturing artifacts. By adjusting the layout, mask, or other lithographic data prior to manufacturing to resolve known manufacturing artifacts, IC manufacturers can better recreate the original, intended design.

[0025] To identify which patterns or features might cause which entity artifacts, IC manufacturers can leverage massive datasets to allow for accurate predictions. This can lead to computationally expensive techniques, which become increasingly complex as IC designs become more sophisticated.

[0026] Because of this increased computational complexity and the need for massive pattern datasets, techniques to reduce complexity are important. Source mask optimization (SMO) is a resolution enhancement technique (RET) used to provide a solution for optimizing sources and masks for critical design layers at a technology node. In some embodiments, SMO is a process in which mask design layout and illumination sources are jointly optimized to produce high-fidelity images on the substrate. Typically, SMO aims to achieve full-chip pattern coverage while reducing computational costs by intelligently selecting small sets of critical design patterns from the entire set of fragments to be used in SMO. SMO is performed only on these selected patterns to obtain optimized sources. The optimized sources are then used to optimize the masks for the entire chip (e.g., using optical proximity correction (OPC) and lithographic manufacturability checks (LMC)), and the results are compared.

[0027] Pattern selection plays a critical role in advanced technology node process development. In some embodiments, a set of test patterns is generated to cover the entire chip design, and the test patterns may include periodic patterns spanning the spacing to cover design rules and aperiodic patterns, the aperiodic patterns including critical patterns and weaknesses / hot spots from the real design. The number of test patterns can be large and can still lead to long runtimes in SMO. To reduce runtime and accelerate development cycles, pattern selection functionality has been developed and used to select representative patterns for optimization. The source can be optimized based on the selected pattern. Therefore, pattern selection is critical for SMO to ensure robust, i.e., reliable pattern coverage, which can lead to superior source and mask optimization, as well as optical proximity correction (OPC) or other applications.

[0028] SMO pattern selection can be based on the classification and grouping of patterns' diffraction orders. Based on the diffraction order grouping results, patterns with similar diffraction identification characteristics can be grouped together, and representative patterns can be selected for each group in the SMO. In some embodiments, SMO pattern selection can be applied to periodic patterns because their diffraction order peaks are discrete peaks, and it is easy to define peak centers and grouping criteria (such as coverage criteria) between the discrete peaks from those periodic patterns. Aperiodic patterns are critical because they can include larger dimensions than periodic patterns, thus having a significant impact on runtime. However, SMO pattern selection can be challenging for aperiodic patterns because the diffraction order identification characteristics of aperiodic patterns may include continuous peaks, making it difficult to define peak centers and coverage relationships. As a result, an improved pattern selection process for aperiodic patterns is desired.

[0029] Embodiments of this disclosure provide techniques for selecting a smaller, representative set of target patterns from a larger set of target patterns, wherein the representative set of target patterns is sufficient to represent key features of a design layout (e.g., a full-chip design). The larger set of target patterns may include the entire design layout of a mask, or a significant portion of the design layout. While embodiments of this disclosure are particularly suitable for SMOs, it should be understood that the pattern selection algorithm can be applied to any situation where a larger design layout can be adequately represented by intelligently selecting a smaller set of target patterns from the design layout.

[0030] For clarity, the relative dimensions of components in the accompanying drawings may be exaggerated / enlarged. In the following description of the drawings, the same or similar reference numerals refer to the same or similar components or entities, and only differences relative to the various embodiments are described. As used herein, unless explicitly stated otherwise, the term "or" covers all possible combinations unless impractical. For example, if a component is stated to include A or B, then unless explicitly stated otherwise or impractical, the component may include A, or B, or A and B. As a second example, if a component is stated to include A, B, or C, then unless explicitly stated otherwise or impractical, the component may include A, or B, or C, or A and B, or A and C, or B and C, or A and B and C.

[0031] Figure 1The figure illustrates an exemplary photolithography projection apparatus 100. Key components may include: a radiation source 120, which may be a deep ultraviolet excimer laser source or another type of source including extreme ultraviolet (EUV) sources (as disclosed above, the photolithography projection apparatus itself does not need to have the radiation source); irradiation optics, which, for example, define partial coherence (denoted as sigma) and may include optics 140, 160a, and 160b for shaping the radiation from the source 120; a pattern forming apparatus 180; and a transmission optics 160c that projects an image of a pattern from the pattern forming apparatus onto a substrate plane 195. An adjustable filter or aperture 190 at the pupil surface of the projection optics can limit the range of beam angles incident on the substrate plane 195, wherein the maximum possible angle defines the numerical aperture NA of the projection optics as n sin(θmax), where n is the refractive index of the medium between the substrate and the final element of the projection optics, and θmax is the maximum angle of the beam exiting the projection optics that can still be incident on the substrate plane 195.

[0032] In a photolithography projection apparatus, a source (e.g., radiation source 120) provides illumination (i.e., radiation) to a patterning apparatus, and projection optics guide and shape the illumination onto a substrate via the patterning apparatus. The projection optics may include at least some of the components 140, 160b, and 160c. A spatial image (AI) is the distribution of radiation intensity at the substrate level. A resist model can be used to calculate the resist image based on the spatial image. The resist model relates only to the properties of the resist layer (e.g., the effects of chemical processes occurring during exposure, post-exposure baking (PEB), and development). The optical properties of the photolithography projection apparatus (e.g., the properties of the illumination, the patterning apparatus, and the projection optics) define the spatial image and can be defined in the optical model. Since the patterning apparatus used in the photolithography projection apparatus can be modified, it is desirable to separate the optical properties of the patterning apparatus from the optical properties of at least the remaining portion of the photolithography projection apparatus, including the source and the projection optics. The techniques and models for transforming design layouts into various lithographic images (e.g., spatial images, resist images, etc.), applying OPC using those techniques and models, and evaluating performance (e.g., in terms of process windows) are described in U.S. Patent Application Publications Nos. US2008-0301620, 2007-0050749, 2007-0031745, 2008-0309897, 2010-0162197, and 2010-0180251, the entire disclosure of each of these U.S. Patent Applications is hereby incorporated by reference.

[0033] The patterning apparatus (e.g., patterning apparatus 180) may include or be able to form one or more design layouts. These design layouts can be generated using CAD (Computer-Aided Design) programs, a process often referred to as EDA (Electronic Design Automation). Most CAD programs follow a predetermined set of design rules to generate functional design layouts / patterning apparatuses. These rules are set through processing and design constraints. For example, design rules define the spacing tolerances or space tolerances between devices (such as gates, capacitors, etc.) or interconnects to ensure that devices or lines do not interact in undesirable ways. One or more of these design rule constraints may be referred to as critical dimensions (CDs). The critical dimension of a device may be defined as the minimum width of a line or via, or the minimum spacing between two lines or two vias. Thus, the CD determines the overall size and density of the designed device. One of the goals in device fabrication is (via the patterning apparatus) to faithfully reproduce the original design intent on the substrate.

[0034] As used in this invention, the terms "mask" or "patterning apparatus" can be broadly interpreted to refer to a general patterning apparatus that can be used to impart a patterned cross-section to an incident radiation beam, the patterned cross-section corresponding to a pattern to be generated in a target portion of the substrate; in this context, the term "optical valve" may also be used. Examples of such patterning apparatuses, besides classic masks (transmissive or reflective; binary, phase-shifting, hybrid, etc.), include:

[0035] - Programmable mirror arrays. An example of such a device is a matrix-addressable surface with a viscoelastic control layer and a reflective surface. The basic principle underlying this device is that, for example, the addressed regions of the reflective surface reflect incident radiation as diffracted radiation, while the unaddressed regions reflect incident radiation as non-diffracted radiation. With the use of a suitable filter, the non-diffracted radiation can be filtered out from the reflected beam, leaving only the diffracted radiation; thus, the beam becomes patterned according to the addressing pattern of the matrix-addressable surface. Suitable electronics can be used to perform the desired matrix addressing.

