Optimization Method, System and Terminal for Drawing Test Graphics Based on Clustering

Abstract

The present invention provides an optimization method, system and terminal for drawing test patterns based on clustering. First, according to the preset layout area and the area of a single test pattern, the number of test patterns that can be placed is calculated. Then, all possible combinations of design parameters are enumerated and the combinations that violate the design rules are filtered. Next, the target design parameter combination is determined from the remaining valid combinations. Further, according to the types of test patterns, cluster analysis is performed on these target design parameter combinations and arranged in category order to generate the entire test pattern layout. The present invention not only reduces the waste of layout area by filtering out invalid combinations of design parameters, but also simplifies the inspection and browsing process of test patterns through cluster analysis. The generated test pattern layout excludes invalid patterns during creation, eliminating subsequent processing steps and improving efficiency. In addition, the generated script retains a framework with high layout utilization rate, which can quickly replace specific test patterns to achieve rapid iteration of the layout.

Description

Technical Field

[0001] The present invention relates to the field of semiconductor technology, and in particular to a clustering-based test pattern drawing optimization method, system and terminal. Background Art

[0002] In the field of semiconductor manufacturing, the model building of Optical Proximity Correction (OPC) relies on drawing test patterns and collecting corresponding measurement data. Test patterns are designed to verify and calibrate lithography models. By drawing these patterns and collecting measurement data, lithography models can be established and optimized. However, the current test pattern drawing methods often only focus on the critical dimension (CD) and the minimum pitch achievable by the lithography process, which may result in patterns that violate design specifications. Although some existing tools can avoid drawing illegal test patterns, they still retain the drawing space and labels of these patterns, fail to effectively utilize the layout area, and thus cause unnecessary waste of layout area. Summary of the invention

[0003] In view of the above-mentioned shortcomings of the prior art, the purpose of the present invention is to provide a clustering-based test pattern drawing optimization method, system and terminal, which are used to solve technical problems such as waste of layout area caused by the existing test pattern drawing method.

[0004] To achieve the above-mentioned purpose and other related purposes, the present invention provides a clustering-based test pattern drawing optimization method, the method comprising: determining the number of test patterns that can be placed according to the planned test pattern drawing area and the area of ​​a single test pattern; enumerating all combinations of test pattern design parameters, and filtering combinations that violate design rules; obtaining target design parameter combinations for the number of test patterns based on the remaining combinations after filtering; performing cluster analysis on each target design parameter combination based on the type of test pattern, and arranging each target design parameter combination in order by category according to the clustering results to generate the entire test pattern layout.

[0005] In one embodiment of the present invention, enumerating all combinations of test graphic design parameters and filtering combinations that violate design rules includes: setting the range and step size of the test graphic design parameters for each test graphic type, enumerating all combinations of test graphic design parameters for each test graphic type; checking whether each combination violates the design rules, and filtering out the combinations that violate the design rules.

[0006] In one embodiment of the present invention, the setting of the range and step size of the test graphic design parameters for each test graphic type and enumerating the combinations of all test graphic design parameters for each test graphic type include: defining each test graphic design parameter; setting the range and step size of the test graphic design parameters for each test graphic type; generating a design parameter vector for each test graphic type based on the set range and step size; wherein the design parameter vector includes: a combination of all test graphic design parameters for the corresponding test graphic type.

[0007] In one embodiment of the present invention, obtaining the target design parameter combination for the number of test graphics based on the remaining combinations after filtering includes: comparing the number of combinations remaining after filtering and the determined number of test graphics that can be placed; if the remaining number of combinations is greater than the number of test graphics, reducing the number of enumerated combinations of test graphic design parameters by one or more of reducing the types of test graphics, narrowing the range of test graphic design parameters, and increasing the step size of test graphic design parameters until the target design parameter combination for the number of test graphics is obtained; if the remaining number of combinations is less than the number of test graphics, increasing the number of enumerated combinations of test graphic design parameters by one or more of increasing the types of test graphics, expanding the range of test graphic design parameters, and reducing the step size of test graphic design parameters until the target design parameter combination for the number of test graphics is obtained; if the remaining number of combinations is equal to the number of test graphics, taking the remaining combinations as the target design parameter combinations.

