Mask design layout blur detection method, storage medium and graphic repair device

Abstract

The present application relates to mask design layout fuzzy detection technology field, especially in a kind of mask design layout fuzzy detection method, storage medium and graphic repair equipment.The method includes the following steps: providing initial mask design layout, obtaining the graphic point information of initial mask design layout;Providing a preset bad point database, the bad point graphic is broken into several graphics consisting of rectangle in the bad point database by design rule check, to blur classification is carried out to bad point information, and the bad point information after blur classification is obtained;The graphic point information in initial mask design layout is matched with the bad point information in the preset bad point database, if matching is successful, then it is judged that there is bad point in initial mask design layout.The present application solves the technical problem that the pattern in mask is usually missing or modified in a large number of small, the probability of matching to pattern by accurate pattern matching method is greatly reduced.

Description

[Technical Field]

[0001] This invention relates to the field of mask layout detection technology, and in particular to a method for detecting blurred mask design layouts, a storage medium, and a graphic repair device. [Background Technology]

[0002] In the manufacturing process of semiconductor integrated circuits, it is necessary to model the design layout of the chip photomask multiple times and modify the bad pixels generated during modeling in order to repair potential defects in the mask design.

[0003] Simulating chip mask designs is complex and time-consuming, and modifying masks is a repetitive and tedious process. Since mask designs are usually repetitive, the areas where defects occur are also repetitive. Therefore, applying pattern matching technology can significantly shorten the time and process for detecting mask defects.

[0004] While mask layouts are repetitive, they also contain numerous minor differences or missing patterns. This results in different hash check values, making it impossible to classify bad pixels and detect their locations. The probability of a precise pattern matching method matching the pattern is greatly reduced, making it impossible to effectively reduce the OPC (Optical Proximity Correction) process and the number of layout modifications.

[0005] To address this, the present invention introduces DRC (Design Rule Check) result information of the mask design layout during the pattern matching process, performs fuzzy classification of bad pixels in LRC (Lithography Rule Check), establishes a bad pixel database, and uses the bad pixels in the bad pixel database to perform fuzzy matching on the new mask design layout, thereby finding potential LRC bad pixels in the mask design layout. [Summary of the Invention]

[0006] To address the technical problem that patterns in masks often have missing parts or numerous minor modifications, significantly reducing the probability of accurate pattern matching methods matching the patterns, this invention provides a mask design layout blur detection method, storage medium, and pattern repair device.

[0007] The solution to the technical problem of this invention is to provide a method for detecting blur in mask design layouts, comprising the following steps:

[0008] Provide an initial mask design layout and obtain the graphic point information of the initial mask design layout;

[0009] It provides a preset bad pixel database. In the bad pixel database, the bad pixel image is broken into several rectangles by design rules to perform fuzzy classification of bad pixel information and obtain the fuzzy classified bad pixel information.

[0010] The graphic point information in the initial mask design layout is matched with the bad point information in the preset bad point database. If the match is successful, it is determined that there are bad points in the initial mask design layout.

[0011] Preferably, constructing a bad pixel database specifically includes the following steps:

[0012] Perform photolithography rule checks on the graphic point information in the mask design layout to extract bad point information;

[0013] Fuzzy classification is performed on the defect information extracted from photolithography rule inspection;

[0014] The bad pixel information after fuzzy classification is stored in the bad pixel database.

[0015] Preferably, the fuzzy classification of bad pixel information includes the following steps:

[0016] By using DRC rules, we can check for non-compliant parts in bad pixel images to identify weak points in width and gap relationships, and break all polygons in the graphic frame into shapes composed of rectangles.

[0017] Preferably, after extracting the bad pixel information and before performing fuzzy classification, the following steps are also included:

[0018] The bad pixel image in the bad pixel information is expanded outward to obtain the expanded bad pixel image;

[0019] Both the images of the bad pixels before and after the expansion are saved together in the bad pixel information.

[0020] Preferably, storing the fuzzy-classified defective pixel information into the defective pixel database requires the following steps:

[0021] The design layout of the bad pixel graphic is cut out using the bounding box of a graphic in each bad pixel level, and the design layout is stored in GDS file format.

[0022] Preferably, the matching rule for bad pixel information in the preset bad pixel database is fuzzy matching, specifically including the following steps:

[0023] The images in the bad pixel database are divided into several rectangles according to the first preset splitting rule;

[0024] The initial mask design layout is divided into several rectangles according to the second preset splitting rule.

[0025] Select rectangles whose width and spacing are within the preset range;

[0026] Calculate the position information of the rectangles obtained by the two splitting rules within the graphics frame, and calculate the size information of these rectangles;

[0027] Based on the position information of the rectangles obtained from the initial mask design layout splitting within the graphic frame, the possible graphic positions are deduced.

[0028] The rectangles obtained by splitting the initial mask design layout are matched with the rectangles obtained by splitting the graphics in the bad pixel database in terms of position and size information, and the number of matching rectangles is calculated.

[0029] Preferably, the first preset splitting rule and / or the second preset splitting rule are DRC rules.

[0030] Preferably, the above method further includes the following steps after matching:

[0031] Continue to match the graphic point information in the initial mask design layout with the bad point information in the preset bad point database. If the match is successful, then correct the initial mask design layout.

[0032] When a match fails, output the corrected mask design layout.

[0033] The present invention also provides a computer-readable storage medium having stored thereon computer program instructions, which, when executed by a processor, implement the above-described mask design layout blur detection method.

[0034] The present invention also provides a graphic restoration device, including a device running program and a device body that executes the device running program. When the device body executes the device running program, it implements the above-mentioned mask design layout blur detection method.