[0036] - Programmable LCD array. An example of such a construction is given in U.S. Patent No. 5,229,872, which is incorporated herein by reference.

[0037] One aspect of understanding the photolithography process is understanding the interaction between the radiation and the patterning apparatus. The electromagnetic field of the radiation after it has passed through the patterning apparatus can be determined based on the electromagnetic field of the radiation before it reaches the patterning apparatus and a function characterizing the interaction. This function can be referred to as the mask transmission function (which can be used to describe the interaction between transmissive and / or reflective patterning apparatuses).

[0038] Variables in the patterning process are referred to as "processing variables." The patterning process can include upstream and downstream processes of the actual transfer of the pattern in a lithography apparatus. A first category can be variables of the lithography apparatus or any other equipment used in the lithography process. Examples of this category include variables of the lithography apparatus's irradiation, projection system, substrate platform, etc. A second category can be variables of one or more steps performed in the patterning process. Examples of this category include focus control or focus measurement, dose control or dose measurement, bandwidth, exposure duration, development temperature, chemical composition used in development, etc. A third category can be variables of the design layout and its implementation in, or using, a patterning apparatus. Examples of this category can include the shape and / or location of auxiliary features, adjustments applied by resolution enhancement techniques (RET), CD of mask features, etc. A fourth category can be variables of the substrate. Examples include characteristics of the structure beneath the resist layer, the chemical composition and / or solid dimensions of the resist layer, etc. A fifth category can be the time-varying characteristics of one or more variables in the patterning process. Examples of this category include characteristics of high-frequency platform movement (e.g., frequency, amplitude, etc.), high-frequency laser bandwidth changes (e.g., frequency, amplitude, etc.), and / or high-frequency laser wavelength changes. These high-frequency changes or movements are high-frequency changes or movements that exceed the response time of the mechanism used to adjust the fundamental variables (e.g., platform position, laser intensity). The sixth category can be characteristics of upstream or downstream processes in lithography equipment, such as spin coating, post-exposure baking (PEB), development, etching, deposition, doping, and / or encapsulation.

[0039] As will be understood, many (if not all) of these variables can influence, and often are, the parameters of interest in the patterning process. Non-limiting examples of parameters for the patterning process may include critical dimension (CD), critical dimension uniformity (CDU), focus, overlap, edge position or placement, sidewall angles, pattern shift, etc. Often, these parameters represent the error of deviation from nominal values ​​(e.g., design values, averages, etc.). The parameter values ​​may be values ​​of characteristics of multiple individual patterns, or statistical values ​​(e.g., averages, variances, etc.) of characteristics of a set of patterns.

[0040] Some or all of the values ​​of the processing variables, or the values ​​of related parameters, can be determined by suitable methods. For example, these values ​​can be determined based on data obtained using various measurement tools (e.g., substrate measurement tools). These values ​​can also be obtained from various sensors or systems in the equipment used in the patterning process (e.g., sensors in a lithography apparatus (such as level sensors or alignment sensors), a lithography apparatus control system (e.g., a substrate or patterning stage control system), sensors in a track tool or coating and developing system tool, etc.). These values ​​can also originate from the operator of the patterning process.

[0041] Figure 2 This is a block diagram of an exemplary system 200 for performing SMO pattern selection according to embodiments of the present disclosure. In some embodiments, system 200 includes a diffraction order (DO) pattern generation unit 210, a DO pattern processing unit 220, a DO grouping unit 230, a pattern selection unit 240, and an SMO design target optimization unit 250. In some embodiments, the design layout to be optimized for the lithography process (e.g., including a layout in a standard digital format such as OASIS, GDSII, etc.) may include memory, test patterns, and logic. Based on this design layout, an initial large set of target patterns (fragments) is identified. In some embodiments, the entire set of fragments is extracted, representing all complex patterns in the design layout (e.g., about 50 to 1000 fragments, or any suitable number of fragments). It will be understood that DO patterns are disclosed as various types or formats of DO data or DO information, which are non-limiting examples. The DO information / data may be in any suitable format or representation without departing from the scope of the present disclosure, such as graphs or tables.

[0042] In some embodiments, the initial larger set of fragments may be provided a priori based on known key feature regions in the design layout used for image optimization. In some embodiments, the initial larger set of fragments may be extracted from the entire design layout using automated (e.g., machine vision) or manual algorithms that identify the key feature regions. In some embodiments, the larger set of patterns may include different pattern types, such as door or logic patterns, or may include patterns with various orientations. In some embodiments, the larger set of patterns may include 1D patterns or 2D patterns. In some embodiments, the larger set of patterns may include periodic patterns, non-periodic patterns, or isometric isolated patterns, including but not limited to line spacing (i.e., row spacing), line ends, contact features, angular features, or any type of feature.

[0043] In some embodiments, the larger set of patterns may include patterns of a certain level of complexity, or patterns that require special attention or verification during the photolithography process. For example, the patterns may include test structures conforming to design rules, such as 1D through-spacing, staggered through-spacing, commonly used design features or primitives (e.g., bends, T-shapes, H-shapes), reusable layout structures, such as memory cells (e.g., brick walls), memory perimeter structures (e.g., hooks for memory cells), or patterns with known imaging problems from previous generations. In some embodiments, the larger set of patterns may include patterns with predetermined process window performance, or patterns that include sensitivity to changes in process parameters.

[0044] In some embodiments, a small subset of patterns or fragments (e.g., 15 to 50 fragments, or any other suitable number) is selected from the initial large set of fragments. As explained below, the selection of this subset of patterns or fragments is performed such that the process window of the selected patterns matches the process window of the larger set of key patterns as closely as possible. The effectiveness of the selection can be measured by the combined pattern selection and the reduction in overall turnaround time or runtime in subsequent SMO processes.

[0045] This disclosure is not limited to any specific method, process, implementation, or algorithm for generating DO images. In some embodiments, DO image generation component 210 generates DO images of the initial large set of segments, wherein each DO image corresponds to a design pattern in a segment included in the initial large set of segments. In some embodiments, test patterns may be provided to these segments by a customer. In some embodiments, these segments may be provided from the design layout. In some embodiments, DO image generation component 210 generates the DO images from the SMO based on Fourier transform theory (e.g., 1D or 2D Fourier transform) to render the image based on the target polygon in the frequency domain. In some embodiments, the DO peaks are centrally symmetric due to the centrosymmetric property of the Fourier transform |F(ωx,ωy)|=|F(-ωx,-ωy)|. In some embodiments, the size of the DO image is 201×201 pixels, or other suitable size. In some embodiments, for array patterns, the DO images comprise discrete peaks in the frequency space. In some embodiments, for non-array patterns (e.g., 2D aperiodic patterns as disclosed herein), the DO images comprise discrete or continuous peaks in the frequency domain.

[0046] In some embodiments, the DO image processing unit 220 processes the generated DO image via one or more image processing steps. Figure 3The figure illustrates a plurality of sub-components included in the DO image processing component 220 according to an embodiment of the present disclosure, including but not limited to a normalization and grayscale sub-component 310, a binarization sub-component 320, and a peak center and contour extraction sub-component 330.

[0047] In some embodiments, the normalization and grayscale sub-component 310 normalizes the DO image. For example, the normalization and grayscale sub-component 310 normalizes the DO image based on the maximum and minimum intensities of the peaks and transforms the DO image into a grayscale image (e.g., based on grayscale values ​​from 0 to 255). In some embodiments, the zeroth order of the corresponding DO image is removed.

