Optical proximity effect correction method and device, storage medium and electronic equipment

Abstract

The invention discloses an optical proximity effect correction method and device, a storage medium and electronic equipment, and the method comprises the steps: placing an SRAF around a target pattern of a through hole layer according to a preset rule; performing OPC correction on the target graph to generate an initial correction graph; obtaining a first distance between each target graph and the corresponding initial correction graph, and a second distance between each target graph and the corresponding SRAF; taking the first distance and the second distance as optimization variables, and constructing a target function based on the first distance; on the basis of the target function, a genetic algorithm is adopted to carry out iterative solution on the optimization variables, and a target parameter combination is obtained; and generating a correction layout according to the target parameter combination. According to the embodiment of the invention, the efficiency of SRAF placement and OPC correction can be improved.

Description

Technical Field

[0001] The embodiments of the present application relate to the field of photolithography technology, and specifically to an optical proximity effect correction method, device, storage medium, and electronic device. Background Art

[0002] In semiconductor integrated circuit manufacturing, as process nodes continue to advance toward 60nm and below, the optical proximity effect (OPE) poses significant challenges to pattern resolution and linewidth control. OPE can cause deviations in the designed pattern during the lithography process, such as shrinkage, edge bending, or bridging, impacting device performance and manufacturing yield.

[0003] To offset the deviations caused by OPE, the industry widely uses optical proximity correction (OPC) technology. In the traditional OPC process, engineers add tiny compensating patterns (such as barriers and boost bars) to the edges of the design layout based on optical simulation models to pre-compensate for imaging distortion. However, with the shrinking size of via layers and the increasing complexity of multi-layer interconnect structures, relying solely on OPC is unable to fully address imaging accuracy issues.

[0004] On the other hand, to improve lithographic imaging contrast and linewidth controllability, sub-resolution assist features (SRAFs) are introduced into the mask layout. SRAFs are typically placed at a certain distance around the target pattern to improve light field distribution and enhance edge definition of the main pattern. However, SRAFs themselves do not contribute to circuit connectivity, and their placement and spacing must strictly adhere to mask process specifications; otherwise, they can easily cause bridging, breakage, or "dead edge" failures.

[0005] Existing technologies typically combine SRAF placement and OPC correction as a two-step, sequential process: first, SRAF placement is performed based on experience or PDK recommendations, followed by OPC correction of the entire pattern. However, this approach requires multiple rounds of manual trial and error, resulting in low efficiency and difficulty meeting the demands of high-throughput manufacturing. Summary of the Invention

[0006] The embodiments of the present application provide an optical proximity effect correction method, device, storage medium, and electronic device, which can improve the efficiency of SRAF placement and OPC correction.

[0007] In a first aspect, an embodiment of the present application provides a method for correcting an optical proximity effect, comprising: Place SRAFs around target patterns on the via layer according to preset rules; Performing OPC correction on the target pattern to generate an initial corrected pattern; Acquire a first distance between each of the target patterns and a corresponding initial correction pattern, and a second distance between each of the target patterns and a corresponding SRAF; Taking the first distance and the second distance as optimization variables, and constructing an objective function based on the first distance; Based on the objective function, a genetic algorithm is used to iteratively solve the optimization variables to obtain a target parameter combination; A revised layout is generated according to the target parameter combination.

[0008] In the optical proximity effect correction method provided in the embodiment of the present application, the optimization variables are iteratively solved using a genetic algorithm based on the objective function to obtain a target parameter combination, including: According to a preset population size and a variable value range, randomly or based on an empirical distribution, a number of current parameter vectors including the first distance and the second distance are generated to form a current population; For each of the current parameter vectors in the current population, calculating a corresponding fitness value according to an objective function; An iterative solution is performed based on the fitness value to obtain a target parameter combination.

[0009] In the optical proximity effect correction method provided in the embodiment of the present application, the iterative solution based on the fitness value to obtain the target parameter includes: Based on the fitness value, selecting a plurality of the current parameter vectors from the plurality of the current parameter vectors by roulette or tournament method to enter the mating pool; For the current parameter vector in the mating pool, gene segments are exchanged according to a single-point crossover or uniform crossover strategy to generate a next generation candidate parameter vector; An iterative solution is performed based on the candidate parameter vector to obtain a target parameter combination.