[0035] Compared with existing technologies, the mask design layout blur detection method, storage medium, and graphic repair device provided by this invention have the following advantages:

[0036] 1. This invention provides a method for fuzzy detection of mask design layouts, comprising the following steps: providing an initial mask design layout and obtaining graphic point information of the initial mask design layout; providing a preset bad pixel database, and breaking the bad pixel graphics into several rectangles in the bad pixel database by design rules to perform fuzzy classification of the bad pixel information, thereby obtaining fuzzy classified bad pixel information; matching the graphic point information in the initial mask design layout with the bad pixel information in the preset bad pixel database, and if the match is successful, determining that there are bad pixels in the initial mask design layout.

[0037] It is understandable that the patterns in the mask often have missing parts or a lot of minor modifications, which greatly reduces the probability of matching the pattern using precise pattern matching methods.

[0038] Understandably, the defective pixel database of the present invention includes defective pixel information. By matching the defective pixels in the defective pixel database with the initial mask design layout, the initial mask design layout can be better detected by the defective pixels in the defective pixel database. This avoids the repeated use of OPC (Optical Proximity Correction) to check and repair defective pixels in the mask design layout, simplifies the defective pixel inspection process, and greatly improves the detection efficiency.

[0039] It should be noted that this invention also uses the DRC (Design Rule Check) method to perform fuzzy classification of bad pixel information in the bad pixel database, breaking the bad pixel image into a graphic composed of several rectangles, determining the degree of matching by matching rectangles, and then setting a threshold so that similar but different bad pixel images can be represented by a single graphic, simplifying the number of bad pixels in the database and reducing the number of graphics to be matched for subsequent bad pixel images.

[0040] 2. The mask design layout fuzzy detection method provided in this embodiment of the invention, which constructs a bad pixel database, specifically includes the following steps: performing photolithography rule checks on the graphic point information in the mask design layout to extract bad pixel information; performing fuzzy classification on the bad pixel information extracted by the photolithography rule checks; and storing the fuzzy classified bad pixel information into the bad pixel database.

[0041] Understandably, the present invention establishes a bad pixel database based on the required bad pixels or past experience. This bad pixel database mainly includes bad pixel information for the initial mask design layout. By using the required bad pixels, it can be ensured that no redundant matching is needed in the subsequent matching process. Subsequently, the bad pixels existing on the mask design layout can be directly replaced and corrected without the need for simulation correction again. This saves time and reduces the resources consumed by matching operations, and avoids the problem of correction errors.

[0042] 3. The mask design layout fuzzy detection method provided in this embodiment of the invention includes the following steps for fuzzy classification of bad pixel information: by using DRC rules, checking the parts of the bad pixel image that do not conform to the rules to obtain weak points in width and gap relationships, and breaking all polygons in the graphic frame into graphics composed of rectangles.

[0043] It should be noted that this invention introduces DRC (Design Rule Check) result information of the mask design layout during the pattern matching process, performs fuzzy classification of bad pixels in LRC (Lithography Rule Check), establishes a bad pixel database, and uses the bad pixels in the bad pixel database to perform fuzzy matching on the new mask design layout, thereby finding potential LRC bad pixels in the mask design layout.

[0044] Understandably, the present invention can select bad pixels from the bad pixel library based on the initial mask design layout or the type of bad pixels in the mask design layout to be repaired, thereby selecting the bad pixels in the bad pixel library to match with the mask design layout to be repaired. Through this design, unnecessary calculations can be reduced, saving time and resources.

[0045] 4. The mask design layout fuzzy detection method provided in this embodiment of the invention further includes the following steps after extracting bad pixel information and before performing fuzzy classification: expanding the bad pixel image in the bad pixel information outward to obtain the expanded bad pixel image; and saving the bad pixel images before and after expansion together into the bad pixel information.

[0046] Understandably, simulating chip mask design is complex and time-consuming, and modifying the mask is also a repetitive and tedious process. Since the pattern of mask design layout is usually repetitive, the locations where bad pixels will occur are also repetitive.

[0047] It should be noted that while the patterns in the mask design layout are repetitive, there are also a large number of minor differences or missing patterns, which leads to different hash check values ​​and makes it impossible to classify bad pixels and detect their locations. By expanding the bad pixel image in the bad pixel information outward, the probability of successful matching can be improved.

[0048] 5. The mask design layout fuzzy detection method provided in this embodiment of the invention stores the fuzzy classified bad pixel information into a bad pixel database, which requires the following steps: using the bounding box of a graphic in each bad pixel level to cut out the design layout of the bad pixel graphic, and storing the design layout in GDS file format.

[0049] Understandably, bad pixel information stored in the bad pixel database is pre-cut out using the bounding box of the graphics to facilitate the matching of bad pixel information in the subsequent mask design layout. It should be noted that the GDS file format is the most commonly used graphic data description language file format in integrated circuit board design.

[0050] 6. The mask design layout fuzzy detection method provided in this embodiment of the invention uses fuzzy matching rules with bad pixel information in a preset bad pixel database. Specifically, it includes the following steps: dividing the graphic in the bad pixel database into several rectangles using a first preset splitting rule; dividing the initial mask design layout into several rectangles using a second preset splitting rule, and selecting rectangles whose width and gaps are within a preset range; calculating the position information of the rectangles obtained by the two splitting rules within the graphic frame, and calculating the size information of these rectangles; inferring possible graphic positions based on the position information of the rectangles obtained by splitting the initial mask design layout within the graphic frame; matching the position and size information of the rectangles obtained by splitting the initial mask design layout with the rectangles obtained by splitting the graphic in the bad pixel database, and calculating the number of matching rectangles.

[0051] Understandably, this invention introduces a fuzzy matching method, which enables the matching of mask design layouts with incomplete polygons and certain modifications when matching graphics, thereby increasing the probability of successful matching.