[0048] In some embodiments, the binarization sub-component 320 performs binarization of the grayscale image. Because the DO amplitude or intensity of these peaks can vary across different segments, normalization and binarization as disclosed herein can enhance the peak profile, such as with 2D aperiodic patterns (e.g., as...). Figure 4A The contours of the corresponding continuous peaks (as shown in the figure) are extracted to obtain the complete contours of these peaks in the corresponding DO image. In some embodiments, a roughly minimum threshold (e.g., G) is applied to all images. min Thresh Values ​​such as 20, 40, 60, or other appropriate values ​​are used to filter out weak signals that may be noise (e.g., signals with values ​​below G). min Thresh The value of the peak). Next, apply a segment-specific minimum threshold (e.g., C) corresponding to the intensity of the weakest peak of the segment. min Thresh This ensures that the weakest peak can be extracted. In some embodiments, the one or more thresholds correspond to the grayscale levels (e.g., between 0 and 255) and can be tuned. In some embodiments, the binarization sub-component 320 can binarize the grayscale image using any suitable binary function (e.g., the sigmoid function, etc.).

[0049] In some embodiments, the extracted sub-component 330 extracts the peak center and peak profile from the DO map in the fragment after binarization, as in an example 2D aperiodic pattern (e.g., Figures 4A to 4BThe peaks are shown in the continuous peaks. In some embodiments, the DO peaks of an array pattern or periodic pattern are discrete peaks, while the DO peaks of a non-array pattern or aperiodic pattern include continuous peaks. In some embodiments, the profile distribution in any direction is considered, but not limited to any particular direction (e.g., not limited to the x or y direction, four directions, or eight directions). Thus, the original peak profiles in all directions can be considered. In some embodiments, the actual (e.g., precise) peak profile can be used in one or more processes as disclosed herein, without approximation. Additionally, all DO peaks, including harmonic DO peaks, are extracted in this disclosure. As a result, the accuracy in determining peak coverage can be improved.

[0050] Figure 4A The diagram illustrates an embodiment of the present disclosure, and is related to... Figure 2 One or more components of system 200, or Figure 3 Example peaks corresponding to the non-periodic patterns in the DO diagram processed by the sub-components. Figure 4B The figure illustrates the extraction of peak centers and contours of example peaks corresponding to periodic and aperiodic patterns according to embodiments of the present disclosure. In some embodiments, such as Figure 4B As shown, the extraction sub-component 330 performs image processing to include neighboring pixels with similar intensities in the peak profile. In some embodiments, pixels in the binarized image have intensity values ​​of 0 or 1, and the extraction sub-component 330 processes the binarized image to include pixels with an intensity value of 1 in the peak profile. This can be achieved from, for example... Figure 4B The peak centers are extracted from the boundary regions of the peak profiles illustrated in the figure. In some embodiments, the zeroth order is removed because it is common in the DO graph, and half of these peaks are extracted, taking into account the central symmetry of the Fourier transform (e.g., through rotational symmetry). In some embodiments, peak profiles with a size greater than a certain number of pixels are classified as continuous peaks. This size limit for the profile area can be adjusted; for example, it can be set to 3 pixels. In some embodiments, other peaks with smaller profile areas are classified as discrete peaks.

[0051] refer to Figure 2In some embodiments, system 200 further includes a DO grouping component 230 for grouping peaks extracted and classified by extraction subcomponent 330, as disclosed above. In some embodiments, during the grouping process, these peaks are grouped by applying one or more grouping criteria (e.g., coverage criteria for determining whether one peak can cover another peak). Peaks that cover each other can be considered sufficiently similar to be placed in the same group according to the grouping criteria. In some embodiments, multiple grouping criteria (e.g., coverage criteria) (e.g., the three criteria disclosed below) can be applied to these peaks simultaneously, sequentially, or in any suitable combination to obtain coverage relationships between the peaks. For example, the extracted peaks are split into multiple peak pairs, or any other suitable number of groups or sets for comparison. The multiple grouping criteria can be applied to the corresponding peak pairs, or corresponding paired peaks, to determine for each peak pair whether a peak can be covered by another peak in the same pair to place the two peaks in the same group. All extracted patterns can be evaluated using the grouping criteria disclosed below. After the grouping process, a representative pattern can be selected from each group to reduce the number of patterns used for inspection or optimization and improve SMO yield.

[0052] Figure 5 The figure illustrates an example grouping criterion for grouping peaks in a DO diagram according to embodiments of the present disclosure. In some embodiments, under a first criterion, for a pair of discrete peaks, or a pair of discrete peaks and a continuous peak, the two peaks P1 and P2 can be grouped together. i The distance between the peak centers D(P1, P) i ) and the predetermined tolerance threshold, i.e., the predetermined tolerance threshold R blur Compare. For example, such as Figure 5 As shown in the example of applying standard 1, when D(P1,P) i )≤R blur When i = 2, 3, 4, etc., determine the two peaks P1 and P2. i There exists a covering relationship between the two peaks (e.g., one peak can be covered by the other). When D(P1,P i R blur Then, the two peaks P1 and P2 are determined. i There is no overlapping relationship between them. For example, such as Figure 5 As shown, the distances D(P1, P2) between the discrete peak "peak 1" and the continuous peak "peak 2", and the distances D(P1, P3) between the discrete peak "peak 1" and the discrete peak "peak 3" are both less than the tolerance threshold R. blur In other words, the peak centers of peaks 2 and 3 are included within a circle centered on the peak center of peak 1 and have a tolerance threshold, i.e., a tolerance threshold R.blur The radius of the peak is determined. As a result, peak 1 is found to cover both peaks 2 and 3. On the other hand, the distance D(P1, P4) between discrete peak 1 and discrete peak 4 is greater than the tolerance threshold R. blur Therefore, peak 1 cannot cover peak 4. In some embodiments, this limitation on the first criterion is considered in cases where a continuous peak cannot cover a discrete peak, since discrete peaks typically have greater intensity than continuous peaks. On the other hand, a discrete peak can cover a continuous peak or another discrete peak.

[0053] In some embodiments, R can be set based on the source rendering effect in SMO. blur For example, R blur It can be set to 3 DO image pixels (e.g., for a 201x201 pixel DO image, where 3 pixels = 0.03 sigma). It has a center distance less than R. blur A pair of peaks (e.g., 3 pixels) are considered to overlap each other and can be placed in the same group. R can be predefined or set by the user. blur In some embodiments, R blur It depends on the parameters of the irradiator of the scanner tool. For example, R blur This may involve a resolution limitation defined by the illuminator, such as half the pixel unit defined in the SMO pupil map. (R) blur Peaks that are closer to each other may not be distinguishable on the SMO pupil map and may therefore be considered to overlap each other in the same group.

[0054] In some embodiments, under the second criterion, for a pair of discrete peaks, if the two peaks are collinear and one peak has a frequency that is an integer multiple of the frequency of the other peak (e.g., for a resonant peak), for example... A peak with a larger frequency covers another peak with a smaller frequency; for example, P1 covers P2, as... Figure 5Examples of the application of Standard 2 are shown in the text. In some embodiments, the second standard can be applied to patterns with added subresolution auxiliary features, such as auxiliary gratings for enhancing isolated features in process windows, where the pattern with SRAF is considered to be covered by a pattern without added SRAF. This is because patterns with lower DO frequencies have larger spacing, and the SRAF generated after optical proximity correction (OPC) can increase the DO frequency (e.g., with smaller spacing) by multiple integer multiples. In some embodiments, the second standard can be applied to peaks that are collinear with each other, and can be applied to peaks in any orientation without limitation. That is, instead of being limited to peaks only in certain directions (e.g., only the x or y direction, only four directions (e.g., orthogonal), or only eight directions), the methods disclosed herein can be applied to peaks in any orientation (e.g., any direction, or an unrestricted direction). Thus, this disclosure can be applied to patterns with any orientation.

