Method of processing optical proximity correction data, medium, product and apparatus

Abstract

This invention relates to a method for processing optical proximity correction data, as well as its medium, product, and apparatus. The method includes: determining the target edge for optical proximity correction of a layout graphic, and obtaining the attribute information and coefficient of variation of the target edge; obtaining the edge position information of the target edge, and calculating the edge placement error value based on the edge position information; and storing the attribute information, coefficient of variation, edge position information, and edge placement error value of the target edge in a database according to a pre-constructed table hierarchy structure. This invention's method for processing optical proximity correction data not only facilitates quick access to key information, but also, due to the table hierarchy structure in the database, facilitates subsequent in-depth processing and efficient utilization of the data.

Description

Technical Field

[0001] This invention relates to the field of photolithography, and in particular to a method for processing optical proximity correction data, as well as its medium, product, and apparatus. Background Technology

[0002] In the precision semiconductor manufacturing process, optical proximity correction (OPC) can be used to pre-adjust and modify the patterns on photomasks, improving the accuracy and yield of semiconductor manufacturing. During OPC, the exposed contours must be checked for optical rules, edge placement error (EPE), and violations of mask rule checks (MRC).

[0003] Furthermore, in the optical proximity correction process, the consistency of the coefficient of variation is a key factor in ensuring semiconductor yield. The coefficient of variation reflects the degree of dispersion in pattern dimensions. Insufficient consistency will lead to deviations in line dimensions in different areas of the chip that exceed process requirements. This will not only cause problems such as pattern distortion and uneven linewidth, but may also cause device performance failures, such as leakage and short circuits, significantly reducing the yield of optical proximity correction.

[0004] Current methods for processing optical proximity correction (OPC) data still have some shortcomings. On the one hand, traditional OPC tools only output mask GDS files, and the data during the OPC process is stored in log form, which is inconvenient to view. On the other hand, the coefficient of variation (COP) data lacks a structured hierarchy, making it impossible to quickly locate the COP data and calculate the consistency at different locations. Summary of the Invention

[0005] One object of the present invention is to overcome at least one deficiency in the prior art and to provide a method for processing optical proximity correction data, a computer program product, a computer-readable storage medium, and a computer device.

[0006] A further objective of this invention is to store the attribute information, coefficient of variation, edge position information, and edge placement error value of the target edge in a database, so that users can easily view the data during the optical proximity correction process.

[0007] Another further objective of this invention is to construct the database into a three-level table hierarchy, including a primary table, secondary tables, and a secondary table, so that users can quickly locate the data they want to view.

[0008] Specifically, the present invention provides a method for processing optical proximity correction data, comprising: determining the target edge for optical proximity correction of a layout graphic, and obtaining the attribute information and coefficient of variation of the target edge; obtaining the edge position information of the target edge, and calculating the edge placement error value based on the edge position information; and storing the attribute information, coefficient of variation, edge position information, and edge placement error value of the target edge into a database according to a pre-constructed table hierarchy structure.

[0009] Optionally, the step of storing the attribute information, coefficient of variation, edge position information, and edge placement error value of the target edge in the database according to a pre-built table hierarchy includes: storing the attribute information in the primary table, which includes the category and quantity of the target edge; calculating the statistical values ​​of the coefficient of variation and the edge placement error based on the coefficient of variation and the edge placement error, and storing the statistical values ​​of the coefficient of variation and the edge placement error in the secondary table; and storing the coefficient of variation, edge position information, and edge placement error value in the final table; wherein the primary table, secondary table, and final table are linked hierarchically.

[0010] Optionally, the step of storing attribute information in the primary table includes: classifying the target edges for correction according to the surrounding topological features; counting the number of target edges for each type; and storing the type of target edges for correction and the number of target edges for each type as attribute information in the primary table.

[0011] Optionally, the step of classifying the target edge for correction according to the surrounding topological features includes: determining the adjacent edges of the target edge for correction based on the edge position information, and determining the size parameters of each adjacent edge, including: length parameters and included angle parameters; calculating the size parameters of the adjacent edges according to the geometric topological hash formula to obtain the topological feature value of the target edge for correction; and classifying the coefficient of variation, edge position information and edge placement error value with the same topological feature value into one category.