[0010] In the optical proximity effect correction method provided in the embodiment of the present application, the iterative solution based on the candidate parameter vector to obtain the target parameter combination includes: Applying Gaussian perturbation or adaptive fine-tuning to some genes of the candidate parameter vector with a preset mutation probability to generate a new population; Using the new population as the current population and the candidate parameter vector as the current parameter vector; Returning to the step of calculating the corresponding fitness value according to the objective function for each current parameter vector in the current population until a convergence condition is met or a preset maximum number of iterations is reached; The optimal current parameter vector at termination is used as the target parameter combination.

[0011] In the optical proximity effect correction method provided in the embodiment of the present application, the convergence condition is that the fitness value of the current population converges, and the edge placement error value corresponding to each first distance in the optimal current parameter vector is less than a preset threshold.

[0012] In the optical proximity effect correction method provided in an embodiment of the present application, constructing the objective function based on the first distance includes: Calculating an edge placement error value corresponding to each of the first distances; The sum of all the edge placement error values ​​is defined as the objective function.

[0013] In the optical proximity effect correction method provided in the embodiment of the present application, generating a correction layout according to the target parameter combination includes: adjusting the edge position of the initial correction graphic according to the first distance in the target parameter combination; adjusting the position of the SRAF according to a second distance in the target parameter combination; The adjusted initial correction pattern and the SRAF are integrated into the same mask layout to generate a correction layout.

[0014] In a second aspect, an embodiment of the present application provides an optical proximity effect correction device, comprising: A placement unit for placing SRAFs around target patterns on the via layer according to preset rules; A correction unit, configured to perform OPC correction on the target pattern to generate an initial corrected pattern; an acquiring unit, configured to acquire a first distance between each of the target patterns and a corresponding initial correction pattern, and a second distance between each of the target patterns and a corresponding SRAF; a construction unit, configured to use the first distance and the second distance as optimization variables and construct an objective function based on the first distance; An iterative unit, configured to iteratively solve the optimization variables using a genetic algorithm based on the objective function to obtain a target parameter combination; A generating unit is used to generate a revised layout according to the target parameter combination.

[0015] In a third aspect, the present application provides a storage medium storing a plurality of instructions, wherein the instructions are suitable for loading by a processor to execute any of the above-mentioned optical proximity effect correction methods.

[0016] In a fourth aspect, the present application provides an electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements any of the above-described optical proximity effect correction methods when executing the computer program.

[0017] In summary, the optical proximity effect correction method provided in the embodiments of the present application includes placing SRAFs around a target pattern in a through-hole layer according to preset rules; performing OPC correction on the target pattern to generate an initial correction pattern; obtaining a first distance between each target pattern and the corresponding initial correction pattern, and a second distance between each target pattern and the corresponding SRAF; using the first and second distances as optimization variables and constructing an objective function based on the first distances; based on the objective function, using a genetic algorithm to iteratively solve the optimization variables to obtain a target parameter combination; and generating a corrected layout based on the target parameter combination. By introducing a genetic algorithm to automatically iteratively solve the optimization variables, the embodiments of the present application significantly shorten the design-verification cycle and reduce manual tuning costs, thereby improving the efficiency of SRAF placement and OPC correction. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For those skilled in the art, other drawings can be obtained based on these drawings without creative work.

[0019] Figure 1 This is a schematic diagram of an application scenario of the optical proximity effect correction method provided in an embodiment of the present application.

[0020] Figure 2 4 is a flow chart of the optical proximity effect correction method provided in an embodiment of the present application.

[0021] Figure 3 It is a schematic diagram of the layout of the target graphics and SRAF provided in an embodiment of the present application.

[0022] Figure 4 It is a schematic diagram of the layout of the target pattern, initial correction pattern and SRAF provided in the embodiment of the present application.

[0023] Figure 5 This is a schematic diagram of chromosome genes provided in the examples of this application.

[0024] Figure 6 Schematic diagram of the structure of the optical proximity effect correction device provided in an embodiment of the present application.

[0025] Figure 7It is a structural diagram of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0026] Exemplary embodiments will be described in detail herein, with examples illustrated in the accompanying drawings. In the following description, when referring to the drawings, identical numerals in different figures represent identical or similar elements, unless otherwise indicated. The embodiments described in the following exemplary embodiments are not intended to represent all embodiments consistent with the present application. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present application, as detailed in the appended claims.