[0052] It should be noted that when a large number of bad pixels need to be detected, fuzzy matching can be used to reduce the matching time and thus achieve the best matching effect. At the same time, it can also reduce unnecessary calculations.

[0053] 7. In the mask design layout blur detection method provided in the embodiments of the present invention, the first preset splitting rule and / or the second preset splitting rule are DRC rules.

[0054] Understandably, the first preset splitting rule is used to split the graphics in the bad pixel database into several rectangles, and the second preset splitting rule is used to split the initial mask design layout into several rectangles, selecting rectangles whose width and gaps are within the preset range.

[0055] It should be noted that by using DRC rules to perform fuzzy classification of the graphics in the bad pixel database and the graphics in the initial mask design layout, and determining the degree of matching by the matching rectangles, the number of bad pixels in the bad pixel database can be simplified by setting a threshold, or by allowing similar but different bad pixel graphics to be represented by the same graphic, thereby reducing the number of graphics to be matched for subsequent bad pixel graphics.

[0056] 8. The mask design layout blur detection method provided in the embodiments of the present invention further includes the following steps after matching: continuing to match the graphic point information in the initial mask design layout with the bad point information in the preset bad point database; if the matching is successful, the initial mask design layout is corrected; if the matching fails, the corrected mask design layout is output.

[0057] Understandably, after completing the initial replacement and correction of defective pixels in the image, the corrected mask design layout can be output without performing simulation calculations on the initial mask design layout. This design saves time in repairing the mask design layout, reduces resource usage, and avoids wasting resources.

[0058] For the overall design pattern, after simulating key areas and matching defective pixel patterns in the mask design layout with a defective pixel database, the correction points corresponding to the defective pixels can be used to replace and correct the pixels to be corrected in the mask design layout. This design can significantly save time, and only requires a small amount of computation to correct the mask design layout, greatly saving resources. In essence, directly replacing the parts of the initial mask design layout that successfully match defective pixels in the matching library with the corresponding correction points reduces the time required to repair the mask design layout.

[0059] 9. The present invention also provides a computer-readable storage medium storing computer program instructions thereon, which, when executed by a processor, implement the above-described mask design layout blur detection method.

[0060] This computer-readable storage medium has the same beneficial effects as the mask design layout blur detection method described above, and will not be elaborated here.

[0061] 10. This embodiment of the invention also provides a graphic repair device, including a device running program and a device body that executes the device running program. When the device body executes the device running program, it implements the above-mentioned mask design layout blur detection method.

[0062] This graphic restoration device has the same beneficial effects as the mask design layout blur detection method described above, and will not be elaborated here. [Attached Image Description]

[0063] To more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0064] Figure 1 This is a flowchart illustrating a mask design layout blur detection method provided in the first embodiment of the present invention.

[0065] Figure 2 This is a flowchart illustrating step S2 in a mask design layout blur detection method provided in the first embodiment of the present invention.

[0066] Figure 3 This is a schematic diagram of step S22 of a mask design layout blur detection method provided in the first embodiment of the present invention, in which the points that make up the rectangle are obtained through DRC (Design Rule Check).

[0067] Figure 4 This is a schematic diagram illustrating step S22 of a mask design layout blur detection method provided in the first embodiment of the present invention, in which the polygon in the graphic frame is broken into a graphic composed of rectangles.

[0068] Figure 5 This is a schematic diagram illustrating the step S22 of a mask design layout blur detection method provided in the first embodiment of the present invention, which involves checking the number of intersections of the center distances of rectangles in the graphic.

[0069] Figure 6 This is a schematic diagram of the process executed after step S21 and before step S22 in a mask design layout blur detection method provided in the first embodiment of the present invention.

[0070] Figure 7 This is a flowchart illustrating step S3 in a mask design layout blur detection method provided in the first embodiment of the present invention.

[0071] Figure 8 This is a schematic diagram of the graphic frame derived from the rectangle in step S34 of a mask design layout blur detection method provided in the first embodiment of the present invention.

[0072] Figure 9 This is a schematic diagram of the process executed after step S3 in a mask design layout blur detection method provided in the first embodiment of the present invention.

[0073] Figure 10 This is a schematic diagram of the framework of a computer-readable storage medium provided in the second embodiment of the present invention.

[0074] Figure 11 This is a schematic diagram of the frame of a graphic restoration device provided in the third embodiment of the present invention.

[0075] Explanation of reference numerals in the attached diagram:

[0076] 1. Computer-readable storage medium; 11. Computer program instructions;

[0077] 2. Graphic restoration equipment; 21. Equipment operating program; 22. Equipment body.

Detailed Implementation Methods

[0078] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0079] In the embodiments provided by this invention, it should be understood that "B corresponding to A" means that B is associated with A, and B can be determined based on A. However, it should also be understood that determining B based on A does not mean determining B solely based on A; B can also be determined based on A and / or other information.

[0080] It should be understood that the phrase "one embodiment" or "an embodiment" throughout the specification means that a specific feature, structure, or characteristic related to the embodiment is included in at least one embodiment of the invention. Therefore, "in one embodiment" or "in an embodiment" appearing throughout the specification does not necessarily refer to the same embodiment. Furthermore, these specific features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. Those skilled in the art should also recognize that the embodiments described in the specification are optional embodiments, and the actions and modules involved are not necessarily essential to the invention.

[0081] In various embodiments of the present invention, it should be understood that the sequence number of each process does not necessarily imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0082] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, or they may sometimes be executed in reverse order, depending on the functions involved. It is particularly important to note that each block in a block diagram and / or flowchart, and combinations of blocks in block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0083] The technical solution of this invention incorporates DRC (Design Rule Check) results information of the mask design layout into the pattern matching process, performs fuzzy classification of defects from LRC (Lithography Rule Check), establishes a defect database, and uses the defects in the database to match new mask design layouts using a fuzzy pattern matching method, thereby finding potential LRC defects in the mask design layout. The technical solution involved in this invention will be described in detail below.