[0055] In some embodiments, under the third criterion, for a pair of consecutive peaks, a peak is determined (e.g., Figure 5 The question asks whether the center of peak 2 or peak 3 in the given pair is included within the contour of another peak (e.g., peak 1) in the corresponding pair. In some embodiments, the original peak contour can be considered in all directions, and the actual (e.g., precise) peak contour can be used without approximation. Suitable image processing methods, such as criteria for determining whether a point is inside a polygon, can be used. For example, as... Figure 5 The illustration of the "rule for determining whether a point is inside a polygon" shows that, to determine whether a point lies inside a polygon, a ray is drawn from the point in any direction. If the number of edges the point crosses in the polygon is odd (e.g., ...), ... Figure 5 If the number is even (e.g., 3), then the point is located inside the polygon. Figure 5 If 4) in the equation, then the point is located outside the polygon. In some embodiments, the third criterion can be applied to peaks in any direction, for example, an unrestricted direction, or any direction without restriction.

[0056] Such as application Figure 5As illustrated by the example of the third criterion and judgment principle, when determining whether a peak center falls inside a peak profile (e.g., using a polygonal profile as an example), a ray can be drawn from the peak center of peak 2 or peak 3 in any direction (e.g., in any direction, without restriction). The number of times the ray intersects the polygonal profile of peak 1 is counted. If the number of times the ray intersects one or more edges of the polygonal profile of peak 1 is odd (e.g., 1 time), then it is determined that the peak center of peak 2 is inside the peak profile of peak 1, and peak 1 covers peak 2. If this number is even (e.g., 2 times), then it is determined that the peak center of peak 3 is outside the peak profile of peak 1, and peak 1 does not cover peak 3.

[0057] refer to Figure 2 In some embodiments, system 200 further includes a pattern selection component 240 for selecting key patterns based on key representative peaks. Figure 6 This is a process flowchart illustrating an example process 600 for pattern selection according to some embodiments of the present disclosure. In some embodiments, by Figure 2 The pattern selection component 240 can be used to perform one or more steps.

[0058] In step 610, groups can be formed for the corresponding peaks, and each peak can be considered as a representative peak in the corresponding group. Then, for each group, a coverage criterion (e.g., based on...) can be applied. Figure 5The three coverage criteria (e.g., the peaks covered by the corresponding representative peak) allow other peaks to be placed in the same group (as associated with the representative peak). Therefore, all peaks covered by the representative peak will be placed in the corresponding group. In step 620, the largest group (e.g., the one with the largest number of peaks) among all existing groups can be identified, and the key representative peak can be selected. In step 630, repeating peaks covered in multiple groups can be merged. In some embodiments, after the grouping process in step 610, there are multiple peaks that can be grouped into different groups as repeating peaks. In some embodiments, pattern selection can be performed to select the minimum number of patterns that can cover all diffraction order (DO) identifiers or as many DO identifiers as possible (e.g., all peaks or as many peaks as possible). Therefore, the process can start from the largest group (e.g., the one with the largest number of peaks) to remove repeating peaks included in other groups. For example, if the representative peak of the corresponding candidate group is already covered by other peaks, such as those included in the largest group, then all peaks in this candidate group can be added to this largest group, and this candidate group can be completely removed. Furthermore, repeating peaks within a group can also be merged. Next, in step 640, it is determined whether all peaks have been removed from other groups (e.g., do any other groups include peaks after removal?). If there are peaks that have not yet been removed (“No”), process 600 proceeds to step 620 to identify the next largest group and repeats steps 620, 630, and 640. If all peaks have been removed (“Yes”), process 600 proceeds to step 650, where a key pattern corresponding to the key representative peak is selected (e.g., a key pattern that generates the key representative peak). During pattern selection, the key representative peak may include multiple peaks that cover the most other peaks, or multiple unique peaks that are not yet covered by any other peaks. After the pattern selection, the pattern corresponding to the aforementioned key representative peak is selected as the key pattern.

[0059] Figure 7 This is a process flowchart illustrating an example process 700 for optimizing SMO design goals according to some embodiments of this disclosure. In some embodiments, by Figure 2 The SMO design goal optimization component 250 is used to perform one or more steps.

[0060] In some embodiments, steps 702 and 704 are generally similar to processes performed by one or more components of system 200, such as DO diagram generation component 210, DO diagram processing component 220, DO grouping component 230, or pattern selection component 240 as disclosed herein. For example, in step 702, for optimization in process 700, a set of fragments (e.g., more than 100 fragments) adopting / having the original design rules is obtained. In step 704, a subset of key patterns including key representative peaks can be intelligently selected from the set of fragments as disclosed herein. In step 706, SMO can be performed (e.g., in...) Figure 6 The selected key pattern obtained in step 650 may include, for example, 20 to 50 key fragments of SRAM features selected from a set of more than 1,000 fragments. In some embodiments, during optimization, the source and mask are considered as variables in step 706 to obtain an optimized source and optimized mask design.

[0061] In step 708, mask optimization (MO) verification can be performed. Verification shows that the performance based on selected key segments (e.g., process window, depth of focus (DOF)) is substantially similar to (or substantially no worse than / substantially no worse than) the performance based on the original set of more than 1000 segments. Furthermore, one or more limiting factors or limiters corresponding to one or more features that restrict performance improvement can be identified, including but not limited to edge placement error (EPE), pattern placement error (PPE), CD, or spacing under legacy design rules. In some embodiments, MO includes optical proximity effect correction (OPC) and sub-resolution auxiliary feature (SRAF) techniques, and is performed on all patterns to obtain the process window, mask error enhancement factor (MEEF), and mask complexity.

[0062] In step 710, layout relocation rules can be optimized for the identified constraints to enhance lithography image performance. For example, for SRAM features, variables can be assigned to different constraints identified in step 708 (such as edge placement error (EPE), pattern placement error (PPE), CD, or spacing), and these variables can be optimized in combination to achieve improved performance based on a process window. The optimized variables for the constraints correspond to new and optimized layout relocation rules. In some embodiments, the optimized layout relocation rules obtained in step 710 can be applied to the original design goal in step 708 (e.g., all segments) to generate a new set of segments in step 710.

[0063] In step 712, key representative segments can be selected, and another SMO process can be performed on the selected key patterns and a new set of segments (e.g., design target optimized (DTO) segments obtained in step 710 based on the new optimized layout relocation rules) to obtain optimized sources. In some embodiments, in step 712, sources (e.g., illumination sources), masks (e.g., mask design layouts), and targets (e.g., layout relocation rules) can be jointly optimized. In step 714, the sources and masks for the key segments can be optimized based on the targets optimized in step 712. In step 716, the DTO can be performed on the remaining parts of the logic segments using the sources optimized in step 714, for example, to optimize the masks and targets. In step 718, it is determined whether hotspots still exist after the DTO. For example, it is determined whether one or more hotspots can be identified among the plurality of patterns on the design layout. If there are no more hotspots (e.g., "No"), then in step 720, all logic segments can be optimized using design targets with new layout relocation rules (e.g., full-chip optimization). If hotspots still exist (e.g., "yes"), process 700 proceeds to step 704 to perform pattern selection based on the DO graph. Steps 704 through 718 disclosed herein are further repeated until at step 718 up to a predetermined threshold number of hotspots can be identified. In some embodiments, after full-chip optimization based on new layout redirection rules, performance improvements (e.g., windowing process, DOF) superior to previous performance can be demonstrated.

[0064] Figure 8 This is a process flowchart illustrating an example method 800 for selecting a subset of key patterns from a plurality of patterns in a design layout, according to some embodiments of the present disclosure. In some embodiments, by Figure 2 One or more components of system 200 in Figure 3 The system 300 uses one or more components to perform one or more steps.