[0027] It should be noted that, in this document, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element defined by the sentence "comprising a ..." does not exclude the presence of other identical elements in the process, method, article or device comprising the element. In addition, components, features, and elements with the same name in different embodiments of the present application may have the same meaning or different meanings, and their specific meanings need to be determined by their explanation in the specific embodiment or further combined with the context of the specific embodiment.

[0028] It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0029] In the subsequent description, the use of suffixes such as "module", "component" or "unit" to represent elements is only for the purpose of facilitating the description of the present application and has no specific meaning. Therefore, "module", "component" or "unit" can be used interchangeably.

[0030] In the description of this application, it should be noted that the terms "upper," "lower," "left," "right," "inner," and "outer," etc., indicating orientations or positional relationships, are based on the orientations or positional relationships shown in the accompanying drawings and are intended solely to facilitate the description of this application and simplify the description. They are not intended to indicate or imply that the devices or components referred to must have a specific orientation, be constructed, or operate in a specific orientation. Therefore, they should not be construed as limitations on this application. Furthermore, terms such as "first" and "second," etc., are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0031] Existing technologies typically combine SRAF placement and OPC correction as a two-step, sequential process: first, SRAF placement is performed based on experience or PDK recommendations, followed by OPC correction of the entire pattern. However, this approach requires multiple rounds of manual trial and error, resulting in low efficiency and difficulty meeting the demands of high-throughput manufacturing.

[0032] Based on this, the embodiments of the present application provide an optical proximity effect correction method, device, storage medium and electronic device. Specifically, the optical proximity effect correction device can be integrated into an electronic device, which can be a server or a terminal; wherein the terminal can include a mobile phone, a wearable smart device, a tablet computer, a laptop computer, and a personal computer (PC), etc.; the server can be a single server or a server cluster composed of multiple servers, and can be a physical server or a virtual server.

[0033] For example, Figure 1 As shown, the electronic device can place SRAF around the target pattern of the through-hole layer according to preset rules; perform OPC correction on the target pattern to generate an initial correction pattern; obtain a first distance between each target pattern and the corresponding initial correction pattern, and a second distance between each target pattern and the corresponding SRAF; use the first distance and the second distance as optimization variables, and construct an objective function based on the first distance; based on the objective function, use a genetic algorithm to iteratively solve the optimization variables to obtain a target parameter combination; and generate a correction layout according to the target parameter combination.

[0034] The following will describe the technical solutions of this application in detail through specific embodiments. It should be noted that the order of description of the following embodiments does not limit the priority order of the embodiments.

[0035] See also Figure 2 , Figure 2 : is a flow chart of the optical proximity effect correction method provided by an embodiment of the present application. The specific process of the optical proximity effect correction method can be as follows: 101. Place SRAFs around the target pattern on the via layer according to preset rules.

[0036] Specifically, preset rules matching the through hole layer mask process may be obtained first, wherein the preset rules may include minimum auxiliary feature width, minimum auxiliary feature and target pattern spacing, minimum pattern spacing, maximum auxiliary feature length, and auxiliary feature shape constraints.

[0037] The minimum assist feature width defines the minimum width limit for a single SRAF line or arc segment (e.g., 20nm) to ensure reliable imaging and transfer during the lithography and etching processes. The minimum assist feature-to-target pattern spacing defines the minimum distance between the SRAF and the edge of the main via (e.g., 30nm) to avoid optical crosstalk or mutual interference caused by close proximity. The minimum pattern spacing (MRC) specifies a minimum spacing requirement for all patterns (including between adjacent SRAFs and between SRAFs and patterns on other layers or adjacent layers). This requirement is typically referred to as "Minimum Pitch Rule" or "MRC" in the PDK, e.g., 25nm. The maximum assist feature length limits the maximum extended length of a single SRAF (e.g., 200nm) to prevent mask stress or uneven illumination caused by excessively long SRAFs. The assist feature shape constraint specifies that SRAFs can only be straight or arc segments, with a minimum radius (e.g., ≥50nm) for reliable exposure. Sharp corners and complex curves are not permitted.