[0012] Alternatively, the geometric topological hash formula is: Where GTH represents the topological eigenvalue, L i θ represents the weight of the length of the i-th adjacent edge. i Let α represent the included angle of the i-th adjacent side, α represent the empirical coefficient corresponding to the length weight of the i-th adjacent side, and β represent the empirical coefficient corresponding to the included angle of the i-th adjacent side.

[0013] Optionally, the steps following the storage of the target edge's attribute information, coefficient of variation, edge position information, and edge placement error value into the database according to a pre-built table hierarchy include: generating a graphical user interface for displaying the optical proximity correction results; loading a primary table in the graphical user interface to display the attribute information; loading a secondary table in response to a selection operation on the primary table to display the coefficient of variation statistics and edge placement error statistics; and loading a final table in response to a selection operation on the secondary table to display the coefficient of variation, edge position information, and edge placement error value.

[0014] Optionally, the steps following obtaining the attribute information and coefficient of variation of the target edge to be corrected include: performing a consistency analysis on the coefficient of variation during the optical proximity correction process according to the following formula: Among them, CV i Denotes the i-th coefficient of variation. σ represents the average value of the coefficient of variation. env The sample standard deviation represents the coefficient of variation; if the sample standard deviation is less than the preset acceptable threshold, the coefficient of variation is determined to meet the consistency requirements.

[0015] According to another aspect of the invention, a computer program product is also provided, which, when executed by a processor, implements the steps of the processing method for optical proximity correction data according to any one of the foregoing claims.

[0016] According to another aspect of the present invention, a computer-readable storage medium is also provided, on which a computer program is stored, wherein the computer program, when executed by a processor, implements the steps of the processing method for optical proximity correction data according to any one of the preceding claims.

[0017] According to another aspect of the present invention, a computer device is also provided, including a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the processing method for optical proximity correction data according to any one of the preceding claims.

[0018] The optical proximity correction data processing method provided by this invention first determines the target edge for optical proximity correction of the layout graphic, and obtains the attribute information and coefficient of variation of the target edge. Then, it obtains the edge position information of the target edge and calculates the edge placement error value based on the edge position information. Finally, it stores the attribute information, coefficient of variation, edge position information, and edge placement error value of the target edge into a database according to a pre-constructed table hierarchy. Storing the data in a database during the optical proximity correction data processing not only facilitates quick retrieval of key information but also benefits subsequent in-depth data processing and efficient utilization.

[0019] Furthermore, the optical proximity correction data processing method provided by this invention stores the category and quantity of the target edge to be corrected in a primary table, the statistical values ​​of the coefficient of variation and the statistical values ​​of the edge placement error in a secondary table, and the coefficient of variation, edge position information, and edge placement error values ​​in a final table. The primary, secondary, and final tables are linked hierarchically. This three-level table structure allows personnel to quickly retrieve the corresponding level information as needed, eliminating the need to sift through massive amounts of data, thereby improving the efficiency of optical proximity correction data processing.

[0020] The above and other objects, advantages and features of the present invention will become more apparent to those skilled in the art from the following detailed description of specific embodiments of the invention in conjunction with the accompanying drawings. Attached Figure Description

[0021] The following sections will describe some specific embodiments of the invention in detail by way of example and not limitation, with reference to the accompanying drawings. The same reference numerals in the drawings denote the same or similar parts or portions. Those skilled in the art should understand that these drawings are not necessarily drawn to scale. In the drawings:

[0022] Figure 1 This is a flowchart illustrating a method for processing optical proximity correction data according to an embodiment of the present invention;

[0023] Figure 2 This is a flowchart illustrating the step of storing attribute information, coefficient of variation, edge position information, and edge placement error value into a database according to a pre-constructed table hierarchy in a method for processing optical proximity correction data according to an embodiment of the present invention.

[0024] Figure 3 This is a flowchart illustrating the step of storing attribute information into a primary table in a method for processing optical proximity correction data according to an embodiment of the present invention.