[0008] In one embodiment of the present invention, based on the test pattern type, each target design parameter combination is clustered and analyzed, and each target design parameter combination is arranged in order according to the category according to the clustering result, to generate the entire test pattern layout, including: determining the number of cluster categories and preliminary cluster centers based on the test pattern type, and classifying the design parameter combinations to determine the number of cluster categories through iterative clustering and cluster center adjustment; arranging the target design parameter combinations of each design parameter combination classification in order according to the category, and placing them in the test pattern layout in the order of arrangement to obtain the entire test pattern layout.

[0009] In one embodiment of the present invention, the method of determining the number of cluster categories and preliminary cluster centers based on the test pattern types includes: determining the number of cluster categories based on the existing test pattern types; and randomly selecting a target design parameter combination from each target design parameter combination of each test pattern type as the preliminary cluster center for the corresponding classification.

[0010] In one embodiment of the present invention, the design parameter combination classification for determining the number of cluster categories by iterative clustering and cluster center adjustment includes: based on the preliminary cluster center, clustering and operating the cluster center adjustment operation on each target design parameter combination of the non-cluster center to obtain each design parameter combination classification and a new cluster center; detecting whether the cluster center is stable; if stable, taking the obtained each design parameter combination classification as the final design parameter combination classification; if unstable, clustering and operating the cluster center adjustment operation on each target design parameter combination of the non-cluster center based on the new cluster center until a stable cluster center is detected to obtain the final design parameter combination classification.

[0011] In one embodiment of the present invention, the clustering and cluster center adjustment operation includes: calculating the distance from each target design parameter combination of the non-cluster center to each cluster center, and classifying the target design parameter combination and the cluster center with the smallest distance to it into the same category to obtain each design parameter combination classification; calculating the average value of each test graphic design parameter in each design parameter combination classification, and combining to obtain the design parameter average value combination of the corresponding design parameter combination classification; calculating the distance from each target design parameter combination in each design parameter combination classification to its corresponding design parameter average value combination, and taking the target design parameter combination with the smallest distance as the new cluster center of the design parameter combination classification.

[0012] To achieve the above-mentioned purpose and other related purposes, the present invention provides a clustering-based test pattern drawing optimization system, the system comprising: a test pattern number determination module configured, used to determine the number of test patterns that can be placed according to the planned test pattern drawing area and the area of ​​a single test pattern; a combination enumeration and filtering module connected to the test pattern number determination module configured, used to enumerate all combinations of test pattern design parameters and filter combinations that violate design rules; a combination determination module connected to the combination enumeration and filtering module, used to obtain the target design parameter combination of the number of test patterns based on the remaining combinations after filtering; a test pattern generation module connected to the combination enumeration and filtering module and the combination determination module, used to perform cluster analysis on each target design parameter combination based on the test pattern type, and arrange each target design parameter combination in order by category according to the clustering results to generate the entire test pattern layout.

[0013] To achieve the above-mentioned objectives and other related objectives, the present invention provides an electronic terminal, comprising: one or more memories and one or more processors; the one or more memories are used to store computer programs; the one or more processors are connected to the memories and are used to run the computer programs to execute the clustering-based test graphics drawing optimization method.

[0014] As described above, the present invention is a clustering-based test pattern drawing optimization method, system and terminal, which has the following beneficial effects: first, according to the preset layout area and the area of ​​a single test pattern, the number of test patterns that can be placed is calculated. Then, all possible design parameter combinations are enumerated, and those combinations that violate the design rules are excluded by screening. Then, the target design parameter combination is determined from the remaining valid combinations. Further, according to the type of test pattern, these target design parameter combinations are clustered and arranged in order of category to generate the entire test pattern layout. The present invention not only reduces the waste of layout area by filtering invalid design parameter combinations, but also simplifies the inspection and browsing process of test patterns through cluster analysis. The generated test pattern layout excludes invalid patterns when it is created, eliminating subsequent processing steps and improving efficiency. In addition, the generated script retains a framework with high layout utilization, so that specific test patterns can be quickly replaced when needed, and rapid iteration of the layout is achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 Shown is a schematic flow chart of a clustering-based test pattern drawing optimization method in one embodiment of the present invention.

[0016] Figure 2 Shown is a schematic flow chart of a clustering-based test pattern drawing optimization method in one embodiment of the present invention.

