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

Abstract

The application discloses an optical proximity correction method and device, a storage medium and an electronic device. The optical proximity correction method comprises the following steps: placing SRAF around a target pattern of a via layer according to a preset rule; performing OPC correction on the target pattern to generate an initial correction pattern; acquiring 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; taking the first distance and the second distance as optimization variables, and constructing a target function based on the first distance; iteratively solving the optimization variables based on the target function by using a genetic algorithm to obtain a target parameter combination; and generating a corrected layout according to the target parameter combination. The embodiment of the application can improve the efficiency of SRAF placement and OPC correction.

Description

Technical Field

[0001] This application relates to the field of photolithography technology, specifically to an optical proximity effect correction method, apparatus, storage medium, and electronic device. Background Technology

[0002] In semiconductor integrated circuit manufacturing, as process nodes continue to advance to 60nm and below, the Optical Proximity Effect (OPE) poses a significant challenge to pattern resolution and linewidth control. OPE can cause deviations such as dimensional shrinkage, edge bending, or bridging in the pattern designed in the layout during photolithography, affecting device performance and manufacturing yield.

[0003] To compensate for the deviations caused by OPE (Optical Proximity Effect), the industry commonly employs Optical Proximity Correction (OPC) technology. In the traditional OPC process, engineers add tiny compensation patterns (such as barrier blocks, booster bars, etc.) at 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 multilayer interconnect structures, relying solely on OPC is insufficient to comprehensively solve the imaging accuracy problem.

[0004] On the other hand, to improve the contrast and linewidth controllability of lithographic imaging, Sub-Resolution Assist Features (SRAFs) are introduced into the mask layout. SRAFs are typically distributed at a certain distance around the target pattern to improve the light field distribution and enhance the sharpness of the main pattern edges. However, SRAFs themselves do not participate in circuit connectivity, and their placement and spacing must strictly adhere to mask process rules; otherwise, bridging, breaks, or "dull edges" failures are likely to occur.

[0005] Existing technologies typically treat SRAF placement and OPC correction as a two-step sequential process: first, SRAF is placed based on experience or PDK recommendations, and then OPC correction is performed on the overall pattern. However, this method requires multiple rounds of manual trial and error, resulting in low efficiency and making it difficult to meet the demands of high-throughput manufacturing. Summary of the Invention

[0006] This application provides an optical proximity effect correction method, apparatus, storage medium, and electronic device, which can improve the efficiency of SRAF placement and OPC correction.

[0007] In a first aspect, embodiments of this application provide an optical proximity effect correction method, including:

[0008] SRAFs are placed around the target pattern of the through-hole layer according to preset rules;

[0009] The target graphic is modified using OPC to generate an initial modified graphic;

[0010] Obtain a first distance between each target graphic and its corresponding initial corrected graphic, and a second distance between each target graphic and its corresponding SRAF;

[0011] The first distance and the second distance are used as optimization variables, and an objective function is constructed based on the first distance;

[0012] Based on the objective function, a genetic algorithm is used to iteratively solve the optimization variables to obtain the combination of objective parameters;

[0013] A revised layout is generated based on the combination of the target parameters.

[0014] In the optical proximity effect correction method provided in this application embodiment, the step of using a genetic algorithm to iteratively solve the optimization variables based on the objective function to obtain the target parameter combination includes:

[0015] Based on the preset population size and variable value range, several current parameter vectors containing the first distance and the second distance are generated randomly or based on empirical distribution to form the current population;

[0016] For each current parameter vector in the current population, calculate the corresponding fitness value according to the objective function;

[0017] The target parameter combination is obtained by iteratively solving based on the fitness value.

[0018] In the optical proximity effect correction method provided in this application embodiment, the step of iteratively solving based on the fitness value to obtain the target parameters includes:

[0019] Based on the fitness value, multiple current parameter vectors are selected from several current parameter vectors and entered into the mating pool through roulette or tournament methods.

[0020] For the current parameter vector in the mating pool, gene fragments are exchanged according to a single-point crossover or uniform crossover strategy to generate the next generation candidate parameter vector;

[0021] The target parameter combination is obtained by iteratively solving based on the candidate parameter vector.