[0025] Figure 4 This is a flowchart illustrating the step of classifying the target edge of the optical proximity correction according to the surrounding topological features in a method for processing optical proximity correction data according to an embodiment of the present invention.

[0026] Figure 5 This is a flowchart illustrating the steps following the storage of attribute information, coefficient of variation, edge position information, and edge placement error value into a database according to a pre-constructed table hierarchy in a method for processing optical proximity correction data according to an embodiment of the present invention.

[0027] Figure 6 This is a schematic diagram of the data flow in a method for processing optical proximity correction data according to an embodiment of the present invention;

[0028] Figure 7 This is a schematic diagram of a computer program product according to an embodiment of the present invention;

[0029] Figure 8 This is a schematic diagram of a computer-readable storage medium according to an embodiment of the present invention; and

[0030] Figure 9 This is a schematic diagram of a computer device according to an embodiment of the present invention. Detailed Implementation

[0031] Those skilled in the art should understand that the embodiments described below are merely a part of the embodiments of the present invention, and not all of the embodiments of the present invention. These partial embodiments are intended to explain the technical principles of the present invention and are not intended to limit the scope of protection of the present invention. Based on the embodiments provided by the present invention, all other embodiments obtained by those skilled in the art without creative effort should still fall within the scope of protection of the present invention.

[0032] It should be noted that the logic and / or steps represented in the flowchart or otherwise described herein can be considered as a ordered list of executable instructions for implementing logical functions, which can be embodied in any computer-readable medium for use by an instruction execution system, apparatus or device (such as a computer-based system, a processor-based system or other system that can fetch and execute instructions from an instruction execution system, apparatus or device) or in conjunction with such instruction execution system, apparatus or device.

[0033] This embodiment provides a method for processing optical proximity correction data. For example... Figure 1 As shown, the method for processing optical proximity correction data includes at least the following steps S101 to S103.

[0034] Step S101: Determine the target edges for optical proximity correction (OPC) of the layout graphic, and obtain the attribute information and coefficient of variation of the target edges. During OPC, not all edges need to be corrected; target edges can be understood as edges that have undergone OPC. Obtaining the attribute information and coefficient of variation corresponding to these target edges allows for the elimination of data for edges that have not undergone OPC, avoiding the storage of redundant and useless data in the database.

[0035] Step S102 involves acquiring the edge position information of the target edge and calculating the edge placement error value based on this information. By quantifying the edge placement error, the optical proximity correction process has a clear target threshold, rather than relying on empirical correction. The step of acquiring the edge placement error value can improve the accuracy and stability of optical proximity correction while ensuring the consistency between the lithographic pattern and the layout pattern.

[0036] Step S103: Store the attribute information, coefficient of variation, edge position information, and edge placement error value of the target edge in the database according to a pre-built table hierarchy. By storing these data in the database, the data can be directly retrieved when needed, eliminating the need for manual data processing and thus improving the efficiency of the optical proximity correction process.

[0037] like Figure 2 As shown, the step of storing attribute information, coefficient of variation, edge position information, and edge placement error value into a database according to a pre-built table hierarchy in the optical proximity correction data processing method may include the following steps S201 to S203.

[0038] Step S201: Store the attribute information in the primary table. The attribute information includes the type and quantity of the target edges to be corrected. The primary table, as the top-level index of the database, stores attribute information such as the type and quantity of the target edges to be corrected. It provides a quick retrieval entry point with a global overview when data in the database needs to be retrieved. You can start from the primary table and query level by level to accurately obtain the data you want.

[0039] Step S202: Calculate the statistical values ​​of the coefficient of variation and edge placement error based on the coefficient of variation and edge placement error values, and store these values ​​in a secondary table. The secondary table stores statistical values ​​calculated using the coefficient of variation and edge placement error values, not the original data. These statistical values ​​can serve as indexes for further retrieval of coefficient of variation and edge placement error data. Furthermore, as an intermediate-level table, the secondary table balances retrieval efficiency and accuracy, improving the efficiency of optical proximity correction.