[0084] For details, please refer to Figure 1 The first embodiment of the present invention provides a mask design layout blur detection method, comprising the following steps:

[0085] S1: Provide the initial mask design layout and obtain the graphic point information of the initial mask design layout;

[0086] S2: Provides a preset bad pixel database. In the bad pixel database, the bad pixel image is broken into several rectangles by design rules to perform fuzzy classification of bad pixel information and obtain the fuzzy classified bad pixel information.

[0087] S3: Match the graphic point information in the initial mask design layout with the bad point information in the preset bad point database. If the match is successful, it is determined that there are bad points in the initial mask design layout.

[0088] It is understandable that the patterns in a mask often have missing parts or numerous minor modifications. If an exact matching algorithm is used, only absolutely identical patterns can be matched successfully, significantly reducing the probability of matching patterns using an exact pattern matching method. However, the defective pixel database provided in the first embodiment of this invention includes defective pixel information, which stores a large number of defective pixels. The initial mask design layout is then matched with the defective pixels in the defective pixel database. As a preferred solution in the first embodiment, this invention uses a fuzzy pattern matching method for matching. Through this design, defective pixels in the database can be used to better detect defective pixels in the initial mask design layout, avoiding the repeated use of OPC (Optical Proximity Correction) to check and repair defective pixels in the mask design layout, simplifying the defective pixel inspection process, and significantly improving the detection efficiency.

[0089] It's important to note that OPC (Optical Proximity Correction) stands for Optical Proximity Correction, also known as lithography correction. OPC technology is used to correct deviations in lithographic patterns during chip manufacturing. It primarily addresses non-ideal effects in the optical image formation process, such as optical scattering and refraction, by introducing minute variations into the lithographic pattern. The main goal of OPC is to improve parameters such as chip linewidth control, pattern alignment, and depth of focus, thereby enhancing chip manufacturing precision and performance.

[0090] Furthermore, this invention also uses the DRC (Design Rule Check) method to perform fuzzy classification of bad pixel information in the bad pixel database, breaking the bad pixel image into a graphic composed of several rectangles. The degree of matching is determined by the matching rectangles, and then by setting a threshold, similar but different bad pixel images can be represented by a single image, simplifying the number of bad pixels in the database and reducing the number of images to be matched with subsequent bad pixel images.

[0091] The threshold represents the proportion of the matching rectangle, and the threshold is set manually. The higher the threshold is set, the less blurred the matched rectangle is; the lower the threshold is set, the more blurred the matched rectangle is. By using the bad pixel images in the bad pixel database as a benchmark to match the image point information in the initial mask design layout, the number of OPC process and layout modifications can be effectively reduced, thereby increasing the probability of successful matching.

[0092] Specifically, when the set threshold is 100%, it can be approximated as an exact match. That is, the technical solution provided in this application can satisfy both fuzzy matching and exact matching of bad pixel information.

[0093] DRC (Design Rule Check) is a fundamental function in Electronic Design Automation (EDA) tools used to check whether a design meets manufacturing process requirements, ensuring its correctness and manufacturability. DRC checks cover aspects such as line width, spacing, area, and vias, primarily addressing design process limitations. These limitations are determined based on the actual process to ensure the final manufactured chip meets specifications. Specifically, the DRC check process generally consists of two parts: "basic rule checks" and "special rule checks." Basic rule checks typically examine the most fundamental rules in chip design, such as interconnect width, line spacing, and device spacing. Special rule checks, on the other hand, adjust the rules based on the actual chip design to achieve optimal manufacturing results.

[0094] In the chip manufacturing process, the DRC inspection step is very important because any manufacturing problem can lead to the damage of the finished chip, and the damage of the finished chip will consume huge production and design costs.

[0095] As an optional implementation, please refer to Figure 2 The construction of a bad pixel database includes the following steps:

[0096] S21: Perform lithography rule checks on the graphic point information in the mask design layout and extract bad point information;

[0097] S22: Perform fuzzy classification on the defect information extracted from the photolithography rule inspection;

[0098] S23: Store the fuzzy-classified bad pixel information into the bad pixel database.

[0099] Understandably, the present invention establishes a bad pixel database based on the required bad pixels or past experience. This bad pixel database mainly includes bad pixel information for the initial mask design layout.

[0100] Furthermore, by using the required defective points, it can be ensured that no redundant matching is needed in the subsequent matching process. This allows for direct replacement and correction of defective points on the mask design layout without the need for further simulation and correction. This saves time, reduces the resources consumed by matching operations, and avoids the problem of correction errors.

[0101] In step S22, the fuzzy classification of bad pixel information includes the following steps:

[0102] By using DRC rules, we can check for non-compliant parts in bad pixel images to identify weak points in width and gap relationships, and break all polygons in the graphic frame into shapes composed of rectangles.

[0103] It should be noted that the weak points in the width and gap relationship refer to placing detection points on the simulated image and checking the values ​​of the width and / or gaps at the detection points. When the values ​​do not meet the set threshold, the detection point is marked as a bad point. The threshold is set manually, that is, the proportion of the matching rectangle. The higher the threshold is set, the less blurry the matched rectangle is. When the threshold is set to 100%, it can be approximated as an exact match.