[0065] In step 810, diffraction order data, such as multiple diffraction pattern diagrams or in any other suitable format or representation, can be acquired. In some embodiments, the diffraction order data is obtained based on the multiple patterns representing features to be formed on at least a portion of a wafer. The diffraction order data or information can be generated using any suitable manner, algorithm, method, etc., without departing from the scope of this disclosure. The diffraction order data or information can be provided by a separate, i.e., individual module or software product independent of the system disclosed herein. The diffraction order data or information can also be generated by a module or component of a system as disclosed herein. In some embodiments, the multiple diffraction pattern diagrams include multiple peaks corresponding to the multiple patterns. In some embodiments, such as Figure 4AAs shown, the DO graph generation unit 210 can perform Fourier transforms on the plurality of patterns to generate the plurality of DO graphs in the frequency domain, respectively. In some embodiments, the plurality of patterns include periodic patterns or aperiodic patterns. In some embodiments, the plurality of DO graphs are processed to identify the plurality of peaks. For example, as... Figure 3 and Figures 4A to 4B As shown, the normalization and grayscale sub-component 310 normalizes the DO image and assigns grayscale values. The binarization sub-component 320 can binarize the DO image to sharpen the image contours. The extraction sub-component 330 can extract peak centers and peak contours, such as... Figures 4A to 4B These peaks can be classified as discrete peaks and continuous peaks. In this disclosure, peaks in any direction are considered without any limitation.

[0066] In step 820, the DO grouping component 230 may identify a subset of the representative peaks that cover other peaks among the plurality of peaks based on one or more grouping criteria. In some embodiments, the one or more grouping criteria are associated with an evaluation of the relative positions of the plurality of peaks in the plurality of DO plots. The plurality of peaks are split into multiple pairs. Figure 5 As shown, in some embodiments, for a pair of discrete peaks, a first representative peak (discrete peak 1 under criterion 2) can be identified as covering one or more other peaks (e.g., discrete peaks) collinear with the first representative peak, and the first representative peak has a frequency that is an integer multiple of the frequency of the one or more other peaks (e.g., resonant peaks). In some embodiments, for a pair of discrete peaks, or a pair of discrete peaks and a continuous peak, a second representative peak (e.g., peak 1 under criterion 1) can be identified as covering one or more other peaks whose peak centers are located at a predetermined distance (e.g., R) from the peak center of the second representative peak. blur Within. In some embodiments, for a pair of discrete peaks, a third representative peak (e.g., peak 3 under standard 3) can be identified as covering one or more other peaks having a peak center located within the peak profile of the third representative peak.

[0067] In step 830, the pattern selection component 240 may select a subset of key patterns based on a subset of the representative peaks selected in step 820. In some embodiments, the subset of key patterns is provided for at least one of optical proximity correction (OPC), defect inspection, defect prediction, or source mask optimization (SMO).

[0068] In some embodiments, the SMO design target optimization component 250 may perform an SMO design target optimization process on the selected key pattern obtained in step 830. For example, limiting factors under the old design rules can be identified, and the design rules can be adjusted accordingly to improve lithographic imaging performance.

[0069] By using the methods disclosed herein, any type of feature with any pattern (including 2D aperiodic patterns) can be efficiently and accurately processed and grouped to identify representative peaks of the aperiodic pattern. Furthermore, DO peaks located in any or any arbitrary direction are considered, without any restrictions on peak direction (e.g., only the x and y directions, or the eight directions in conventional methods) or pattern orientation. Additionally, various criteria are used simultaneously to determine the coverage relationships between DO peaks. As a result, the accuracy of pattern selection can be improved, and fewer critical patterns are selected compared to conventional methods, while simultaneously providing sufficient coverage of multiple patterns across the entire chip to deliver performance similar to conventional methods. Therefore, runtime can be reduced (e.g., by 30% to 60% compared to conventional methods), and yield can be improved without compromising performance. For example, running an SMO with complex patterns on 2D aperiodic patterns can be time-consuming. By accurately analyzing and grouping 2D aperiodic patterns as disclosed herein, SMO runtime can be significantly reduced. In addition, the target pattern optimization disclosed herein includes performing a second optimization on the selected pattern to further improve lithography performance gain and reduce SMO turnaround time.

[0070] A non-transitory computer-readable medium may be provided, which stores information for use by the system (e.g., Figure 2 The processor or component (e.g., System 200) of the system 200 Figure 3The instructions of component 220) are, among other things, specifically performed DO image generation, DO image processing, DO grouping, pattern grouping, peak selection, pattern selection, SMO, SMO design target optimization, mask optimization, source optimization, image inspection, image acquisition, image transformation, image processing, image comparison, platform positioning, beam focusing, electric field adjustment, beam bending, condenser lens adjustment, activation of charged particle sources, and beam deflection, as described above with respect to process 600, process 700, or method 800. Common forms of non-transitory media include, for example, floppy disks, floppy disks, hard disks, solid-state drives, magnetic tapes, or any other magnetic data storage media, optical disc read-only memory (CD-ROM), any other optical data storage media, any physical media with a perforated pattern, random access memory (RAM), programmable read-only memory (PROM) and erasable programmable read-only memory (EPROM), flash erasable programmable read-only memory (FLASH-EPROM), or any other flash memory, non-volatile random access memory (NVRAM), cache, registers, any other memory chip or cassette memory, and their network versions.

[0071] The embodiments can also be described using the following aspects:

[0072] 1. A method for selecting a subset of key patterns from a plurality of patterns in a design layout, the method comprising:

[0073] Obtain diffraction order data based on the plurality of patterns representing features to be formed on at least a portion of a wafer, the diffraction order data including a plurality of peaks corresponding to the plurality of patterns;

[0074] Identifying a subset of representative peaks from the plurality of peaks based on one or more grouping criteria, including:

[0075] Identify a first representative peak in a subset of representative peaks, the first representative peak covering another peak collinear with the first representative peak, wherein the first representative peak is a discrete peak having a frequency that is an integer multiple of the frequency of the other discrete peak; and select a subset of key patterns corresponding to the subset of representative peaks.

[0076] 2. The method according to aspect 1 further includes:

[0077] Generate the diffraction order data, which includes multiple diffraction order patterns; and

[0078] Perform Fourier transform on the plurality of patterns to generate the plurality of diffraction pattern in the frequency domain, respectively.

[0079] 3. The method according to any one of aspects 1 to 2, wherein the plurality of patterns comprises one or more non-periodic patterns.

[0080] 4. The method according to any one of aspects 2 to 3, further comprising:

[0081] The multiple diffraction pattern is processed by normalization and grayscale conversion.

[0082] 5. The method according to aspect 4 further includes:

[0083] The multiple diffraction patterns are processed by binarization to identify the multiple peaks.

[0084] 6. The method according to any one of aspects 1 to 5, further comprising:

[0085] Extract the peak centers and peak profiles of the multiple peaks; and

[0086] The multiple peaks are classified into discrete peaks and continuous peaks.

[0087] 7. The method according to any one of aspects 2 to 6, wherein the plurality of peaks are located in various directions on the diffraction pattern without limitation.

[0088] 8. The method according to any one of aspects 1 to 7, wherein identifying a subset of representative peaks from the plurality of peaks based on one or more grouping criteria further comprises:

[0089] Identify a second representative peak in a subset of representative peaks, the second representative peak covering one or more other peaks having corresponding peak centers located within a predetermined distance from the peak center of the first representative peak, wherein the second representative peak is a discrete peak, and the one or more peaks covered by the second representative peak include discrete peaks or continuous peaks.

[0090] 9. The method according to any one of aspects 1 to 8, wherein identifying a subset of representative peaks from the plurality of peaks according to one or more grouping criteria comprises:

[0091] Identify a third representative peak in a subset of representative peaks, the third representative peak covering one or more other peaks having peak centers located within the peak profile of the third representative peak, wherein the third representative peak is a continuous peak and located in an unrestricted direction, and the one or more peaks covered by the third representative peak are continuous.

[0092] 10. The method according to any one of aspects 1 to 9, wherein the peak profile is an actual profile without approximation.