[0038] Then, for each edge of the target graphic, the candidate SRAF positions are arranged at a preset distance along the normal direction to generate a set of initial candidate auxiliary features. Then, according to the preset rules, the width and length are assigned to each candidate SRAF, and its shape (straight line segment or circular arc segment) is determined; for each candidate SRAF, it is determined whether the spacing between it and the adjacent target graphics, other SRAFs and other graphics on the layout meets the preset rules, and the candidates that do not meet the minimum graphic spacing or overlap with adjacent graphics are eliminated. For the candidate SRAFs that pass the manufacturability screening, the optical simulation tool is called to calculate their contribution value to the improvement of the imaging contrast of the target graphic, and the candidate SRAFs with contribution values ​​lower than the preset threshold are eliminated; the remaining candidate SRAFs are used as auxiliary features of the through-hole layer target graphic, and their position and shape data in the layout are output to form the following Figure 3 The layout shown.

[0039] 102. Perform OPC correction on the target graphic to generate an initial correction graphic.

[0040] Specifically, optical simulation parameters that match the through-hole layer process can be obtained. These optical simulation parameters include exposure wavelength, projection system numerical aperture (NA), process margin, and substrate reflectivity. Next, edge polygons and their coordinates are extracted from the target graphic layout data. Based on the aforementioned optical simulation parameters, optical imaging simulation is performed on the extracted edge polygons to generate an aerial image distribution. Based on the aerial image distribution, the actual landing point position of each target graphic edge is calculated using the equal intensity method or threshold projection method, and the deviation from the original edge position of the target graphic is calculated to obtain the edge placement error (EPE). For each edge placement error, the corresponding displacement or reshaping compensation is calculated according to a preset OPC algorithm (e.g., linear regression or table lookup). The compensation is applied to the corresponding edge polygon coordinates to generate an initial correction graphic containing all corrections.

[0041] In some embodiments, the layout after step 102 can be as follows Figure 4 shown.

[0042] 103. Obtain a first distance between each target pattern and the corresponding initial correction pattern, and a second distance between each target pattern and the corresponding SRAF.

[0043] Specifically, the edge polygon vertex sequences of the target shape, the initial modified shape, and the SRAF are extracted from GDSII / OASIS. Then, a ray is emitted along the normal direction or at the sampling point of each edge of the target shape, and the intersection with the corresponding edge of the initial modified shape is calculated. The signed distance along the normal direction is calculated as the first distance. Similarly, for each sampling point of each edge of the target shape, the nearest projection point is found in the SRAF edge set, and the distance between them (or the projection distance along the normal direction) is calculated as the second distance.

[0044] That is, the first distance refers to the distance from an edge of the target pattern to the corresponding edge of the corresponding initial correction pattern. The second distance refers to the distance from an edge of the target pattern to the corresponding edge of the corresponding SRAF. In some embodiments, the first distance (S1-S6) and the second distance (O1-O8) can be as follows: Figure 5 shown.

[0045] 104. Use the first distance and the second distance as optimization variables, and construct an objective function based on the first distance.

[0046] In the embodiment of the present application, each set of first distance and second distance can be used as a chromosome gene of the genetic algorithm. Figure 5 As shown, each chromosome gene includes fourteen parameters such as S1-S6, O1-O8, etc.

[0047] In some embodiments, for each chromosomal gene, an edge placement error value corresponding to each first distance may be calculated; and the sum of all edge placement error values ​​is defined as the objective function.

[0048] It should be noted that, in the embodiment of the present application, each edge placement error value needs to be smaller than a preset threshold value. For example, each edge placement error value needs to be smaller than 1 nm.

[0049] It is understandable that during the iterative process of the genetic algorithm, any chromosome gene with at least one EPE value greater than or equal to the edge placement error value can be directly eliminated to ensure that all EPE values ​​in the final convergence result are less than the preset threshold.

[0050] It is understandable that while a single edge error is acceptable, if multiple edges have small deviations, the cumulative effect can lead to circuit mismatch. Using the sum of the EPE of all edges as the objective function can globally ensure the accuracy consistency of the entire layout.

[0051] In the embodiment of the present application, the first distance and the second distance affect each other. During the optimization process, each adjustment will have a chain reaction on the EPE of all edges. Minimizing the objective function prompts the algorithm to find the optimal balance in the global space, rather than focusing on only one edge.

[0052] 104. Based on the objective function, the genetic algorithm is used to iteratively solve the optimization variables to obtain the target parameter combination.