[0104] For details, please refer to Figure 3 and Figure 4In the preferred embodiment of the first embodiment of the present invention, the bad pixel information extracted after LRC (lithography rule check) is fuzzy classified, and weak points in the width and gap relationship are obtained through DRC (design rule check). Specifically, a rectangle is constructed by projecting the two endpoints of the short side and the two points on the long side corresponding to the projection of the short side onto the long side. Then, all polygons in the graphic frame are broken into graphics composed of rectangles.

[0105] LRC (Lithography Rule Checking) refers to the process of identifying potential manufacturability issues in a chip design before implementing the lithography process by using a lithography imaging model, thereby minimizing the chances of integrated circuit manufacturing failure.

[0106] Furthermore, after breaking down all polygons in the graphic frames into shapes composed of rectangles, the specific method for fuzzy classification of defective pixels is as follows:

[0107] Record the length and height of all rectangles within each graphic frame. Set the length and height of the rectangles in one graphic frame to length1 and height1 respectively, and the length and height of the rectangles in another graphic frame to length2 and height2 respectively.

[0108] If the rectangles in the two figure frames satisfy...

[0109] length1=length2∧height1=height2

[0110] or

[0111] length1=height2∧height1=length2

[0112] Then the two rectangles are considered equal, where ∧ is the logical OR operator.

[0113] If the number of equal rectangles exceeds the set threshold, which is the number of rectangles within the graphic frame multiplied by the tolerance (the proportion of different rectangles that can be tolerated), then the next step of the judgment operation can be performed.

[0114] The thresholds and tolerances set above can be adjusted according to different application scenarios or usage environments. This invention does not limit the thresholds and tolerances, and they can be set according to specific actual conditions. Any modifications, equivalent substitutions, and improvements made within the principles of this invention should be included within the protection scope of this invention.

[0115] Please see Figure 5Check the number of intersections of the center distances of rectangles in the figure. For a figure that should have n intersecting rectangles, if the number of intersections exceeds [a certain threshold], [the check will fail]. Then the two graphics are determined to be a successful match.

[0116] It should be further explained that the present invention introduces the DRC (Design Rule Check) result information of the mask design layout during the pattern matching process, performs fuzzy classification of bad points in LRC (Lithography Rule Check), establishes a bad point database, and uses the bad points in the bad point database to perform fuzzy matching on the new mask design layout, thereby finding potential LRC bad points in the mask design layout.

[0117] Understandably, the present invention can select bad pixels from the bad pixel library based on the initial mask design layout or the type of bad pixels in the mask design layout to be repaired, thereby selecting the bad pixels in the bad pixel library to match with the mask design layout to be repaired. Through this design, unnecessary calculations can be reduced, saving time and resources.

[0118] For details, please refer to Figure 6 After extracting the bad pixel information and before performing fuzzy classification, the following steps are also included:

[0119] S211: Expand the bad pixel image in the bad pixel information outward to obtain the expanded bad pixel image;

[0120] S212: Save both the images of the bad pixels before and after expansion into the bad pixel information.

[0121] Understandably, simulating chip mask design is complex and time-consuming, and modifying the mask is a repetitive and tedious process. Since the pattern of mask design layout is usually repetitive, the locations where bad pixels will occur are also repetitive. It should be noted that expanding the bad pixel image outward is done by the user manually setting values ​​to expand the bad pixel image by the same length in four directions: upward, downward, left, and right, forming a square. This is because the closer the mask design layout image is to the center, the greater its impact on bad pixels.

[0122] It should be further explained that photolithography rule checking simulates the photolithography process to obtain an approximate real image printed on the silicon wafer. While the mask design layout has repetitive patterns, it also has a large number of small differences or missing patterns, which leads to different hash check values ​​and makes it impossible to classify bad pixels and detect their location. By expanding the bad pixel image in the bad pixel information outward, the probability of successful matching can be improved.

[0123] Furthermore, storing the fuzzy-classified defective pixel information into the defective pixel database requires the following steps:

[0124] The design layout of the bad pixel graphic is cut out using the bounding box of one graphic in each bad pixel level, and the design layout is saved in GDS file format.

[0125] Understandably, bad pixel information stored in the bad pixel database is pre-cut out using the bounding box of the graphic to facilitate the matching of bad pixel information in the initial mask design layout.

[0126] It should be noted that the GDS file format is the most commonly used graphical data description language file format in integrated circuit board design; as a standard file format in semiconductor chip design, GDS files are used to store chip layout information.

[0127] As another alternative implementation, please refer to Figure 7 The matching rule for bad pixel information in the preset bad pixel database is fuzzy matching, which specifically includes the following steps:

[0128] S 31: Divide the image in the bad pixel database into several rectangles according to the first preset splitting rule;

[0129] S 32: Divide the initial mask design layout into several rectangles according to the second preset splitting rule, and select the rectangles whose width and gap are within the preset range;

[0130] S 33: Calculate the position information of the rectangles obtained by the two splitting rules within the graphics frame, and calculate the size information of these rectangles;

[0131] S 34: Based on the position information of the rectangle obtained by splitting the initial mask design layout within the graphic frame, infer the possible graphic position;

[0132] S 35: Match the position and size information of the rectangles obtained by splitting the initial mask design layout with the rectangles obtained by splitting the graphics in the bad pixel database, and calculate the number of matching rectangles.

[0133] It should be noted that in step S31, the same method as constructing the bad pixel database is used to find all widths and gap ranges of the graphic through DRC rules, so as to split the graphic in the bad pixel database into several rectangles; in step S32, the width and gap relationship of the initial mask design pattern is obtained in the same way through the DRC rules in step S31, the mask design pattern is split into rectangles, and only rectangles with widths and gaps within the preset range are left.