[0093] 11. The method according to any one of aspects 1 to 10, further comprising:

[0094] Perform the first SMO on a subset of the key patterns;

[0095] Perform first mask optimization verification to identify one or more feature constraints;

[0096] The layout redirection rules are optimized based on the one or more identified feature constraints, and a set of fragments is generated based on the optimized layout redirection rules.

[0097] Perform a second SMO on a subset of the key pattern and the resulting set of fragments to obtain an optimized source;

[0098] A second mask optimization verification is performed on the plurality of patterns based on the adjusted layout redirection rules, with optimized sources and optimized targets;

[0099] Determine whether one or more hotspots are identified in the design layout; and

[0100] Based on the determination that no hotspots were identified, optimized source and optimized layout redirection rules were used for the full-chip design.

[0101] 12. The method according to any one of aspects 1 to 11, wherein the design layout is in Graphic Database System (GDS) format, Graphic Database System II (GDS II) format, Open Artwork System Interchange Standard (OASIS) format, or Caltech Intermediate Format (CIF).

[0102] 13. The method according to any one of aspects 1 to 12, wherein a subset of the key pattern is provided for at least one of optical proximity correction (OPC), defect inspection, defect prediction, or source mask optimization (SMO).

[0103] 14. A system comprising:

[0104] Memory, the memory storing instruction sets; and

[0105] At least one processor, the at least one processor being configured to execute the set of instructions to cause the system to perform:

[0106] Obtain diffraction order data based on the plurality of patterns representing features to be formed on at least a portion of a wafer, the diffraction order data including a plurality of peaks corresponding to the plurality of patterns;

[0107] Identifying a subset of representative peaks from the plurality of peaks based on one or more grouping criteria, including:

[0108] Identify a first representative peak in a subset of representative peaks, the first representative peak covering another peak collinear with the first representative peak, wherein the first representative peak is a discrete peak having a frequency that is an integer multiple of the frequency of the other discrete peak; and

[0109] Select a subset of key patterns that correspond to a subset of representative peaks.

[0110] 15. The system according to aspect 14, wherein the at least one processor is further configured to cause the system to perform:

[0111] The diffraction order data, which includes multiple diffraction order patterns, is generated.

[0112] Perform Fourier transform on the plurality of patterns to generate the plurality of diffraction pattern in the frequency domain, respectively.

[0113] 16. The system according to any one of aspects 14 to 15, wherein the plurality of patterns comprises one or more non-periodic patterns.

[0114] 17. The system according to any one of aspects 15 to 16, wherein the at least one processor is further configured to cause the system to perform:

[0115] The multiple diffraction pattern is processed by normalization and grayscale conversion.

[0116] 18. The system according to aspect 17, wherein the at least one processor is further configured to cause the system to perform:

[0117] The multiple diffraction patterns are processed by binarization to identify the multiple peaks.

[0118] 19. The system according to any one of aspects 14 to 18, wherein the at least one processor is further configured to cause the system to perform:

[0119] Extract the peak centers and peak profiles of the multiple peaks; and

[0120] The multiple peaks are classified into discrete peaks and continuous peaks.

[0121] 20. The system according to any one of aspects 15 to 19, wherein the plurality of peaks are located in various directions on the diffraction pattern without limitation.

[0122] 21. The system according to any one of aspects 14 to 20, wherein identifying a subset of representative peaks from the plurality of peaks according to one or more grouping criteria comprises:

[0123] Identify a second representative peak in a subset of representative peaks, the second representative peak covering one or more other peaks having corresponding peak centers located within a predetermined distance from the peak center of the first representative peak, wherein the second representative peak is a discrete peak, and the one or more peaks covered by the second representative peak include discrete peaks or continuous peaks.

[0124] 22. The system according to any one of aspects 14 to 21, wherein identifying a subset of representative peaks from the plurality of peaks according to one or more grouping criteria comprises:

[0125] Identify a third representative peak in a subset of representative peaks, the third representative peak covering one or more other peaks having peak centers located within the peak profile of the third representative peak, wherein the third representative peak is a continuous peak and located in an unrestricted direction, and the one or more peaks covered by the third representative peak are continuous.

[0126] 23. The system according to any one of aspects 14 to 22, wherein the peak profile is an actual profile without approximation.

[0127] 24. The system according to any one of aspects 14 to 23, wherein the at least one processor is further configured to cause the system to perform:

[0128] Perform the first SMO on a subset of the key patterns;

[0129] Perform first mask optimization verification to identify one or more feature constraints;

[0130] The layout redirection rules are optimized based on the identified one or more feature constraints, and a set of fragments is generated based on the optimized layout redirection rules; and

[0131] Perform a second SMO on a subset of the key pattern and the resulting set of fragments to obtain an optimized source;

[0132] A second mask optimization verification is performed on the plurality of patterns based on the adjusted layout redirection rules, with optimized sources and optimized targets;

[0133] Determine whether one or more hotspots are identified in the design layout; and

[0134] Based on the determination that no hotspots were identified, optimized source and optimized layout redirection rules were used for the full-chip design.

[0135] 25. The system according to any one of aspects 14 to 24, wherein the design layout is in Graphic Database System (GDS) format, Graphic Database System II (GDS II) format, Open Artwork System Interchange Standard (OASIS) format, or Caltech Intermediate Format (CIF).

[0136] 26. The system according to any one of aspects 14 to 25, wherein a subset of the key pattern is provided for at least one of optical proximity correction (OPC), defect inspection, defect prediction, or source mask optimization (SMO).

[0137] 27. A non-transitory computer-readable medium storing a set of instructions executable by at least one processor of a system to cause the system to perform a method for selecting a subset of key patterns from a plurality of patterns of a design layout, the method comprising:

[0138] Obtain diffraction order data based on the plurality of patterns representing features to be formed on at least a portion of a wafer, the diffraction order data including a plurality of peaks corresponding to the plurality of patterns;

[0139] Identifying a subset of representative peaks from the plurality of peaks based on one or more grouping criteria, including:

[0140] Identify a first representative peak in a subset of representative peaks, the first representative peak covering another peak collinear with the first representative peak, wherein the first representative peak is a discrete peak having a frequency that is an integer multiple of the frequency of the other discrete peak; and select a subset of key patterns corresponding to the subset of representative peaks.

[0141] 28. The non-transitory computer-readable medium according to aspect 27, wherein the instruction set is executable by the at least one processor of the system to cause the system to further perform:

[0142] Generate the diffraction order data, which includes multiple diffraction order patterns; and

[0143] Perform Fourier transform on the plurality of patterns to generate the plurality of diffraction pattern in the frequency domain, respectively.

[0144] 29. The non-transitory computer-readable medium according to any one of aspects 27 to 28, wherein the plurality of patterns comprises one or more non-periodic patterns.

[0145] 30. The non-transitory computer-readable medium according to any one of aspects 28 to 29, wherein the instruction set is executable by the at least one processor of the system to cause the system to further perform:

[0146] The multiple diffraction pattern is processed by normalization and grayscale conversion.

[0147] 31. The non-transitory computer-readable medium according to aspect 30, wherein the instruction set is executable by the at least one processor of the system to cause the system to further perform:

[0148] The multiple diffraction patterns are processed by binarization to identify the multiple peaks.

[0149] 32. The non-transitory computer-readable medium according to any one of aspects 28 to 31, wherein the instruction set is executable by the at least one processor of the system to cause the system to further perform:

[0150] Extract the peak centers and peak profiles of the multiple peaks; and

[0151] The multiple peaks are classified into discrete peaks and continuous peaks.

[0152] 33. The non-transitory computer-readable medium according to any one of aspects 27 to 32, wherein the plurality of peaks are located in various orientations on a diffraction pattern without limitation.