[0022] In the optical proximity effect correction method provided in this application embodiment, the step of iteratively solving based on the candidate parameter vector to obtain the target parameter combination includes:

[0023] A new population is generated by applying Gaussian perturbation or adaptive fine-tuning to some genes of the candidate parameter vector with a preset mutation probability.

[0024] The new population is used as the current population, and the candidate parameter vector is used as the current parameter vector;

[0025] Return to 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;

[0026] Use the optimal current parameter vector at the time of termination as the target parameter combination.

[0027] In the optical proximity effect correction method provided in this application embodiment, the convergence condition is that the fitness value of the current population converges, and the edge placement error value corresponding to each of the first distances in the optimal current parameter vector is less than a preset threshold.

[0028] In the optical proximity effect correction method provided in this application embodiment, the step of constructing an objective function based on the first distance includes:

[0029] Calculate the edge placement error value corresponding to each of the first distances;

[0030] The sum of all the edge placement error values ​​is defined as the objective function.

[0031] In the optical proximity effect correction method provided in this application embodiment, the step of generating a corrected pattern based on the target parameter combination includes:

[0032] The edge position of the initial correction graphic is adjusted according to the first distance in the target parameter combination;

[0033] The position of the SRAF is adjusted according to the second distance in the target parameter combination;

[0034] The adjusted initial correction pattern is integrated with the SRAF into the same mask layout to generate the correction layout.

[0035] Secondly, embodiments of this application provide an optical proximity effect correction device, comprising:

[0036] Placement unit, used to place SRAF around the target pattern of the through-hole layer according to preset rules;

[0037] The correction unit is used to perform OPC correction on the target graphic to generate an initial corrected graphic;

[0038] The acquisition unit is used to acquire a first distance between each target graphic and its corresponding initial correction graphic, and a second distance between each target graphic and its corresponding SRAF.

[0039] A construction unit is used to take the first distance and the second distance as optimization variables and construct an objective function based on the first distance;

[0040] An iterative unit is used to iteratively solve the optimization variables based on the objective function using a genetic algorithm to obtain a combination of objective parameters;

[0041] The generation unit is used to generate a modified layout based on the target parameter combination.

[0042] Thirdly, this application provides a storage medium storing a plurality of instructions adapted for loading by a processor to execute the optical proximity effect correction method described in any of the preceding claims.

[0043] Fourthly, this application provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the optical proximity effect correction method described in any of the preceding claims.

[0044] In summary, the optical proximity effect correction method provided in this application includes placing SRAFs around a target pattern in a via layer according to preset rules; performing OPC correction on the target pattern to generate an initial corrected pattern; obtaining a first distance between each target pattern and its corresponding initial corrected pattern, and a second distance between each target pattern and its corresponding SRAF; using the first distance and the second distance as optimization variables, and constructing an objective function based on the first distance; using a genetic algorithm to iteratively solve the optimization variables based on the objective function to obtain a combination of objective parameters; and generating a corrected layout based on the combination of objective parameters. This application significantly shortens the design-verification cycle and reduces manual tuning costs by introducing a genetic algorithm to automatically iteratively solve the optimization variables, thereby improving the efficiency of SRAF placement and OPC correction. Attached Figure Description

[0045] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0046] Figure 1 This is a schematic diagram illustrating an application scenario of the optical proximity effect correction method provided in the embodiments of this application.

[0047] Figure 2 This is a schematic flowchart of the optical proximity effect correction method provided in the embodiments of this application.

[0048] Figure 3 This is a schematic diagram of the layout of the target graphic and SRAF provided in the embodiments of this application.

[0049] Figure 4 This is a layout diagram of the target graphic, the initial modified graphic, and the SRAF provided in the embodiments of this application.

[0050] Figure 5 This is a schematic diagram of chromosome genes provided in the embodiments of this application.

[0051] Figure 6 This is a schematic diagram of the optical proximity effect correction device provided in the embodiments of this application.

[0052] Figure 7 This is a schematic diagram of the structure of the electronic device provided in the embodiments of this application. Detailed Implementation

[0053] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0054] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element. Furthermore, components, features, and elements with the same names in different embodiments of this application may have the same meaning or different meanings, the specific meaning of which must be determined by its interpretation in that specific embodiment or further in conjunction with the context of that specific embodiment.

[0055] It should be understood that the specific embodiments described herein are merely illustrative of this application and are not intended to limit this application.