[0017] Figure 3 Shown is a schematic diagram of the entire test pattern layout in one embodiment of the present invention.

[0018] Figure 4 Shown is a structural schematic diagram of a clustering-based test pattern drawing optimization system in one embodiment of the present invention.

[0019] Figure 5 Shown is a schematic structural diagram of an electronic terminal in an embodiment of the present invention. DETAILED DESCRIPTION

[0020] The following describes the embodiments of the present invention by specific examples, and those skilled in the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the following embodiments and features in the embodiments can be combined with each other without conflict.

[0021] It should be noted that in the following description, reference is made to the accompanying drawings, which describe several embodiments of the present invention. It should be understood that other embodiments may also be used, and that mechanical composition, structure, electrical and operational changes may be made without departing from the spirit and scope of the present invention. The following detailed description should not be considered restrictive, and the scope of the embodiments of the present invention is limited only by the claims of the published patents. The terms used herein are only for describing specific embodiments and are not intended to limit the present invention. Spatially related terms, such as "upper", "lower", "left", "right", "below", "below", "lower", "above", "upper", etc., may be used in the text to facilitate the description of the relationship between an element or feature shown in the figure and another element or feature.

[0022] Throughout the specification, when a part is said to be "connected" to another part, this includes not only the case of "direct connection" but also the case of "indirect connection" by placing other elements therebetween. In addition, when a part is said to "include" a certain constituent element, unless otherwise stated, it does not exclude other constituent elements, but means that other constituent elements may be included.

[0023] The terms first, second and third mentioned herein are used to describe various parts, components, regions, layers and / or segments, but are not limited thereto. These terms are only used to distinguish a certain part, component, region, layer or segment from other parts, components, regions, layers or segments. Therefore, the first part, component, region, layer or segment described below may refer to the second part, component, region, layer or segment within the scope of the present invention.

[0024] Furthermore, as used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, unless there is an indication to the contrary in the context. It should be further understood that the terms "comprise", "include" indicate the presence of the described features, operations, elements, components, items, kinds, and / or groups, but do not exclude the presence, occurrence or addition of one or more other features, operations, elements, components, items, kinds, and / or groups. The terms "or" and "and / or" used herein are interpreted as inclusive, or mean any one or any combination. Therefore, "A, B or C" or "A, B and / or C" means "any of the following: A; B; C; A and B; A and C; B and C; A, B and C". Exceptions to this definition will only occur when the combination of elements, functions or operations is inherently mutually exclusive in some way.

[0025] The present invention provides a clustering-based test pattern drawing optimization method. First, according to a preset layout area and the area of ​​a single test pattern, the number of test patterns that can be placed is calculated. Then, all possible design parameter combinations are enumerated, and those combinations that violate the design rules are excluded by screening. Then, a target design parameter combination is determined from the remaining valid combinations. Further, according to the types of test patterns, these target design parameter combinations are clustered and analyzed, and arranged in order of categories to generate the entire test pattern layout. The present invention not only reduces the waste of layout area by filtering invalid design parameter combinations, but also simplifies the inspection and browsing process of test patterns through cluster analysis. The generated test pattern layout excludes invalid patterns when it is created, omitting subsequent processing steps and improving efficiency. In addition, the generated script retains a framework with high layout utilization, so that specific test patterns can be quickly replaced when needed, and rapid iteration of the layout is achieved.

[0026] The following is a detailed description of the embodiments of the present invention with reference to the accompanying drawings so that those skilled in the art can easily implement the present invention. The present invention can be embodied in many different forms and is not limited to the embodiments described herein.

[0027] like Figure 1 A schematic flow chart showing a clustering-based test graphic drawing optimization method in an embodiment of the present invention.

[0028] The method comprises:

[0029] Step S1: Determine the number of test patterns that can be placed according to the planned test pattern drawing area and the area of ​​a single test pattern.

[0030] Specifically, first we need to determine the total area where the test patterns will be drawn. Next, we need to set the area of ​​a single test pattern, which usually depends on the shape and size of the test pattern required. After determining the total test pattern drawing area and the area of ​​a single test pattern, we can calculate the number of test patterns that can be placed by dividing the total area by the area of ​​a single pattern.

[0031] Step S2: Enumerate all combinations of test pattern design parameters and filter out combinations that violate design rules.