[0040] Step S203: Store the coefficient of variation, edge location information, and edge placement error values ​​in the final-level table. The primary, secondary, and final-level tables are linked hierarchically. The final-level table stores all collected coefficients of variation, edge location information, and edge placement error values, providing a data foundation for subsequent consistency analysis.

[0041] Furthermore, the three-level table structure, with primary, secondary, and final tables linked sequentially, allows for rapid location of the required data through step-by-step retrieval, which is beneficial for improving the efficiency of optical proximity correction.

[0042] like Figure 3 As shown, the step of storing attribute information in the primary table in the optical proximity correction data processing method may include the following steps S301 to S303.

[0043] Step S301: Classify the target edge to be corrected according to the surrounding topological features.

[0044] Step S302: Count the number of target edges for each type of correction.

[0045] Step S303: Store the type of the target edge to be corrected and the number of target edges of each type as attribute information in the primary table.

[0046] Edges with different topologies are significantly affected by diffraction and interference during optical proximity correction. For example, edges at corners are more prone to shape distortion than straight edges, and closely spaced adjacent edges are more likely to generate optical crosstalk than isolated edges. Therefore, classifying edges according to their surrounding topological characteristics can clearly define the category of the target edge to be corrected, making it less likely to be confused. This ensures that the categories and quantities in the primary table are clear and accurate, thus improving the accuracy of the data in the primary table.

[0047] like Figure 4 As shown, the step of classifying the target edge of the optical proximity correction data according to the surrounding topological features in the optical proximity correction data processing method may include the following steps S401 to S403.

[0048] Step S401: Determine the adjacent edges of the target edge to be corrected based on the edge position information, and determine the dimensional parameters of each adjacent edge. The dimensional parameters include: length parameters and included angle parameters. The degree of lithographic distortion of the target edge to be corrected (such as linewidth deviation and edge offset) is mainly determined by the spatial relationship of its surrounding adjacent edges. The length and included angle of the adjacent edges directly affect the light propagation path and interference results.

[0049] In this context, an adjacent edge can be understood as the edge that is adjacent to or closest to the target edge, or it can be understood as an adjacent edge that shares the same fixed point as the target edge.

[0050] Step S402: Calculate the size parameters of adjacent edges according to the geometric topological hash formula to obtain the topological feature value of the corrected target edge.

[0051] Step S403: The coefficient of variation, edge location information and edge placement error value with the same topological feature value are recorded as a class.

[0052] After calculating the dimensional parameters of adjacent edges using the geometric topological hash formula, topological feature values ​​are obtained. Identical topological feature values ​​mean that the lengths and angles of adjacent edges are within the equivalent range defined by the geometric topological hash formula, and their influence from the optical proximity effect (such as the changing trend of edge placement error values ​​with process fluctuations and the calculation model for required corrections) is highly consistent. Grouping these edges into one category ensures that similar edges use the same strategy (such as sharing correction parameters and algorithm models) during subsequent corrections, avoiding inconsistent correction effects due to classification errors.

[0053] In some optional embodiments, the geometric topological hash formula is: Where GTH represents the topological eigenvalue, L i θ represents the weight of the length of the i-th adjacent edge. i Let α represent the included angle of the i-th adjacent side, β represent the empirical coefficient corresponding to the length weight of the i-th adjacent side, and β represent the empirical coefficient corresponding to the included angle of the i-th adjacent side. The geometric topological hash formula quantifies complex geometric relationships into topological feature values ​​that guide precise correction, making classification more efficient and accurate.

[0054] like Figure 5 As shown, the steps after storing attribute information, coefficient of variation, edge position information, and edge placement error value into the database according to a pre-built table hierarchy in the optical proximity correction data processing method may include the following steps S501 to S504.

[0055] Step S501: Generate a graphical user interface for displaying the optical proximity correction results.

[0056] Step S502: Load the header table in the graphical user interface to display attribute information.

[0057] In step S503, in response to the selection operation for the primary table, the secondary table is loaded to display the coefficient of variation statistics and edge placement error statistics.

[0058] In step S504, in response to the selection operation for the secondary table, the final table is loaded to display the coefficient of variation, edge position information, and edge placement error value.