[0153] 34. The non-transitory computer-readable medium according to any one of aspects 27 to 33, wherein identifying a subset of representative peaks from the plurality of peaks according to one or more grouping criteria comprises:

[0154] Identify a second representative peak in a subset of representative peaks, the second representative peak covering one or more other peaks having corresponding peak centers located within a predetermined distance from the peak center of the first representative peak, wherein the second representative peak is a discrete peak, and the one or more peaks covered by the second representative peak include discrete peaks or continuous peaks.

[0155] 35. The non-transitory computer-readable medium according to any one of aspects 27 to 34, wherein identifying a subset of representative peaks from the plurality of peaks according to one or more grouping criteria comprises:

[0156] Identify a third representative peak in a subset of representative peaks, the third representative peak covering one or more other peaks having peak centers located within the peak profile of the third representative peak, wherein the third representative peak is a continuous peak and located in an unrestricted direction, and the one or more peaks covered by the third representative peak are continuous.

[0157] 36. The non-transitory computer-readable medium according to any one of aspects 27 to 35, wherein the peak profile is an actual profile without approximation.

[0158] 37. The non-transitory computer-readable medium according to any one of aspects 27 to 36, wherein the instruction set is executable by the at least one processor of the system to cause the system to further perform:

[0159] Perform the first SMO on a subset of the key patterns;

[0160] Perform first mask optimization verification to identify one or more feature constraints;

[0161] The layout redirection rules are optimized based on the identified one or more feature constraints, and a set of fragments is generated based on the optimized layout redirection rules; and

[0162] Perform a second SMO on a subset of the key pattern and the resulting set of fragments to obtain an optimized source;

[0163] A second mask optimization verification is performed on the plurality of patterns based on the adjusted layout redirection rules, with optimized sources and optimized targets;

[0164] Determine whether one or more hotspots are identified in the design layout; and

[0165] Based on the determination that no hotspots were identified, optimized source and optimized layout redirection rules were used for the full-chip design.

[0166] 38. The non-transitory computer-readable medium according to any one of aspects 27 to 37, wherein the design layout is in Graphic Database System (GDS) format, Graphic Database System II (GDS II) format, Open Artwork System Interchange Standard (OASIS) format, or Caltech Intermediate Format (CIF).

[0167] 39. The non-transitory computer-readable medium according to any one of aspects 27 to 38, wherein a subset of the key pattern is provided for at least one of optical proximity correction (OPC), defect inspection, defect prediction, or source mask optimization (SMO).

[0168] 40. A method for performing design objective optimization, the method comprising:

[0169] Perform first source mask optimization (SMO) on a subset of the key patterns of multiple patterns in the design layout;

[0170] Optimize layout redirection rules based on one or more feature constraints;

[0171] A set of fragments is generated based on optimized layout redirection rules; and

[0172] A second SMO is performed on a subset of the key pattern and the resulting set of fragments to obtain an optimized source.

[0173] 41. The method according to aspect 40 further includes:

[0174] Perform a first mask optimization verification to identify one or more feature constraints.

[0175] 42. The method according to aspect 41 further includes:

[0176] A second mask optimization verification is performed on the plurality of patterns based on the optimized layout redirection rules, with optimized source and optimized target.

[0177] 43. The method according to aspect 42 further includes:

[0178] Determine whether one or more hotspots are identified in the design layout; and

[0179] Based on the determination that no hotspots were identified, optimized source and optimized layout redirection rules were used for the full-chip design.

[0180] 44. The method according to any one of aspects 40 to 43, further comprising selecting a subset of key patterns of the plurality of patterns, including:

[0181] Obtain diffraction order data including multiple peaks corresponding to the multiple patterns;

[0182] Identify a representative subset of peaks from the plurality of peaks based on one or more grouping criteria; and

[0183] Select a subset of key patterns that correspond to a subset of representative peaks.

[0184] 45. The method according to aspect 44, wherein identifying a subset of representative peaks from the plurality of peaks based on one or more grouping criteria further comprises:

[0185] Identify a first representative peak in a subset of representative peaks, the first representative peak covering another peak collinear with the first representative peak, wherein the first representative peak is a discrete peak having a frequency that is an integer multiple of the frequency of the other discrete peak.

[0186] 46. ​​The method according to aspect 44, wherein identifying a subset of representative peaks from the plurality of peaks based on one or more grouping criteria further comprises:

[0187] Identify a second representative peak in a subset of representative peaks, the second representative peak covering one or more other peaks having corresponding peak centers located within a predetermined distance from the peak center of the first representative peak, wherein the second representative peak is a discrete peak, and the one or more peaks covered by the second representative peak include discrete peaks or continuous peaks.

[0188] 47. The method according to aspect 44, wherein identifying a subset of representative peaks from the plurality of peaks based on one or more grouping criteria further comprises:

[0189] Identify a third representative peak in a subset of representative peaks, the third representative peak covering one or more other peaks having peak centers located within the peak profile of the third representative peak, wherein the third representative peak is a continuous peak and located in an unrestricted direction, and the one or more peaks covered by the third representative peak are continuous.

[0190] 48. A system comprising:

[0191] Memory, the memory storing instruction sets; and

[0192] At least one processor, the at least one processor being configured to execute the set of instructions to cause the system to perform:

[0193] Perform first source mask optimization (SMO) on a subset of the key patterns of multiple patterns in the design layout;

[0194] Optimize layout redirection rules based on one or more feature constraints;

[0195] A set of fragments is generated based on optimized layout redirection rules; and

[0196] A second SMO is performed on a subset of the key pattern and the resulting set of fragments to obtain an optimized source.

[0197] 49. The system according to aspect 48, wherein the at least one processor is further configured to cause the system to perform:

[0198] Perform a first mask optimization verification to identify one or more feature constraints.

[0199] 50. The system according to aspect 49, wherein the at least one processor is further configured to cause the system to perform:

[0200] A second mask optimization verification is performed on the plurality of patterns based on the optimized layout redirection rules, with optimized source and optimized target.

[0201] 51. The system according to aspect 50, wherein the at least one processor is further configured to cause the system to perform:

[0202] Determine whether one or more hotspots are identified in the design layout; and

[0203] Based on the determination that no hotspots were identified, optimized source and optimized layout redirection rules were used for the full-chip design.

[0204] 52. The system according to any one of aspects 48 to 51, wherein the at least one processor is further configured to cause the system to perform:

[0205] Select a subset of the key patterns from the plurality of patterns, including:

[0206] Obtain diffraction order data including multiple peaks corresponding to the multiple patterns;

[0207] Identify a representative subset of peaks from the plurality of peaks based on one or more grouping criteria; and

[0208] Select a subset of key patterns that correspond to a subset of representative peaks.

[0209] 53. The system according to aspect 52, wherein identifying a subset of representative peaks from the plurality of peaks based on one or more grouping criteria further comprises:

[0210] Identify a first representative peak in a subset of representative peaks, the first representative peak covering another peak collinear with the first representative peak, wherein the first representative peak is a discrete peak having a frequency that is an integer multiple of the frequency of the other discrete peak.

[0211] 54. The system according to aspect 52, wherein identifying a subset of representative peaks from the plurality of peaks based on one or more grouping criteria further comprises:

[0212] Identify a second representative peak in a subset of representative peaks, the second representative peak covering one or more other peaks having corresponding peak centers located within a predetermined distance from the peak center of the first representative peak, wherein the second representative peak is a discrete peak, and the one or more peaks covered by the second representative peak include discrete peaks or continuous peaks.

[0213] 55. The system according to aspect 52, wherein identifying a subset of representative peaks from the plurality of peaks based on one or more grouping criteria further comprises:

[0214] Identify a third representative peak in a subset of representative peaks, the third representative peak covering one or more other peaks having peak centers located within the peak profile of the third representative peak, wherein the third representative peak is a continuous peak and located in an unrestricted direction, and the one or more peaks covered by the third representative peak are continuous.