[0032] In one embodiment, step S2 includes:

[0033] Set the range and step size of the test pattern design parameters of each test pattern type, and enumerate the combinations of all test pattern design parameters of each test pattern type; specifically, determine each test pattern type and its design parameters, set a range and step size for each design parameter of each test pattern type, obtain all possible values ​​of each design parameter of each test pattern type, and then enumerate the combinations of all design parameters of each test pattern type.

[0034] Check whether each combination violates the design rules, and filter out the combinations that violate the design rules. Specifically, the design rules are set according to the requirements. When the combination of design parameters does not meet the design rule restrictions, the combination is removed.

[0035] In a specific embodiment, the setting of the range and step size of the test pattern design parameters for each test pattern type and enumerating the combinations of all test pattern design parameters for each test pattern type include:

[0036] First, we need to define the design parameters of each test pattern. Preferably, these parameters usually include but are not limited to: Test Pattern Type, which refers to the specific type of test pattern, such as lines, polygons, circles, etc. Different types of patterns may have different effects on the lithography process; Critical Dimensions (CD), which is the most important physical feature in the test pattern, usually refers to the width of the line, the side length of the polygon, or the diameter of the circle. This parameter is crucial for evaluating the resolution and accuracy of the lithography process. Pitch, which is the repetition interval of the test pattern in a specific direction. This parameter is important for understanding periodic effects (such as diffraction and interference) in the lithography process.

[0037] After defining the design parameters, we need to set a reasonable value range and step size for each test pattern design parameter of each test pattern type. The range setting can be based on the specific requirements of the lithography process and the resolution limit of the equipment, and a reasonable minimum and maximum value can be set for each design parameter. The step size setting is the interval between parameter values.

[0038] Based on the set range and step size, a design parameter vector for each test pattern type is generated; specifically, for each design parameter, a value is taken within the set range and with the set step size, and combined with all possible values ​​of other design parameters. A vector containing all possible combinations of design parameters is obtained. This vector is a one-dimensional array X = [x1, x2, x3,…], which contains all combinations of design parameters x.

[0039] In one embodiment, it is checked whether each enumerated combination violates the design rules, and the combinations that violate the design rules are filtered out. The design rules can be set for each test pattern type, or a general design rule can be set. The design rules can be expressed by design parameters, for example, the combination of test pattern design parameters is represented by X=(A, B, C), the design rule is A≤0.5×B, and the combinations that do not meet the design rules are filtered out.

[0040] Step S3: Obtain target design parameter combinations for the number of test patterns based on the combinations remaining after filtering.

[0041] In one embodiment, step S3 includes:

[0042] Compare the number of combinations remaining after filtering and the number of test patterns that can be placed;

[0043] If the number of remaining combinations is greater than the number of test patterns, we can adopt one or more of the following methods to reduce the number of combinations of enumerated test pattern design parameters until the target design parameter combination of the number of test patterns is obtained: the specific methods include:

[0044] Reduce the total number of combinations by eliminating some less important or highly repetitive test patterns.

[0045] By narrowing the range of design parameters, the design is made more compact, thus reducing the number of combinations.

[0046] By increasing the step size, the number of combinations can be reduced.

[0047] If the number of remaining combinations is less than the number of test patterns, we can adopt one or more of the following methods to increase the number of combinations of enumerated test pattern design parameters until the target design parameter combination of the number of test patterns is obtained: the specific methods include:

[0048] By introducing new types of test patterns, more lithography process characteristics and parameter combinations can be covered.

[0049] By expanding the range of test pattern design parameters to include more potential combinations;

[0050] By reducing the step size, more combinations can be generated within the current design parameter range.

[0051] If the number of remaining combinations is equal to the number of test patterns, the remaining combinations are used as target design parameter combinations.

[0052] Step S4: Based on the test pattern type, cluster analysis is performed on each target design parameter combination, and each target design parameter combination is arranged in order according to the category according to the clustering result to generate the entire test pattern layout.

[0053] In one embodiment, step S4 includes:

[0054] Determine the number of cluster categories and preliminary cluster centers based on the test pattern types, and determine the design parameter combination classification of the number of cluster categories by iterative clustering and cluster center adjustment;

[0055] The target design parameter combinations classified by each design parameter combination are arranged in order according to the categories, and are sequentially placed in the test pattern layout in the arrangement order to obtain the entire test pattern layout.