[0059] The graphical user interface (GUI) serves as a bridge between the three-level structured data and user operations, transforming data into a visual information carrier. The primary table displays attribute information; selection operations within the primary table load secondary tables. Secondary tables display statistical values ​​for the coefficient of variation and edge placement error; selection operations within these secondary tables load the final table, which displays the coefficient of variation, edge location information, and edge placement error value. Users can sequentially load the required coefficient of variation, edge location information, and edge placement error value by using the primary, secondary, and final tables.

[0060] Furthermore, the selection process through the graphical user interface (GUI) allows users to more intuitively choose the data to be loaded. For example, a user selects a category with the same topological feature values ​​in the primary table of the GUI. After selection, the GUI jumps to the next level, that is, to the secondary table. In the secondary table's GUI, the user can select a coefficient of variation statistic and an edge placement error statistic. After selection, the GUI jumps to the next level, that is, to the final table. In the final table, the required coefficient of variation, edge position information, and edge placement error values ​​can be precisely loaded.

[0061] In some optional embodiments, naming the primary, secondary, and final tables can better achieve the effect of hierarchical linking. For example, the primary table can be named checker_id, the secondary table category_id, and the final table segment_id. When locating specific data in the graphical user interface, quick links between levels can be established in the order of checker_id, category_id, and segment_id.

[0062] This method is beneficial for increasing the rate of optical proximity correction.

[0063] In some optional embodiments, a consistency analysis can be performed on the coefficient of variation during the optical proximity correction process. The formula for the consistency analysis is: Among them, CV i Denotes the i-th coefficient of variation. σ represents the average value of the coefficient of variation. env The sample standard deviation represents the coefficient of variation.

[0064] If the sample standard deviation is less than the preset acceptable threshold, the coefficient of variation is deemed to meet the consistency requirements. If the sample standard deviation is greater than or equal to the preset acceptable threshold, the coefficient of variation is deemed not to meet the consistency requirements. Quantifying the consistency of the coefficient of variation during optical proximity correction and comparing it with the preset acceptable threshold can accurately reveal the dispersion of the coefficient of variation itself, thereby improving the accuracy of the optical correction process.

[0065] In some alternative embodiments, such as Figure 6As shown, the data flow in the optical proximity correction data processing method is as follows: During the optical proximity correction process, attribute information, coefficient of variation, edge position information, and edge placement error values ​​are stored in a database. In the database, topological feature values ​​are calculated using a geometric topological hash formula. These feature values ​​are then used to classify the coefficient of variation, edge position information, and edge placement error values ​​in the database. The classified data is then stored in a primary table, a secondary table, and a final table. A three-level table browsing structure is generated in the graphical user interface, corresponding to the primary, secondary, and final tables, respectively. Users can load the attribute information, coefficient of variation, edge position information, and edge placement error values ​​stored in the database through the graphical user interface. This three-level table browsing structure in the graphical user interface, combined with the primary, secondary, and final tables, allows for more convenient and intuitive data loading, improving the efficiency of optical proximity correction.

[0066] In some optional embodiments, to facilitate subsequent database loading, the database type and its associated GDS can be bound, thereby better enabling database type analysis and data loading. For example, two databases, DB_V1 and DB_V2, correspond to two GDS files, V1.0 and V2.0, respectively. DB_V1 is bound to V1.0, and DB_V2 is bound to V2.0. When a user needs to analyze the correction data of version V2.0, the system can automatically match the corresponding GDS V2.0 file through the binding relationship, eliminating the need to manually search for the corresponding file. Simultaneously, it can directly retrieve information such as layout layers and graphic structures from the bound GDS, and perform linked analysis with data such as edge placement error values ​​and coefficients of variation in the database, avoiding version confusion and quickly completing data loading and correlation verification.

[0067] Further, still using the example of naming the primary table `checker_id`, the secondary table `category_id`, and the final table `segment_id`, the graphical user interface operation is as follows: the user loads the database onto the canvas, and simultaneously loads the GDS file from the optical proximity correction process onto the canvas. Double-clicking the detector accesses the primary table named `checker_id`, double-clicking `category_id` in the primary table accesses the specific secondary table, and double-clicking `segment_id` in the secondary table accesses the specific final table to load the required data.