[0134] For step S33, calculate the position information of the rectangles obtained by the two splitting rules within the graphic frame, and calculate the size information of these rectangles; specifically, calculate the position and size relationship of all rectangles on the graphic relative to the lower left corner of the graphic frame, using x as the horizontal coordinate of the rectangle center relative to the lower left corner of the graphic frame, y as the vertical coordinate of the rectangle center relative to the lower left corner of the graphic frame, dx as the width of the rectangle, and dy as the height of the rectangle.

[0135] Please see Figure 8 For step S34, it is necessary to find two types of rectangles with the same size within the graphic frame. Based on the two cases of the rectangle (same angle as in the graphic and rotated 90 degrees), four cases can be derived: no mirroring, mirroring relative to the X-axis, mirroring relative to the Y-axis, and mirroring simultaneously relative to both the X-axis and Y-axis. That is, for each state of the rectangle, four possible rectangles within the graphic frame can be obtained. After deriving four cases from each of the two cases, eight possible graphic frame cases can be obtained. In step S35, when matching the graphic position and size information, all rectangles within the four possible graphic frames are checked. If the number of rectangles exceeds the set threshold ratio, the match is considered successful.

[0136] Understandably, this invention introduces a fuzzy matching method, which enables the matching of mask design layouts with incomplete polygons and certain modifications when matching graphics, thereby increasing the probability of successful matching.

[0137] It should be further noted that when a large number of bad pixel detections are required, using fuzzy matching can reduce the matching time and thus achieve the best matching effect. At the same time, it can also reduce unnecessary calculations.

[0138] Furthermore, the first preset splitting rule and / or the second preset splitting rule are DRC rules.

[0139] The first embodiment of this invention uses DRC rules as an example, where both the first and second preset splitting rules are DRC rules. The specific splitting rules for the graphics in the bad pixel database and the initial mask design layout can be determined according to actual needs. This invention is merely an example of one implementation method; any modifications, equivalent substitutions, and improvements made within the principles of this invention should be included within the scope of protection of this invention.

[0140] It should be noted that by using DRC rules to perform fuzzy classification of the graphics in the bad pixel database and the graphics in the initial mask design layout, and determining the degree of matching by the matching rectangles, the number of bad pixels in the bad pixel database can be simplified by setting a threshold, or by allowing similar but different bad pixel graphics to be represented by the same graphic, thereby reducing the number of graphics to be matched for subsequent bad pixel graphics.

[0141] As another alternative implementation, please refer to Figure 9 The above method also includes the following steps after matching:

[0142] S4: Continue to match the graphic point information in the initial mask design layout with the bad point information in the preset bad point database. If the match is successful, then correct the initial mask design layout.

[0143] S5: When a match fails, output the corrected mask design layout.

[0144] Understandably, after completing the initial replacement and correction of defective pixels in the image, the corrected mask design layout can be output without performing simulation calculations on the initial mask design layout. The corrected mask design layout can be obtained directly and output. This design can save time in repairing the mask design layout, reduce resource usage, and avoid wasting resources.

[0145] For the overall design pattern, after simulating key areas and matching defective pixel patterns in the mask design layout with a defective pixel database, the correction points corresponding to the defective pixels can be used to replace and correct the pixels to be corrected in the mask design layout. This design can significantly save time, and only requires a small amount of computation to correct the mask design layout, greatly saving resources. In essence, directly replacing the parts of the initial mask design layout that successfully match defective pixels in the defective pixel database with their corresponding correction points reduces the time required to repair the mask design layout.

[0146] Please see Figure 10 The third embodiment of the present invention provides a computer-readable storage medium 1, on which computer program instructions 11 are stored. When the computer program instructions 11 are executed by a processor, they implement the above-described mask design layout blur detection method.

[0147] Understandably, the computer-readable storage medium 1 in the second embodiment of the present invention stores computer program instructions 11, which can be called by a processor to execute a mask design layout blur detection method described in the first embodiment above.

[0148] It should be noted that the computer-readable storage medium 1 has the same beneficial effects as the above-mentioned mask design layout blur detection method, which will not be elaborated here.

[0149] Specifically, the computer-readable storage medium 1 may be an electronic storage device such as flash memory, EEPROM (electrically erasable programmable read-only memory), EPROM, hard disk, or ROM; optionally, the computer-readable storage medium 1 may include a non-volatile computer-readable medium.

[0150] Specifically, the computer-readable storage medium 1 has storage space for computer program instructions 11 that perform any of the method steps described above, which can be read from or written into one or more computer program products; optionally, the computer program instructions 11 can be compressed in an appropriate form.

[0151] Please see Figure 11 The third embodiment of the present invention provides a graphic repair device 2, including a device running program 21 and a device body 22 that executes the device running program 21. When the device body 22 executes the device running program 21, it implements the above-mentioned mask design layout blur detection method.

[0152] Understandably, when the graphic repair device 2 in the second embodiment of the present invention is running, the device body 22 can implement the mask design layout blur detection method described in the first embodiment when executing the device running program 21.

[0153] It should be noted that the graphic restoration device 2 has the same beneficial effect as the above-mentioned mask design layout blur detection method, which will not be elaborated here.

[0154] Compared with existing technologies, the mask design layout blur detection method, storage medium, and graphic repair device provided by this invention have the following advantages:

[0155] 1. This invention provides a method for fuzzy detection of mask design layouts, comprising the following steps: providing an initial mask design layout and obtaining graphic point information of the initial mask design layout; providing a preset bad pixel database, and breaking the bad pixel graphics into several rectangles in the bad pixel database by design rules to perform fuzzy classification of the bad pixel information, thereby obtaining fuzzy classified bad pixel information; matching the graphic point information in the initial mask design layout with the bad pixel information in the preset bad pixel database, and if the match is successful, determining that there are bad pixels in the initial mask design layout.