[0053] In some embodiments, according to the preset population size and variable value range, several current parameter vectors containing the first distance and the second distance can be generated randomly or based on the empirical distribution to form the current population (Population); for each current parameter vector in the current population, the corresponding fitness value is calculated according to the objective function; and an iterative solution is performed based on the fitness value to obtain the target parameter combination.

[0054] It can be understood that each current parameter vector is a chromosome gene of the genetic algorithm.

[0055] In genetic algorithms, a population refers to the multiple candidate solutions that exist in parallel during each generation of optimization. The preset population size is the number of parameter vectors contained in each generation. Its value can be set according to the specific computing resources and optimization accuracy requirements, such as 50, 100 or 200 individuals.

[0056] The variable range refers to the legal value range for each optimization variable, including the first and second distances. These ranges should comply with mask manufacturing rules and the physical limitations of the optical imaging model, such as minimum line width, minimum spacing, and offset limits. The specific range can be set based on the via layer mask design specifications.

[0057] The fitness value is a metric used to measure the quality of the current parameter vector in controlling edge placement error. Its value is calculated from the objective function and is typically set to the inverse of the sum of all EPEs. In other words, the smaller the error, the higher the fitness. If the EPE of any edge exceeds a preset threshold, its fitness value is reduced through a penalty term. The fitness value guides the selection, crossover, and mutation processes in the genetic algorithm and is a key factor in determining whether a candidate solution advances to the next generation, thereby driving the optimization process toward more accurate and manufacturable mask patterns.

[0058] In some embodiments, multiple current parameter vectors can be selected from several current parameter vectors through roulette or tournament methods based on fitness values ​​and entered into a mating pool; for the current parameter vectors in the mating pool, gene fragments are exchanged according to a single-point crossover or uniform crossover strategy to generate the next generation of candidate parameter vectors; and an iterative solution is performed based on the candidate parameter vectors to obtain a target parameter combination.

[0059] The roulette wheel method involves probabilistic selection based on the relative proportion of fitness values, with higher fitness values ​​resulting in a greater probability of selection. The tournament method involves randomly selecting several individuals (with the current parameter vector) from the population for a local competition, with the highest fitness values ​​selected to enter the mating pool.

[0060] Single-point crossover involves selecting the same position in the two current parameter vectors as the crossover point, taking the parameter fragments before this position from the first current parameter vector and the parameter fragments after this position from the second current parameter vector, thereby generating a new next-generation candidate parameter vector. The reverse combination can also be used to generate another set of candidate parameter vectors. Uniform crossover involves constructing a new vector from the corresponding parameter values ​​of any one of the two current parameter vectors, with a preset crossover probability (e.g., 0.5), for each dimension of the two current parameter vectors, thereby generating multiple next-generation candidate parameter vectors.

[0061] In some embodiments, the step of "iteratively solving based on the candidate parameter vector to obtain the target parameter combination" can specifically be: applying Gaussian perturbation or adaptive fine-tuning to some genes of the candidate parameter vector with a preset mutation probability to generate a new population; taking the new population as the current population and the candidate parameter vector as the current parameter vector; returning to execute the step of calculating the corresponding fitness value according to the objective function for each current parameter vector in the current population until the convergence condition is met or the preset maximum number of iterations is reached; and taking the optimal current parameter vector at termination as the target parameter combination.

[0062] The partial gene application of the candidate parameter vector refers to the parameter values ​​on certain dimensions in the candidate parameter vector, that is, part of the variables in the optimization variables.

[0063] Gaussian perturbation refers to adding normally distributed random offsets to selected parameter values ​​to simulate small natural variations, making it suitable for continuous variable optimization. Adaptive fine-tuning dynamically adjusts the perturbation amplitude based on the overall fitness level of the current population or the number of iterations, making the search process more exploratory in the early stages and more convergent in the later stages. The mutation probability is typically set between 0.01 and 0.1 to control the frequency of mutations and avoid excessive perturbations that can cause jumps in the solution space.

[0064] For example, the candidate parameter vector is x=[S1,S2,S3,S4,S5,S6,O1,O2,...,O8]. During the mutation process, only the values ​​of dimensions such as S2, O4, and O7 may be adjusted, while the other dimensions remain unchanged.

[0065] In some embodiments, the candidate parameter vectors after the crossover and mutation operations may be combined with some parameter vectors with higher fitness values ​​in the current parameter vector to form a new population.