[0056] In the following description, the use of suffixes such as "module," "part," or "unit" to denote elements is solely for the purpose of illustrative purposes and has no specific meaning in itself. Therefore, "module," "part," or "unit" may be used interchangeably.

[0057] In the description of this application, it should be noted that the terms "upper," "lower," "left," "right," "inner," and "outer," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are used only for the convenience of describing this application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this application. In addition, terms such as "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0058] Existing technologies typically treat SRAF placement and OPC correction as a two-step sequential process: first, SRAF is placed based on experience or PDK recommendations, and then OPC correction is performed on the overall pattern. However, this method requires multiple rounds of manual trial and error, resulting in low efficiency and making it difficult to meet the demands of high-throughput manufacturing.

[0059] Based on this, embodiments of this application provide an optical proximity effect correction method, apparatus, storage medium, and electronic device. Specifically, the optical proximity effect correction apparatus can be integrated into an electronic device, which can be a server or a terminal, etc. The terminal can include mobile phones, wearable smart devices, tablet computers, laptops, and personal computers (PCs), 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.

[0060] For example, such as Figure 1 As shown, the electronic device can place SRAFs around the target pattern of the via layer according to preset rules; perform OPC correction on the target pattern to generate an initial corrected pattern; obtain the first distance between each target pattern and the corresponding initial corrected pattern, and the 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; use a genetic algorithm to iteratively solve the optimization variables based on the objective function to obtain the target parameter combination; and generate a corrected layout according to the target parameter combination.

[0061] The technical solutions shown in this application will be described in detail below through specific embodiments. It should be noted that the order of description of the following embodiments is not intended to limit the priority of the embodiments.

[0062] Please see Figure 2 , Figure 2 This is a schematic flowchart of the optical proximity effect correction method provided in this application embodiment. The specific flow of the optical proximity effect correction method can be as follows:

[0063] 101. Place SRAF around the target pattern of the through-hole layer according to the preset rules.

[0064] Specifically, preset rules matching the via layer mask process can be obtained first. These preset rules may include minimum auxiliary feature width, minimum distance between auxiliary feature and target pattern, minimum pattern spacing, maximum auxiliary feature length, and auxiliary feature shape constraints.

[0065] The minimum auxiliary feature width defines the minimum width limit (e.g., 20 nm) for a single line segment or arc segment of the SRAF, ensuring reliable imaging and transfer during photolithography and etching. The minimum auxiliary feature-to-target pattern spacing limits the minimum distance (e.g., 30 nm) between the SRAF and the edge of the main via, avoiding optical crosstalk or mutual interference caused by excessive proximity. The minimum pattern spacing (MRC) refers to the minimum spacing requirement for all patterns (including between multiple adjacent SRAFs, and between SRAFs and patterns in other or adjacent layers), typically referred to as the "Minimum Pitch Rule" or "MRC" in PDK, such as 25 nm. The maximum auxiliary feature length limits the maximum unfolded length of a single SRAF (e.g., 200 nm), preventing excessively long SRAFs from causing mask stress or uneven illumination. Auxiliary feature shape constraints specify that SRAFs can only be straight lines or arc segments, and the lower limit of the radius of curvature (e.g., ≥50 nm) allows for reliable exposure; sharp corners or complex curves are not permitted.

[0066] Then, for each edge of the target graphic, candidate SRAF positions are arranged along the normal direction at a preset distance, generating a set of initial candidate auxiliary features. Next, according to preset rules, the width and length of each candidate SRAF are assigned, and its shape (straight line segment or arc segment) is determined. For each candidate SRAF, it is determined whether its spacing with adjacent target graphics, other SRAFs, and other graphics on the layout meets preset rules; candidates that do not meet the minimum graphic spacing or overlap with adjacent graphics are eliminated. For candidate SRAFs that pass the manufacturability screening, optical simulation tools are used to calculate their contribution to improving the imaging contrast of the target graphic, and candidate SRAFs with contribution values ​​below a preset threshold are eliminated. The remaining candidate SRAFs are used as auxiliary features of the via layer target graphic, and their position and shape data in the layout are output, forming a set of auxiliary features. Figure 3 The layout shown.

[0067] 102. Perform OPC correction on the target graphic to generate an initial corrected graphic.