[0056] In one embodiment, determining the number of cluster categories and preliminary cluster centers based on the test pattern type includes:

[0057] First, the number of cluster categories is determined based on the existing test pattern types, and each test pattern type will correspond to a cluster category. If each target design parameter combination involves N test pattern types, then the number of cluster categories is N.

[0058] After determining the cluster categories, we need to select a preliminary cluster center for each category. A cluster center is one or more points or combinations that can represent the characteristics of the category. From each target design parameter combination of each test pattern type, a target design parameter combination is randomly selected as the preliminary cluster center of the corresponding category.

[0059] In one embodiment, the design parameter combination classification for determining the number of cluster categories by iterative clustering and cluster center adjustment includes:

[0060] Based on the preliminary cluster centers, clustering and cluster center adjustment operations are performed on each target design parameter combination that is not a cluster center, and classification of each design parameter combination and new cluster centers are obtained; specifically, at the beginning of the clustering process, each test pattern type has a preliminary cluster center, which is randomly selected from the target design parameter combination of each type. Cluster center adjustment operations are performed on each target design parameter combination that is not a cluster center, and classification of each design parameter combination and new cluster centers are obtained.

[0061] Detect whether the cluster center is stable, that is, detect whether the cluster center changes;

[0062] If it is stable, then it can be considered that the clustering process has converged, and the design parameter combination classifications obtained at this time can be used as the final design parameter combination classifications.

[0063] If it is unstable, based on the new cluster center, cluster the target design parameter combinations of non-cluster centers and perform cluster center adjustment operations until a stable cluster center is detected to obtain the final design parameter combination classification.

[0064] In a specific embodiment, the clustering and cluster center adjustment operation includes:

[0065] For each target design parameter combination of non-cluster center, the distance to each cluster center is calculated respectively. The distance calculation expression is as follows:

[0066] Dis(X 1 , X 2 ) = ∑W i |X 1i -X 2i |;(1)

[0067] Where Wi is the weight of the i-th design parameter (if there is no weight, it can be set to 1), X 1 is the first target parameter combination, X 2 is a cluster center, X 1i is the i-th design parameter in the first target parameter combination, X 2i is the i-th design parameter of the cluster center.

[0068] For each target design parameter combination of non-cluster centers, find the cluster center with the smallest distance to it. And classify the target design parameter combination and the cluster center with the smallest distance to it into the same category; until all target design parameter combinations of non-cluster centers are assigned to the corresponding cluster categories, the classification of each design parameter combination is obtained; it should be noted that for category or string type design parameters, they can be mapped to integers for clustering. After clustering is completed, these integers are mapped back to the original string type.

[0069] Calculate the average value of each test graphic design parameter in each design parameter combination classification, and combine to obtain the design parameter average value combination of the corresponding design parameter combination classification; specifically, for each test graphic design parameter in the classification, calculate the average value of each test graphic design parameter in each design parameter combination classification. The specific steps are: for each test graphic design parameter in the cluster, add the value of the parameter in all target design parameter combinations. Divide the obtained sum by the number of target design parameter combinations in the cluster to obtain the average value. For each test graphic design parameter in the cluster, an average value is obtained. These average values ​​are combined to form a design parameter average value combination, which represents the center position of the classification.

[0070] Calculate the distance between each target design parameter combination in each design parameter combination classification and its corresponding design parameter average value combination, and use the target design parameter combination with the smallest distance as the new cluster center of the design parameter combination classification; specifically, for each target design parameter combination in each design parameter combination classification, calculate its distance to the corresponding design parameter average value combination. In each classification, find the target design parameter combination closest to the design parameter average value combination. Use this closest combination as the new cluster center of the design parameter combination classification. After each new cluster center is obtained, check whether the cluster center is stable. When the cluster center is stable, the clustering process ends. If the cluster center is unstable, iteratively perform the clustering and cluster center adjustment operations until the cluster center is stable.