[0066] Specifically, an "elite retention strategy" can be adopted, that is, several parameter vectors with the highest fitness values ​​in the current population are directly retained in the new population to ensure that the high-quality solutions are not replaced; the remaining population is filled with candidate parameter vectors generated by crossover mutation, and finally constitutes a new population for the next round of fitness evaluation.

[0067] The convergence conditions are: the fitness value of the current population changes within several consecutive generations less than the preset convergence threshold, and the edge placement error value corresponding to each first distance of the current parameter vector with the best fitness value in the current population is less than the preset error threshold.

[0068] 105. Generate a revised layout based on the target parameter combination.

[0069] Specifically, the edge position of the initial correction pattern can be adjusted according to the first distance in the target parameter combination; the position of the SRAF can be adjusted according to the second distance in the target parameter combination; the adjusted initial correction pattern and the SRAF can be integrated into the same mask layout to generate a correction layout.

[0070] In summary, the optical proximity effect correction method provided in the embodiments of the present application includes placing SRAFs around target patterns in a via layer according to preset rules; performing OPC correction on the target patterns to generate initial correction patterns; obtaining a first distance between each target pattern and its corresponding initial correction pattern, and a second distance between each target pattern and its corresponding SRAF; using the first and second distances as optimization variables and constructing an objective function based on the first distance; iteratively solving the optimization variables using a genetic algorithm based on the objective function to obtain a target parameter combination; and generating a correction layout based on the target parameter combination. The embodiments of the present application treat the SRAF position (first distance) and the OPC correction amount (second distance) as overall optimization objectives, establishing a closed-loop feedback mechanism to achieve coordinated optimization of SRAF placement and OPC correction, thereby improving the overall effectiveness of optical proximity effect correction. The genetic algorithm is used for multi-objective constrained optimization, leveraging its parallel global search capabilities to effectively minimize the total edge placement error (EPE) and ensure that each individual edge error meets a preset threshold. This improves the imaging accuracy and consistency of the mask pattern, significantly shortens the design-verification cycle, and reduces manual tuning costs, thereby increasing the efficiency of SRAF placement and OPC correction.

[0071] To facilitate better implementation of the optical proximity effect correction method provided in the embodiments of the present application, the embodiments of the present application also provide an optical proximity effect correction device. The meanings of the terms herein are the same as those in the aforementioned optical proximity effect correction method, and the specific implementation details can be referred to the description in the method embodiment.

[0072] See also Figure 6 , Figure 6 Schematic diagram of the structure of the optical proximity effect correction device provided by the embodiment of the present application. The optical proximity effect correction device may include a placement unit 201, a correction unit 202, an acquisition unit 203, a construction unit 204, an iteration unit 205 and a generation unit 206. A placement unit 201 is configured to place SRAFs around a target pattern in a via layer according to a preset rule; The correction unit 202 is used to perform OPC correction on the target pattern to generate an initial corrected pattern; an acquiring unit 203, configured to acquire a first distance between each target pattern and the corresponding initial correction pattern, and a second distance between each target pattern and the corresponding SRAF; A construction unit 204 is configured to use the first distance and the second distance as optimization variables and construct an objective function based on the first distance; Iterative unit 205, used to iteratively solve the optimization variables based on the objective function using a genetic algorithm to obtain a target parameter combination; The generating unit 206 is configured to generate a revised layout according to the target parameter combination.

[0073] The specific implementation of each of the above units can be found in the above-mentioned embodiment of the optical proximity effect correction method, and will not be described in detail here.

[0074] In summary, the optical proximity effect correction device provided in the embodiments of the present application can place an SRAF around a target pattern in a via layer according to preset rules via a placement unit 201; perform OPC correction on the target pattern by a correction unit 202 to generate an initial correction pattern; obtain a first distance between each target pattern and the corresponding initial correction pattern, and a second distance between each target pattern and the corresponding SRAF by an acquisition unit 203; construct a target function based on the first distance using the first and second distances as optimization variables; and iterate using a genetic algorithm based on the target function to iteratively solve the optimization variables to obtain a target parameter combination by an iteration unit 205; and generate a corrected layout based on the target parameter combination by a generation unit 206. This embodiment of the present application treats the SRAF position (first distance) and the OPC correction amount (second distance) as overall optimization targets, establishing a closed-loop feedback mechanism to achieve coordinated optimization of SRAF placement and OPC correction, thereby improving the overall effectiveness of optical proximity effect correction. A genetic algorithm is used for multi-objective constrained optimization, fully utilizing its parallel global search capability to effectively minimize the total edge placement error (EPE) and ensure that the edge errors of each individual element meet the preset threshold. This improves the imaging accuracy and consistency of the mask pattern, significantly shortens the design-verification cycle, and reduces manual tuning costs, thereby improving the efficiency of SRAF placement and OPC correction.