[0068] In some alternative embodiments, statistical tools can also be set up on the graphical user interface to help accelerate the optical proximity correction process. For example, a filter tool can be set up on the graphical user interface to filter out the desired coefficients of variation from a large number of coefficients of variation, thereby increasing the rate of optical proximity correction.

[0069] The flowchart provided in this embodiment is not intended to indicate that the operations of the method will be performed in any particular order, or that all operations of the method are included in every case. Furthermore, the method may include additional operations. Within the scope of the technical concept provided by the method in this embodiment, additional variations can be made to the above method.

[0070] It should be understood that in some embodiments, the components may be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods may be implemented using software or firmware stored in memory and executed by a suitable instruction execution system.

[0071] This embodiment also provides a computer program product 10, a computer-readable storage medium 20, and a computer device 30. Figure 7 This is a schematic diagram of a computer program product 10 according to an embodiment of the present invention. Figure 8 This is a schematic diagram of a computer-readable storage medium 20 according to an embodiment of the present invention. Figure 9 This is a schematic diagram of a computer device 30 according to an embodiment of the present invention. The computer program product 10 includes a computer program 11, which, when executed by the processor 32, implements the steps of the optical proximity correction data processing method described above. A computer-readable storage medium 20 stores the computer program 11 thereon, which, when executed by the processor 32, implements the steps of the optical proximity correction data processing method described above. The computer device 30 may include a memory 31, a processor 32, and the computer program 11 stored in the memory 31 and running on the processor 32.

[0072] The computer program 11 used to perform the operations of this invention may be assembly instructions, Instruction Set Architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, status setting data, integrated circuit configuration data, or source code or object code written in any combination of one or more programming languages ​​and procedural programming languages. The computer program 11 may execute entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the latter case, the remote computer may be connected to the user's computer via any type of network, including a Local Area Network (LAN) or Wide Area Network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider). In some embodiments, to perform aspects of this invention, electronic circuits, including, for example, programmable logic circuits, Field-Programmable Gate Arrays (FPGAs), or Programmable Logic Arrays (PLAs), may execute computer-readable program instructions using status information from computer-readable program instructions to personalize the electronic circuits.

[0073] For the purposes of this embodiment, computer program product 10 is a related product containing computer program 11. For the purposes of this embodiment, computer-readable storage medium 20 is a tangible device capable of holding and storing computer program 11, and can be any device capable of containing, storing, communicating, propagating, or transmitting program 11 for use by or in conjunction with an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable storage medium 20 include: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), static random access memory (SRAM), portable optical disc read-only memory (CD-ROM), digital versatile disc (DVD), memory stick, floppy disk, mechanical encoding device, and any suitable combination thereof.

[0074] Computer device 30 can be, for example, a server, desktop computer, laptop computer, tablet computer, or smartphone. In some examples, computer device 30 can be a cloud computing node. Computer device 30 can be described in the general context of computer system executable instructions (such as program modules) executed by a computer system. Typically, program modules can include routines, programs, object programs, components, logic, data structures, etc., that perform specific tasks or implement specific abstract data types. Computer device 30 can be implemented in a distributed cloud computing environment where tasks are performed by remote processing devices linked through a communication network. In a distributed cloud computing environment, program modules can reside on local or remote computing system storage media, including storage devices.

[0075] Computer device 30 may include a processor 32 adapted to execute stored instructions and a memory 31 that provides temporary storage space for the operation of instructions during operation. The processor 32 may be a single-core processor, a multi-core processor, a computing cluster, or any other configuration. The memory 31 may include random access memory (RAM), read-only memory, flash memory, or any other suitable storage system.

[0076] Computer device 30 may also include a network adapter / interface and an input / output (I / O) interface. The I / O interface allows external devices that can be connected to the computer device to input and output data. The network adapter / interface provides communication between the computer device and a network, typically represented as a communication network.