[0068] Specifically, optical simulation parameters matching the via layer process can be obtained. These parameters include exposure wavelength, projection system numerical aperture (NA), process allowance, and substrate reflectivity. Then, 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. According to 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 shaping compensation is calculated according to a preset OPC algorithm (e.g., linear regression or lookup table method). The compensation is applied to the corresponding edge polygon coordinates to generate an initial corrected graphic containing all corrections.

[0069] In some embodiments, the layout after step 102 can be as follows: Figure 4 As shown.

[0070] 103. Obtain the first distance between each target graphic and the corresponding initial corrected graphic, and the second distance between each target graphic and the corresponding SRAF.

[0071] Specifically, the target graphic, the initial corrected graphic, and the edge polygon vertex sequence of SRAF are extracted from GDSII / OASIS; then, rays are emitted along the normal or at the sampling point of each edge of the target graphic, and the intersection with the corresponding edge of the initial corrected graphic is calculated, and the signed distance along the normal is used as the first distance; similarly, for each sampling point of each edge of the target graphic, the nearest projection point is found in the SRAF edge set, and its distance (or projection distance along the normal) is calculated, which is used as the second distance.

[0072] That is, the first distance refers to the distance from one edge of the target graphic to the corresponding edge of the corresponding initial corrected graphic. The second distance refers to the distance from one edge of the target graphic 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 As shown.

[0073] 104. Use the first distance and the second distance as optimization variables, and construct the objective function based on the first distance.

[0074] In this embodiment of the application, each set of first and second distances can be used as a chromosome gene in the genetic algorithm. For example, as... Figure 5 As shown, each chromosome gene includes fourteen parameters, such as S1-S6 and O1-O8.

[0075] In some embodiments, for each chromosome gene, the edge placement error value corresponding to each first distance can be calculated; the sum of all edge placement error values ​​is defined as the objective function.

[0076] It should be noted that, in this embodiment, each edge placement error value must be less than a preset threshold. For example, each edge placement error value must be less than 1 nm.

[0077] Understandably, during the iteration 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 removed to ensure that all EPE values ​​in the final convergence result are less than the preset threshold.

[0078] Understandably, while errors at a single edge are acceptable, small deviations at multiple edges can accumulate and lead to circuit mismatch. Summing the EPE values ​​of all edges as the objective function ensures consistent accuracy across the entire layout at a global level.

[0079] In the embodiments of this application, the first distance and the second distance influence 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 just taking care of a certain edge.

[0080] 104. Based on the objective function, a genetic algorithm is used to iteratively solve the optimization variables to obtain the combination of objective parameters.

[0081] In some embodiments, several current parameter vectors containing a first distance and a second distance can be randomly generated or based on an empirical distribution according to a preset population size and variable value range to form the current population. For each current parameter vector in the current population, the corresponding fitness value is calculated according to the objective function. The objective parameter combination is obtained by iteratively solving based on the fitness value.

[0082] It is understandable that each current parameter vector is the chromosome gene of the genetic algorithm.

[0083] In genetic algorithms, a population refers to 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, and its value can be set according to specific computing resources and optimization accuracy requirements, such as 50, 100 or 200 individuals.

[0084] The range of variable values ​​refers to the legal numerical interval of each optimization variable, including the first distance and the second distance. These ranges should comply with the physical limitations of mask manufacturing rules and optical imaging models, such as minimum linewidth, minimum spacing, and offset limits. The specific ranges can be set according to the via layer mask design specifications.

[0085] The fitness value is an indicator used to measure the performance of the current parameter vector in controlling edge placement error. Its value is calculated from the objective function and is typically set as the negative of the sum of all edge placement errors (EPEs); a smaller error indicates a higher 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 genetic algorithms and is a crucial factor in determining whether a candidate solution can enter the next generation, thereby driving the optimization process towards a more accurate and manufacturable mask pattern.

[0086] In some embodiments, multiple current parameter vectors can be selected from several current parameter vectors and entered into the mating pool based on fitness values ​​through roulette or tournament methods; gene fragments are exchanged on the current parameter vectors in the mating pool according to a single-point crossover or uniform crossover strategy to generate next-generation candidate parameter vectors; the target parameter combination is obtained by iteratively solving based on the candidate parameter vectors.

[0087] Among them, the roulette wheel selection method refers to probabilistic selection based on the relative proportions of fitness values, with higher fitness values ​​having a greater probability of being selected. The tournament selection method refers to randomly selecting a number of individuals (the current parameter vector) from the population to compete locally, selecting the one with the best fitness to enter the mating pool.