[0075] The embodiment of the present application further provides an electronic device, in which the optical proximity effect correction device of the embodiment of the present application can be integrated, such as Figure 7 , which shows a schematic diagram of the structure of the electronic device involved in the embodiment of the present application, specifically: The electronic device may include one or more processing core processors 301 and one or more computer readable storage media memories 302 and other components. Those skilled in the art will understand that Figure 7 The electronic device structure shown in the figure does not constitute a limitation of the electronic device, and may include more or fewer components than shown in the figure, or combine certain components, or arrange components differently. The processor 301 is the control center of the electronic device. It connects the various parts of the entire electronic device using various interfaces and lines. By running or executing the software programs and / or this application stored in the memory 302, and calling the data stored in the memory 302, it performs various functions of the electronic device and processes data, thereby monitoring the electronic device as a whole. Optionally, the processor 301 may include one or more processing cores; preferably, the processor 301 may integrate an application processor and a modem processor, wherein the application processor mainly handles the operation of storage media, user interface and application programs, etc., and the modem processor mainly handles wireless communication. It is understandable that the above-mentioned modem processor may not be integrated into the processor 301.

[0076] The memory 302 can be used to store software programs and the present application. The processor 301 executes various functional applications and data processing by running the software programs and the present application stored in the memory 302. The memory 302 may mainly include a program storage area and a data storage area, wherein the program storage area may store operating storage media, applications required for at least one function, etc.; the data storage area may store data created based on the use of the electronic device, etc. In addition, the memory 302 may include a high-speed random access memory and may also include a non-volatile memory, such as at least one disk storage device, a flash memory device, or other volatile solid-state storage device. Accordingly, the memory 302 may also include a memory controller to provide the processor 301 with access to the memory 302.

[0077] Although not shown, the electronic device may further include a display unit, an input unit, a power supply, etc., which will not be described in detail here. Specifically, in this embodiment, the processor 301 in the electronic device will load the executable files corresponding to the processes of one or more application programs into the memory 302 according to the following instructions, and the processor 301 will run the application programs stored in the memory 302 to implement various functions as follows: Place SRAFs around target patterns on the via layer according to preset rules; Perform OPC correction on the target graphics to generate the initial correction graphics; Obtaining a first distance between each target pattern and the corresponding initial correction pattern, and a second distance between each target pattern and the corresponding SRAF; The first distance and the second distance are used as optimization variables, and an objective function is constructed based on the first distance; Based on the objective function, the genetic algorithm is used to iteratively solve the optimization variables to obtain the target parameter combination; Generate a revised layout based on the target parameter combination.

[0078] Those skilled in the art will appreciate that all or part of the steps in the various methods of the above embodiments may be accomplished by instructions, or by controlling related hardware through instructions. The instructions may be stored in a computer-readable storage medium and loaded and executed by a processor.

[0079] To this end, an embodiment of the present application provides a storage medium storing a plurality of instructions that can be loaded by a processor to execute the steps of any of the methods provided in the embodiments of the present application. For example, the instructions can execute the following steps: Place SRAFs around target patterns on the via layer according to preset rules; Perform OPC correction on the target graphics to generate the initial correction graphics; Obtaining a first distance between each target pattern and the corresponding initial correction pattern, and a second distance between each target pattern and the corresponding SRAF; The first distance and the second distance are used as optimization variables, and an objective function is constructed based on the first distance; Based on the objective function, the genetic algorithm is used to iteratively solve the optimization variables to obtain the target parameter combination; Generate a revised layout based on the target parameter combination.

[0080] The specific implementation of the above operations can be found in the previous embodiments and will not be repeated here.

[0081] The storage medium may include a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, etc.

[0082] Since the instructions stored in the storage medium can execute the steps in any method provided in the embodiments of the present application, the beneficial effects that can be achieved by any method provided in the embodiments of the present application can be achieved. Please refer to the previous embodiments for details and will not be repeated here.