[0215] 56. A non-transitory computer-readable medium storing a set of instructions executable by at least one processor of a system to cause the system to perform a method for optimizing a design objective, the method comprising:

[0216] Perform first source mask optimization (SMO) on a subset of the key patterns of multiple patterns in the design layout;

[0217] Optimize layout redirection rules based on one or more feature constraints;

[0218] A set of fragments is generated based on optimized layout redirection rules; and

[0219] A second SMO is performed on a subset of the key pattern and the resulting set of fragments to obtain an optimized source.

[0220] 57. The non-transitory computer-readable medium according to aspect 56, wherein the set of instructions is executable by the at least one processor of the system to cause the system to further perform:

[0221] Perform a first mask optimization verification to identify one or more feature constraints.

[0222] 58. The non-transitory computer-readable medium according to aspect 57, wherein the set of instructions is executable by the at least one processor of the system to cause the system to further perform:

[0223] A second mask optimization verification is performed on the plurality of patterns based on the optimized layout redirection rules, with optimized source and optimized target.

[0224] 59. The non-transitory computer-readable medium according to aspect 58, wherein the set of instructions is executable by the at least one processor of the system to cause the system to further perform:

[0225] Determine whether one or more hotspots are identified in the design layout; and

[0226] Based on the determination that no hotspots were identified, optimized source and optimized layout redirection rules were used for the full-chip design.

[0227] 60. The non-transitory computer-readable medium according to any one of aspects 56 to 59, wherein the set of instructions is executable by the at least one processor of the system to cause the system to further perform:

[0228] Select a subset of the key patterns from the plurality of patterns, including:

[0229] Obtain diffraction order data including multiple peaks corresponding to the multiple patterns;

[0230] Identify a representative subset of peaks from the plurality of peaks based on one or more grouping criteria; and

[0231] Select a subset of key patterns that correspond to a subset of representative peaks.

[0232] 61. The non-transitory computer-readable medium according to aspect 60, wherein identifying a subset of representative peaks from the plurality of peaks according to one or more grouping criteria further comprises:

[0233] Identify a first representative peak in a subset of representative peaks, the first representative peak covering another peak collinear with the first representative peak, wherein the first representative peak is a discrete peak having a frequency that is an integer multiple of the frequency of the other discrete peak.

[0234] 62. The non-transitory computer-readable medium according to aspect 60, wherein identifying a subset of representative peaks from the plurality of peaks according to one or more grouping criteria further comprises:

[0235] Identify a second representative peak in a subset of representative peaks, the second representative peak covering one or more other peaks having corresponding peak centers located within a predetermined distance from the peak center of the first representative peak, wherein the second representative peak is a discrete peak, and the one or more peaks covered by the second representative peak include discrete peaks or continuous peaks.

[0236] 63. The non-transitory computer-readable medium according to aspect 60, wherein identifying a subset of representative peaks from the plurality of peaks based on one or more grouping criteria further comprises:

[0237] Identify a third representative peak in a subset of representative peaks, the third representative peak covering one or more other peaks having peak centers located within the peak profile of the third representative peak, wherein the third representative peak is a continuous peak and located in an unrestricted direction, and the one or more peaks covered by the third representative peak are continuous.

[0238] The block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer hardware / software products according to various exemplary embodiments of the present disclosure. In this context, each block in the diagrams may represent an algorithmic or logical operation that can be implemented using hardware such as electronic circuit systems. Blocks may also represent a module, segment, or portion of code that includes one or more executable instructions for implementing a specified logical function. It should be understood that in some alternative implementations, the functions indicated in the blocks may not occur in the order shown in the figures. For example, two blocks shown consecutively may be executed or implemented substantially simultaneously, or two blocks may sometimes be executed in reverse order, depending on the functionality involved. Some blocks may also be omitted.

[0239] It will be understood that the embodiments of this disclosure are not limited to the exact constructions already described above and illustrated in the accompanying drawings, and various modifications and changes can be made without departing from the scope of this disclosure. This disclosure has been described with respect to various embodiments, and other embodiments of the invention will become apparent to those skilled in the art based on the specification and practical considerations of the invention disclosed herein. It is contemplated that the specification and examples are to be considered exemplary only, and the true scope and spirit / purpose of the invention are indicated by the appended claims.

[0240] The foregoing description is intended to be exemplary and not restrictive. Therefore, those skilled in the art will understand that modifications as described can be made without departing from the scope of the appended claims.

Claims

1. A method for selecting a subset of key patterns from a plurality of patterns in a design layout, the method comprising: Obtain diffraction order data based on the plurality of patterns representing features to be formed on at least a portion of a wafer, the diffraction order data including a plurality of peaks corresponding to the plurality of patterns; Identifying a subset of representative peaks from the plurality of peaks based on one or more grouping criteria, including: Identify a first representative peak in a subset of representative peaks, the first representative peak covering another peak collinear with the first representative peak, wherein the first representative peak is a discrete peak having a frequency that is an integer multiple of the frequency of the other discrete peak; and Select a subset of key patterns that correspond to a subset of representative peaks. The plurality of patterns includes one or more aperiodic patterns, each aperiodic pattern having multiple consecutive peaks, each consecutive peak failing to cover any discrete peak; and The method further includes: processing multiple diffraction patterns via normalization and grayscale, and processing the multiple diffraction patterns via binarization to identify the multiple peaks.

2. The method according to claim 1, further comprising performing a Fourier transform on the plurality of patterns to generate the diffraction order data respectively.

3. The method according to claim 1, wherein, The diffraction order data represents multiple diffraction order diagrams.

4. The method according to claim 1, further comprising: Extract the peak center and peak profile of the multiple peaks; and The multiple peaks are classified into discrete peaks and continuous peaks.

5. The method according to claim 3, wherein, The multiple peaks are located in any direction on the diffraction pattern.

6. The method according to claim 1, wherein, Identifying a subset of representative peaks from the plurality of peaks based on one or more grouping criteria also includes: Identify a second representative peak in a subset of representative peaks, the second representative peak covering one or more other peaks having corresponding peak centers located within a predetermined distance from the peak center of the first representative peak, wherein the second representative peak is a discrete peak, and the one or more peaks covered by the second representative peak include discrete peaks or continuous peaks.

7. The method according to claim 1, wherein, Identifying a subset of representative peaks from the plurality of peaks based on one or more grouping criteria also includes: Identify a third representative peak in a subset of representative peaks, the third representative peak covering one or more other peaks having peak centers located within the peak profile of the third representative peak, wherein the third representative peak is a continuous peak and located in an unrestricted direction, and the one or more peaks covered by the third representative peak are continuous.

8. The method according to claim 4 or 7, wherein, The peak profile is the actual profile and not an approximation.

9. The method according to claim 1, wherein, The method further includes: Perform the first SMO on a subset of the key patterns; Perform first mask optimization verification to identify one or more feature constraints; The layout redirection rules are optimized based on the identified one or more feature constraints, and a set of fragments is generated based on the optimized layout redirection rules; and Perform a second SMO on a subset of the key pattern and the resulting set of fragments to obtain an optimized source; A second mask optimization verification is performed on the plurality of patterns based on the adjusted layout redirection rules, with optimized sources and optimized targets; Determine whether one or more hotspots are identified in the design layout; and Based on the determination that no hotspots were identified, optimized source and optimized layout redirection rules were used for the full-chip design.

10. The method according to claim 1, wherein, The design layout is in Graphics Database System (GDS) format, Graphics Database System II (GDS II) format, Open Artwork System Interchange Standard (OASIS) format, or Caltech Intermediate Format (CIF).

11. The method according to claim 1, wherein, A subset of the key patterns is provided for at least one of optical proximity correction (OPC), defect inspection, defect prediction, or source mask optimization (SMO).

12. A non-transitory computer-readable medium storing a set of instructions executable by at least one processor of a system to cause the system to perform a method for selecting a subset of key patterns from a plurality of patterns of a design layout, the method comprising any one of claims 1 to 11.

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