[0088] Single-point crossover refers to selecting the same position in two current parameter vectors as the crossover point, taking the parameter fragment before that position from the first current parameter vector, and the parameter fragment after that position from the second current parameter vector, thus generating a new next-generation candidate parameter vector; it can also be combined in reverse to generate another set of candidate parameter vectors. Uniform crossover refers to taking each parameter of two current parameter vectors, according to a preset crossover probability (e.g., 0.5), randomly selecting the corresponding parameter value of one vector to construct a new vector, thus generating multiple next-generation candidate parameter vectors.

[0089] In some embodiments, the step "iteratively solving based on candidate parameter vectors to obtain the target parameter combination" can specifically be as follows: 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 the convergence condition is met or the preset maximum number of iterations is reached; and using the optimal current parameter vector at the time of termination as the target parameter combination.

[0090] Partial gene application of the candidate parameter vector refers to the parameter values ​​in certain dimensions of the candidate parameter vector, that is, some variables in the optimization variables.

[0091] Among them, Gaussian perturbation refers to adding a random offset that follows a normal distribution to the selected parameter values ​​to simulate natural small variations, which is suitable for continuous variable optimization. Adaptive fine-tuning refers to dynamically adjusting the perturbation amplitude according to 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 usually set between 0.01 and 0.1 to control the frequency of mutation and avoid excessive perturbation that could cause jumps in the solution space.

[0092] 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.

[0093] In some embodiments, the candidate parameter vectors after crossover and mutation operations can be combined with the parameter vectors with higher fitness values ​​in the current parameter vectors to form a new population.

[0094] Specifically, an "elite retention strategy" can be adopted, in which the parameter vectors with the highest fitness values ​​in the current population are directly retained in the new population to ensure that 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.

[0095] The convergence condition is: the fitness value of the current population changes less than the preset convergence threshold over several consecutive generations, 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.

[0096] 105. Generate a revised layout based on the target parameter combination.

[0097] 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; and the adjusted initial correction pattern and SRAF can be integrated into the same mask layout to generate the correction layout.

[0098] In summary, the optical proximity effect correction method provided in this application includes placing SRAFs around the target pattern of the via layer according to preset rules; performing OPC correction on the target pattern to generate an initial corrected pattern; obtaining a first distance between each target pattern and the corresponding initial corrected pattern, and a second distance between each target pattern and the corresponding SRAF; using the first distance and the second distance as optimization variables, and constructing an objective function based on the first distance; using a genetic algorithm to iteratively solve the optimization variables based on the objective function to obtain a combination of objective parameters; and generating a corrected pattern based on the combination of objective parameters. This application considers 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, thus improving the overall effect of optical proximity effect correction. The use of a genetic algorithm for multi-objective constraint optimization fully utilizes its parallel global search capability, effectively minimizing the total edge placement error (EPE) and ensuring that individual edge errors meet preset thresholds, improving the imaging accuracy and consistency of the mask pattern, significantly shortening the design-verification cycle and reducing manual tuning costs, thereby improving the efficiency of SRAF placement and OPC correction.

[0099] To facilitate better implementation of the optical proximity effect correction method provided in this application, this application also provides an optical proximity effect correction device. The meanings of the terms used are the same as in the optical proximity effect correction method described above, and specific implementation details can be found in the descriptions within the method embodiments.

[0100] Please see Figure 6 , Figure 6 This is a schematic diagram of the optical proximity effect correction device provided in an embodiment of this 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.

[0101] Placement unit 201 is used to place SRAF around the target pattern of the through-hole layer according to preset rules;

[0102] Correction unit 202 is used to perform OPC correction on the target graphic and generate an initial corrected graphic;

[0103] The acquisition unit 203 is used to acquire a first distance between each target graphic and the corresponding initial correction graphic, and a second distance between each target graphic and the corresponding SRAF;

[0104] Construction unit 204 is used to take the first distance and the second distance as optimization variables and construct an objective function based on the first distance;

[0105] Iteration unit 205 is used to iteratively solve the optimization variables based on the objective function using a genetic algorithm to obtain the combination of objective parameters;

[0106] The generation unit 206 is used to generate a modified layout based on the combination of target parameters.