[0083] The optical proximity effect correction method, device, storage medium and electronic device provided by the present application are respectively introduced in detail above. Specific examples are used in this article to illustrate the principles and implementation methods of the present application. The description of the above embodiments is only used to help understand the core idea of ​​the present application. At the same time, for technical personnel in this field, based on the idea of ​​the present application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as a limitation on the present application.

Claims

1. A method for correcting an optical proximity effect, characterized in that: include: Place SRAFs around target patterns on the via layer according to preset rules; Performing OPC correction on the target pattern to generate an initial corrected pattern; Acquire a first distance between each of the target patterns and a corresponding initial correction pattern, and a second distance between each of the target patterns and a corresponding SRAF; Taking the first distance and the second distance as optimization variables, and constructing an objective function based on the first distance; Based on the objective function, a genetic algorithm is used to iteratively solve the optimization variables to obtain a target parameter combination; A revised layout is generated according to the target parameter combination.

2. The optical proximity effect correction method according to claim 1, wherein: The method of iteratively solving the optimization variables based on the objective function using a genetic algorithm to obtain a target parameter combination includes: According to a preset population size and a variable value range, randomly or based on an empirical distribution, a number of current parameter vectors including the first distance and the second distance are generated to form a current population; For each of the current parameter vectors in the current population, calculating a corresponding fitness value according to an objective function; An iterative solution is performed based on the fitness value to obtain a target parameter combination.

3. The optical proximity effect correction method according to claim 2, wherein: The iterative solution based on the fitness value to obtain the target parameter includes: Based on the fitness value, selecting a plurality of the current parameter vectors from the plurality of the current parameter vectors by roulette or tournament method to enter the mating pool; For the current parameter vector in the mating pool, gene segments are exchanged according to a single-point crossover or uniform crossover strategy to generate a next generation candidate parameter vector; An iterative solution is performed based on the candidate parameter vector to obtain a target parameter combination.

4. The optical proximity effect correction method according to claim 3, wherein: The iterative solution based on the candidate parameter vector to obtain the target parameter combination includes: Applying Gaussian perturbation or adaptive fine-tuning to some genes of the candidate parameter vector with a preset mutation probability to generate a new population; Using the new population as the current population and the candidate parameter vector as the current parameter vector; Returning to the step of calculating the corresponding fitness value according to the objective function for each current parameter vector in the current population until a convergence condition is met or a preset maximum number of iterations is reached; The optimal current parameter vector at termination is used as the target parameter combination.

5. The optical proximity effect correction method according to claim 4, wherein: The convergence condition is that the fitness value of the current population converges, and the edge placement error value corresponding to each first distance in the optimal current parameter vector is less than a preset threshold.

6. The optical proximity effect correction method according to claim 1, wherein: The constructing of the objective function based on the first distance includes: Calculating an edge placement error value corresponding to each of the first distances; The sum of all the edge placement error values ​​is defined as the objective function.

7. The optical proximity effect correction method according to claim 1, wherein: Generating a revised layout according to the target parameter combination includes: adjusting the edge position of the initial correction graphic according to the first distance in the target parameter combination; adjusting the position of the SRAF according to a second distance in the target parameter combination; The adjusted initial correction pattern and the SRAF are integrated into the same mask layout to generate a correction layout.

8. An optical proximity effect correction device, characterized in that: include: A placement unit for placing SRAFs around target patterns on the via layer according to preset rules; A correction unit, configured to perform OPC correction on the target pattern to generate an initial corrected pattern; an acquiring unit, configured to acquire a first distance between each of the target patterns and a corresponding initial correction pattern, and a second distance between each of the target patterns and a corresponding SRAF; a construction unit, configured to use the first distance and the second distance as optimization variables and construct an objective function based on the first distance; An iterative unit, configured to iteratively solve the optimization variables using a genetic algorithm based on the objective function to obtain a target parameter combination; A generating unit is used to generate a revised layout according to the target parameter combination.

9. A storage medium, characterized in that: The storage medium stores a plurality of instructions, and the instructions are suitable for being loaded by a processor to execute the optical proximity effect correction method according to any one of claims 1 to 7.

10. An electronic device, characterized in that: The method comprises a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the method for correcting the optical proximity effect according to any one of claims 1 to 7 is implemented.

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