[0077] Therefore, those skilled in the art should recognize that although numerous exemplary embodiments of the present invention have been shown and described in detail herein, many other variations or modifications conforming to the principles of the present invention can be directly determined or derived from the disclosure of the present invention without departing from the spirit and scope of the invention. Thus, the scope of the present invention should be understood and construed as covering all such other variations or modifications.

Claims

1. A method for processing optical proximity correction data, characterized in that, include: Identify the target edge for optical proximity correction of the layout graphic, and obtain the attribute information and coefficient of variation of the target edge; Obtain the edge position information of the target edge to be corrected, and calculate the edge placement error value based on the edge position information; The attribute information, the coefficient of variation, the edge position information, and the edge placement error value of the target edge to be corrected are stored in the database according to a pre-constructed table hierarchy structure; The step of storing the attribute information, the coefficient of variation, the edge position information, and the edge placement error value of the corrected target edge into the database according to a pre-constructed table hierarchy includes: The attribute information is stored in the primary table, and the attribute information includes the category and quantity of the target edge to be corrected; The statistical values ​​of the coefficient of variation and the edge placement error are calculated based on the coefficient of variation and the edge placement error, and the statistical values ​​of the coefficient of variation and the edge placement error are stored in the secondary table. The coefficient of variation, the edge position information, and the edge placement error value are stored in the final table; wherein the first table, the second table, and the final table are linked level by level.

2. The method for processing optical proximity correction data according to claim 1, characterized in that, The step of storing the attribute information into the primary table includes: The target edges for correction are classified according to their surrounding topological features; Count the number of the corrected target edges for each category; The category of the target edge to be corrected and the number of target edges of each category are stored as attribute information in the primary table.

3. The method for processing optical proximity correction data according to claim 2, characterized in that, The step of classifying the modified target edge according to its surrounding topological features includes: The adjacent edges of the target edge to be corrected are determined based on the edge position information, and the size parameters of each adjacent edge are determined, including: length parameters and included angle parameters; The size parameters of the adjacent edges are calculated according to the geometric topological hash formula to obtain the topological feature value of the corrected target edge; The coefficients of variation, edge location information, and edge placement error values ​​that have the same topological feature value are grouped into one category.

4. The method for processing optical proximity correction data according to claim 3, characterized in that, The geometric topological hash formula is as follows: ； Wherein, GTH represents the topological feature value. This represents the weight of the length of the i-th adjacent edge. This represents the included angle between the i-th and i-th adjacent sides. This represents the empirical coefficient corresponding to the length weight of the i-th adjacent edge. This represents the empirical coefficient corresponding to the included angle of the i-th adjacent side.

5. The method for processing optical proximity correction data according to claim 1, characterized in that, The steps following the storage of the attribute information, the coefficient of variation, the edge position information, and the edge placement error value of the corrected target edge into the database according to a pre-constructed table hierarchy include: Generate a graphical user interface to display the results of optical proximity correction; The header table is loaded in the graphical user interface to display the attribute information; In response to a selection operation on the primary table, the secondary table is loaded to display the coefficient of variation statistics and the edge placement error statistics; In response to a selection operation on the secondary table, the final table is loaded to display the coefficient of variation, the edge position information, and the edge placement error value.

6. The method for processing optical proximity correction data according to claim 1, characterized in that, The steps following the acquisition of the attribute information and coefficient of variation of the target edge to be corrected include: In the optical proximity correction process, the coefficient of variation is analyzed for consistency according to the following formula: ； in, Denotes the i-th coefficient of variation. The sample standard deviation represents the coefficient of variation. This represents the average value of the coefficients of variation; where, If the sample standard deviation is less than the preset pass / fail threshold, then the coefficient of variation is determined to meet the consistency requirements.

7. A computer program product, comprising a computer program, characterized in that, When executed by a processor, the computer program implements the steps of the method for processing optical proximity correction data as described in any one of claims 1 to 6.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that... When the computer program is executed by a processor, it implements the steps of the method for processing optical proximity correction data as described in any one of claims 1 to 6.

9. A computer device, comprising a memory, a processor, and a computer program stored in the memory, characterized in that, The processor executes the computer program to implement the steps of the method for processing optical proximity correction data according to any one of claims 1 to 6.

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