[0107] For specific implementation methods of each of the above units, please refer to the embodiments of the optical proximity effect correction method described above, which will not be repeated here.

[0108] In summary, the optical proximity effect correction device provided in this application embodiment can place SRAFs around the target pattern of the via layer according to preset rules by the placement unit 201; the correction unit 202 performs OPC correction on the target pattern to generate an initial corrected pattern; the acquisition unit 203 acquires the first distance between each target pattern and the corresponding initial corrected pattern, and the second distance between each target pattern and the corresponding SRAF; the construction unit 204 uses the first distance and the second distance as optimization variables and constructs an objective function based on the first distance; the iteration unit 205 uses a genetic algorithm to iteratively solve the optimization variables based on the objective function to obtain the target parameter combination; and the generation unit 206 generates the corrected pattern according to the target parameter combination. This application embodiment considers the SRAF position (first distance) and the OPC correction amount (second distance) as overall optimization targets, establishes a closed-loop feedback mechanism, realizes the synergistic optimization of SRAF placement and OPC correction, and improves the overall effect of optical proximity effect correction. A genetic algorithm is used for multi-objective constraint optimization, which fully utilizes its parallel global search capability to effectively minimize the total edge placement error (EPE) and ensure that the individual edge errors meet the preset threshold. This improves the imaging accuracy and consistency of the mask pattern, significantly shortens the design-verification cycle and reduces the cost of manual tuning, thereby improving the efficiency of SRAF placement and OPC correction.

[0109] This application also provides an electronic device that may integrate the optical proximity effect correction device of this application, such as... Figure 7 As shown, it illustrates a structural schematic diagram of the electronic device involved in the embodiments of this application, specifically:

[0110] The electronic device may include components such as a processor 301 with one or more processing cores and a memory 302 with one or more computer-readable storage media. Those skilled in the art will understand that... Figure 7 The electronic device structure shown does not constitute a limitation on the electronic device and may include more or fewer components than shown, or combine certain components, or have different component arrangements. Wherein:

[0111] The processor 301 is the control center of the electronic device. It connects various parts of the electronic device via various interfaces and lines. By running or executing software programs stored in the memory 302 and / or this application, and by calling data stored in the memory 302, it performs various functions and processes data, thereby providing overall monitoring of the electronic device. 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 the storage medium, user interface, and application programs, while the modem processor mainly handles wireless communication. It is understood that the modem processor may not be integrated into the processor 301.

[0112] The memory 302 can be used to store software programs and this application. The processor 301 executes various functional applications and data processing by running the software programs and this application stored in the memory 302. The memory 302 may mainly include a program storage area and a data storage area. The program storage area may store applications required for operating the storage medium and at least one function; the data storage area may store data created based on the use of the electronic device. In addition, the memory 302 may include high-speed random access memory and may also include non-volatile memory, such as at least one disk storage device, 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.

[0113] Although not shown, the electronic device may also include a display unit, an input unit, and a power supply, etc., which will not be described in detail here. Specifically, in this embodiment, the processor 301 in the electronic device loads 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 runs the application programs stored in the memory 302 to realize various functions, as follows:

[0114] SRAFs are placed around the target pattern of the through-hole layer according to preset rules;

[0115] Perform OPC correction on the target graphic to generate an initial corrected graphic;

[0116] Obtain the first distance between each target graphic and its corresponding initial corrected graphic, and the second distance between each target graphic and its corresponding SRAF;

[0117] The first distance and the second distance are used as optimization variables, and the objective function is constructed based on the first distance;

[0118] Based on the objective function, a genetic algorithm is used to iteratively solve the optimization variables to obtain the combination of objective parameters;

[0119] A revised layout is generated based on the combination of target parameters.

[0120] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be performed by instructions, or by instructions controlling related hardware. These instructions can be stored in a computer-readable storage medium and loaded and executed by a processor.

[0121] Therefore, embodiments of this application provide a storage medium storing a plurality of instructions that can be loaded by a processor to execute steps in any of the methods provided in embodiments of this application. For example, the instructions can execute the following steps:

[0122] SRAFs are placed around the target pattern of the through-hole layer according to preset rules;

[0123] Perform OPC correction on the target graphic to generate an initial corrected graphic;

[0124] Obtain the first distance between each target graphic and its corresponding initial corrected graphic, and the second distance between each target graphic and its corresponding SRAF;

[0125] The first distance and the second distance are used as optimization variables, and the objective function is constructed based on the first distance;

[0126] Based on the objective function, a genetic algorithm is used to iteratively solve the optimization variables to obtain the combination of objective parameters;

[0127] A revised layout is generated based on the combination of target parameters.