[0156] It is understandable that the patterns in the mask often have missing parts or a lot of minor modifications, which greatly reduces the probability of matching the pattern using precise pattern matching methods.

[0157] Understandably, the defective pixel database of the present invention includes defective pixel information. By matching the defective pixels in the defective pixel database with the initial mask design layout, the initial mask design layout can be better detected by the defective pixels in the defective pixel database. This avoids the repeated use of OPC (Optical Proximity Correction) to check and repair defective pixels in the mask design layout, simplifies the defective pixel inspection process, and greatly improves the detection efficiency.

[0158] It should be noted that this invention also uses the DRC (Design Rule Check) method to perform fuzzy classification of bad pixel information in the bad pixel database, breaking the bad pixel image into a graphic composed of several rectangles, determining the degree of matching by matching rectangles, and then setting a threshold so that similar but different bad pixel images can be represented by a single graphic, simplifying the number of bad pixels in the database and reducing the number of graphics to be matched for subsequent bad pixel images.

[0159] 2. The mask design layout fuzzy detection method provided in this embodiment of the invention, which constructs a bad pixel database, specifically includes the following steps: performing photolithography rule checks on the graphic point information in the mask design layout to extract bad pixel information; performing fuzzy classification on the bad pixel information extracted by the photolithography rule checks; and storing the fuzzy classified bad pixel information into the bad pixel database.

[0160] Understandably, the present invention establishes a bad pixel database based on the required bad pixels or past experience. This bad pixel database mainly includes bad pixel information for the initial mask design layout. By using the required bad pixels, it can be ensured that no redundant matching is needed in the subsequent matching process. Subsequently, the bad pixels existing on the mask design layout can be directly replaced and corrected without the need for simulation correction again. This saves time and reduces the resources consumed by matching operations, and avoids the problem of correction errors.

[0161] 3. The mask design layout fuzzy detection method provided in this embodiment of the invention includes the following steps for fuzzy classification of bad pixel information: by using DRC rules, checking the parts of the bad pixel image that do not conform to the rules to obtain weak points in width and gap relationships, and breaking all polygons in the graphic frame into graphics composed of rectangles.

[0162] It should be noted that this invention introduces DRC (Design Rule Check) result information of the mask design layout during the pattern matching process, performs fuzzy classification of bad pixels in LRC (Lithography Rule Check), establishes a bad pixel database, and uses the bad pixels in the bad pixel database to perform fuzzy matching on the new mask design layout, thereby finding potential LRC bad pixels in the mask design layout.

[0163] Understandably, the present invention can select bad pixels from the bad pixel library based on the initial mask design layout or the type of bad pixels in the mask design layout to be repaired, thereby selecting the bad pixels in the bad pixel library to match with the mask design layout to be repaired. Through this design, unnecessary calculations can be reduced, saving time and resources.

[0164] 4. The mask design layout fuzzy detection method provided in this embodiment of the invention further includes the following steps after extracting bad pixel information and before performing fuzzy classification: expanding the bad pixel image in the bad pixel information outward to obtain the expanded bad pixel image; and saving the bad pixel images before and after expansion together into the bad pixel information.

[0165] Understandably, simulating chip mask design is complex and time-consuming, and modifying the mask is also a repetitive and tedious process. Since the pattern of mask design layout is usually repetitive, the locations where bad pixels will occur are also repetitive.

[0166] It should be noted that while the patterns in the mask design layout are repetitive, there are also a large number of minor differences or missing patterns, which leads to different hash check values ​​and makes it impossible to classify bad pixels and detect their locations. By expanding the bad pixel image in the bad pixel information outward, the probability of successful matching can be improved.

[0167] 5. The mask design layout fuzzy detection method provided in this embodiment of the invention stores the fuzzy classified bad pixel information into a bad pixel database, which requires the following steps: using the bounding box of a graphic in each bad pixel level to cut out the design layout of the bad pixel graphic, and storing the design layout in GDS file format.

[0168] Understandably, bad pixel information stored in the bad pixel database is pre-cut out using the bounding box of the graphics to facilitate the matching of bad pixel information in the initial mask design layout. It should be noted that the GDS file format is the most commonly used graphic data description language file format in integrated circuit board design.

[0169] 6. The mask design layout fuzzy detection method provided in this embodiment of the invention uses fuzzy matching rules with bad pixel information in a preset bad pixel database. Specifically, it includes the following steps: dividing the graphic in the bad pixel database into several rectangles using a first preset splitting rule; dividing the initial mask design layout into several rectangles using a second preset splitting rule, and selecting rectangles whose width and gaps are within a preset range; calculating the position information of the rectangles obtained by the two splitting rules within the graphic frame, and calculating the size information of these rectangles; inferring possible graphic positions based on the position information of the rectangles obtained by splitting the initial mask design layout within the graphic frame; matching the position and size information of the rectangles obtained by splitting the initial mask design layout with the rectangles obtained by splitting the graphic in the bad pixel database, and calculating the number of matching rectangles.

[0170] Understandably, this invention introduces a fuzzy matching method, which enables the matching of mask design layouts with incomplete polygons and certain modifications when matching graphics, thereby increasing the probability of successful matching.

[0171] It should be noted that when a large number of bad pixels need to be detected, fuzzy matching can be used to reduce the matching time and thus achieve the best matching effect. At the same time, it can also reduce unnecessary calculations.

[0172] 7. In the mask design layout blur detection method provided in the embodiments of the present invention, the first preset splitting rule and / or the second preset splitting rule are DRC rules.

[0173] Understandably, the first preset splitting rule is used to split the graphics in the bad pixel database into several rectangles, and the second preset splitting rule is used to split the initial mask design layout into several rectangles, selecting rectangles whose width and gaps are within the preset range.