[0128] For details on the implementation of each of the above operations, please refer to the previous examples, which will not be repeated here.

[0129] The storage medium may include: read-only memory (ROM), random access memory (RAM), disk or optical disk, etc.

[0130] Since the instructions stored in the storage medium can execute the steps of any method provided in the embodiments of this application, the beneficial effects that any method provided in the embodiments of this application can achieve can be realized. For details, please refer to the previous embodiments, which will not be repeated here.

[0131] The optical proximity effect correction method, apparatus, storage medium, and electronic device provided in this application have been described in detail above. Specific examples have been used to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the core ideas of this application. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this application. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A method for correcting optical proximity effect, characterized in that, include: SRAFs are placed around the target pattern of the through-hole layer according to preset rules; The target graphic is modified using OPC to generate an initial modified graphic; Obtain a first distance between each target graphic and its corresponding initial corrected graphic, and a second distance between each target graphic and its corresponding SRAF; The first distance and the second distance are used as optimization variables, and the edge placement error value corresponding to each first distance is calculated; The sum of all the edge placement error values ​​is defined as the objective function; Based on the objective function, a genetic algorithm is used to iteratively solve the optimization variables to obtain the combination of objective parameters; A revised layout is generated based on the combination of the target parameters.

2. The optical proximity effect correction method as described in claim 1, characterized in that, Based on the objective function, a genetic algorithm is used to iteratively solve the optimization variables to obtain the target parameter combination, including: Based on the preset population size and variable value range, several current parameter vectors containing the first distance and the second distance are generated randomly or based on empirical distribution to form the current population; For each current parameter vector in the current population, calculate the corresponding fitness value according to the objective function; The target parameter combination is obtained by iteratively solving based on the fitness value.

3. The optical proximity effect correction method as described in claim 2, characterized in that, The iterative solution based on the fitness value to obtain the target parameters includes: Based on the fitness value, multiple current parameter vectors are selected from several current parameter vectors and entered into the mating pool through roulette or tournament methods. For the current parameter vector in the mating pool, gene fragments are exchanged according to a single-point crossover or uniform crossover strategy to generate the next generation candidate parameter vector; The target parameter combination is obtained by iteratively solving based on the candidate parameter vector.

4. The optical proximity effect correction method as described in claim 3, characterized in that, The iterative solution based on the candidate parameter vector to obtain the target parameter combination includes: A new population is generated by applying Gaussian perturbation or adaptive fine-tuning to some genes of the candidate parameter vector with a preset mutation probability. The new population is used as the current population, and the candidate parameter vector is used as the current parameter vector; Return to 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; Use the optimal current parameter vector at the time of termination as the target parameter combination.

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

6. The optical proximity effect correction method as described in claim 1, characterized in that, The step of generating the corrected layout based on the target parameter combination includes: The edge position of the initial correction graphic is adjusted according to the first distance in the target parameter combination; The position of the SRAF is adjusted according to the second distance in the target parameter combination; The adjusted initial correction pattern is integrated with the SRAF into the same mask layout to generate the correction layout.

7. An optical proximity effect correction device, characterized in that, include: Placement unit, used to place SRAF around the target pattern of the through-hole layer according to preset rules; The correction unit is used to perform OPC correction on the target graphic to generate an initial corrected graphic; The acquisition unit is used to acquire a first distance between each target graphic and its corresponding initial correction graphic, and a second distance between each target graphic and its corresponding SRAF. A construction unit is used to take the first distance and the second distance as optimization variables and calculate the 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; An iterative unit is used to iteratively solve the optimization variables based on the objective function using a genetic algorithm to obtain a combination of objective parameters; The generation unit is used to generate a modified layout based on the target parameter combination.

8. A storage medium, characterized in that, The storage medium stores a plurality of instructions adapted for loading by a processor to execute the optical proximity effect correction method according to any one of claims 1-6.

9. An electronic device, characterized in that, It includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, implements the optical proximity effect correction method as described in any one of claims 1-6.

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