[0174] It should be noted that by using DRC rules to perform fuzzy classification of the graphics in the bad pixel database and the graphics in the initial mask design layout, and determining the degree of matching by the matching rectangles, the number of bad pixels in the bad pixel database can be simplified by setting a threshold, or by allowing similar but different bad pixel graphics to be represented by the same graphic, thereby reducing the number of graphics to be matched for subsequent bad pixel graphics.

[0175] 8. The mask design layout blur detection method provided in the embodiments of the present invention further includes the following steps after matching: continuing to match the graphic point information in the initial mask design layout with the bad point information in the preset bad point database; if the matching is successful, the initial mask design layout is corrected; if the matching fails, the corrected mask design layout is output.

[0176] Understandably, after completing the initial replacement and correction of defective pixels in the image, the corrected mask design layout can be output without performing simulation calculations on the initial mask design layout. This design saves time in repairing the mask design layout, reduces resource usage, and avoids wasting resources.

[0177] For the overall design pattern, after simulating key areas and matching defective pixel patterns in the mask design layout with a defective pixel database, the correction points corresponding to the defective pixels can be used to replace and correct the pixels to be corrected in the mask design layout. This design can significantly save time, and only requires a small amount of computation to correct the mask design layout, greatly saving resources. In essence, directly replacing the parts of the initial mask design layout that successfully match defective pixels in the matching library with the corresponding correction points reduces the time required to repair the mask design layout.

[0178] 9. The present invention also provides a computer-readable storage medium storing computer program instructions thereon, which, when executed by a processor, implement the above-described mask design layout blur detection method.

[0179] This computer-readable storage medium has the same beneficial effects as the mask design layout blur detection method described above, and will not be elaborated here.

[0180] 10. This embodiment of the invention also provides a graphic repair device, including a device running program and a device body that executes the device running program. When the device body executes the device running program, it implements the above-mentioned mask design layout blur detection method.

[0181] This graphic restoration device has the same beneficial effects as the mask design layout blur detection method described above, and will not be elaborated here.

[0182] The foregoing has provided a detailed description of a mask design layout blur detection method, storage medium, and graphic repair device disclosed in the embodiments of the present invention. Specific examples have been used to illustrate the principles and implementation methods of the present invention. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of the present invention. At the same time, for those skilled in the art, there will be changes in specific implementation methods and application scope based on the ideas of the present invention. Therefore, the content of this specification should not be construed as a limitation of the present invention. Any modifications, equivalent substitutions, and improvements made within the principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for detecting fuzziness in a mask design layout, characterized in that, Includes the following steps: Provide an initial mask design layout and obtain the graphic point information of the initial mask design layout; It provides a preset bad pixel database. In the bad pixel database, the bad pixel image is broken into several rectangles by design rules to perform fuzzy classification of bad pixel information and obtain the fuzzy classified bad pixel information. The graphic point information in the initial mask design layout is matched with the bad pixel information in a preset bad pixel database. If a match is successful, it is determined that there are bad pixels in the initial mask design layout. The matching rule with the bad pixel information in the preset bad pixel database is fuzzy matching, which specifically includes the following steps: The images in the bad pixel database are divided into several rectangles according to the first preset splitting rule; The initial mask design layout is divided into several rectangles according to the second preset splitting rule, and rectangles with width and gap within the preset range are selected; Calculate the position information of the rectangles obtained by the two splitting rules within the graphics frame, and calculate the size information of these rectangles; Based on the position information of the rectangles obtained from the initial mask design layout splitting within the graphic frame, the possible graphic positions are deduced. The rectangles obtained by splitting the initial mask design layout are matched with the rectangles obtained by splitting the graphics in the bad pixel database in terms of position and size information, and the number of matching rectangles is calculated.

2. The mask design layout blur detection method as described in claim 1, characterized in that, Building a bad pixel database specifically includes the following steps: Perform photolithography rule checks on the graphic point information in the mask design layout to extract bad point information; Fuzzy classification is performed on the defect information extracted from photolithography rule inspection; The bad pixel information after fuzzy classification is stored in the bad pixel database.

3. The mask design layout blur detection method as described in claim 2, characterized in that, The fuzzy classification of bad pixel information includes the following steps: By using DRC rules, we can check for non-compliant parts in bad pixel images to identify weak points in width and gap relationships, and break all polygons in the graphic frame into shapes composed of rectangles.

4. The mask design layout fuzzy detection method as described in claim 3, after extracting the bad pixel information and before performing fuzzy classification, further includes the following steps: The bad pixel image in the bad pixel information is expanded outward to obtain the expanded bad pixel image; Both the images of the bad pixels before and after the expansion are saved together in the bad pixel information.

5. The mask design layout blur detection method as described in claim 4, characterized in that, Storing the fuzzy-classified bad pixel information into the bad pixel database requires the following steps: The design layout of the bad pixel graphic is cut out using the bounding box of a graphic in each bad pixel level, and the design layout is stored in GDS file format.

6. The mask design layout blur detection method as described in claim 1, characterized in that: The first preset splitting rule and / or the second preset splitting rule are DRC rules.

7. The mask design layout blur detection method as described in claim 1, characterized in that, The above method also includes the following steps after matching: Continue to match the graphic point information in the initial mask design layout with the bad point information in the preset bad point database. If the match is successful, then correct the initial mask design layout. When a match fails, output the corrected mask design layout.

8. A computer-readable storage medium storing computer program instructions thereon, characterized in that: When the computer program instructions are executed by the processor, they implement the mask design layout blur detection method according to any one of claims 1-7.

9. A graphic restoration device, characterized in that: The device includes a device operating program and a device body that executes the device operating program. When the device body executes the device operating program, it implements the mask design layout blur detection method according to any one of claims 1-7.

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