[0071] In one embodiment, the target design parameter combinations of the design parameter combinations are arranged in order according to the categories, and are placed in the test pattern layout in sequence according to the arrangement order, and obtaining the entire test pattern layout includes:

[0072] Arrange the target design parameter combinations of each design parameter combination classification in order according to the category, and put the target design parameter combinations belonging to the same classification together. Extract the first Group (i.e., a design parameter combination classification) from the clustering results in order, extract all the target design parameter combinations in the Group in order, and place these target design parameter combinations in order on the test pattern layout to obtain the entire test pattern layout. The placement method can be determined according to specific needs, such as grid layout, ring layout, etc.

[0073] In order to better describe the clustering-based test graph drawing optimization method, it is now described in conjunction with the following specific embodiments.

[0074] Embodiment: A test graph drawing optimization method based on clustering. Figure 2 The figure is a flow chart of the clustering-based test pattern drawing optimization method in this embodiment.

[0075] The method comprises:

[0076] Step 1: Based on the total area of ​​the planned test pattern layout and the area of ​​a single test pattern, we can calculate the maximum number of test patterns that can be placed on a given layout.

[0077] Step 2: Define the key design parameters such as the test pattern type, critical dimension (CD), and pitch. Set the range and step size of the design parameters for each test pattern, and enumerate all possible design parameter combinations x = (A, B, C) by traversing these ranges and step sizes. Represent the design parameters as a one-dimensional vector X = [x1, x2, x3, ...], where xi represents a specific design parameter.

[0078] Step 3: Set the design rule: A≤0.5*B, and remove the design parameter combinations that do not meet the design rule.

[0079] Step 4: Compare the number of remaining design parameter combinations with the maximum number of test patterns that can be drawn and make corresponding adjustments.

[0080] If the number of remaining combinations is greater than the maximum number, return to step 3 to reduce the number of combinations by narrowing the range of design parameters, increasing the step size, or reducing the types of test patterns.

[0081] If the number of remaining combinations is exactly equal to the maximum number, the next step of cluster analysis is entered.

[0082] If the number of remaining combinations is less than the maximum number, return to step 2 to supplement by expanding the range of design parameters, reducing the step size, or adding new test pattern types.

[0083] Step 5: Determine the number of clustering categories based on the type of test pattern, and randomly select a combination from each test pattern as the initial cluster center.

[0084] Step 6: Traverse the remaining design parameter combinations and calculate their distances to each cluster center. Assign each combination to the category to which the nearest cluster center belongs. For each category, calculate the average design parameter value of all combinations within it and generate a new cluster center.

[0085] Step 7: Check whether the cluster center is stable;

[0086] If it is stable, the obtained design parameter combination classification is taken as the final design parameter combination classification, and step 8 is executed;

[0087] If it is unstable, return to step 6 based on the new cluster center.

[0088] Step 8: According to the clustering results, arrange all categories in order (Group1, Group2, ..., GroupN). Each category contains a certain number of test pattern combinations arranged in order (tp1, tp2, ..., tpn). Each time a Group is extracted in order, all tp in the Group are placed in order in the blank test pattern layout until all test patterns are placed to generate the entire test pattern layout, such as Figure 3 .

[0089] Similar to the principle of the above embodiment, the present invention provides a test pattern drawing optimization system based on clustering.

[0090] The following provides specific embodiments in conjunction with the accompanying drawings:

[0091] like Figure 4 A structural schematic diagram of a clustering-based test graphic drawing optimization system in an embodiment of the present invention is shown.

[0092] The system comprises:

[0093] A test pattern quantity determination module 1 is configured to determine the number of test patterns that can be placed according to the planned test pattern drawing area and the area of ​​a single test pattern;

[0094] A combination enumeration and filtering module 2, connected to the configuration test pattern quantity determination module 1, is used to enumerate all combinations of test pattern design parameters and filter combinations that violate design rules;

[0095] A combination determination module 3, connected to the combination enumeration and filtering module 2, for obtaining a target design parameter combination of the number of test patterns based on the combinations remaining after filtering;

[0096] The test pattern generation module 4 is connected to the combination enumeration and filtering module 2 and the combination determination module 3, and is used to perform cluster analysis on each target design parameter combination based on the test pattern type, and arrange each target design parameter combination in order according to the category according to the clustering result to generate the entire test pattern layout.

[0097] Since the implementation principle of the clustering-based test pattern drawing optimization system has been described in the aforementioned embodiment, it will not be repeated here.

[0098] The clustering-based test pattern drawing optimization method provided in the embodiment of the present invention can be implemented on the terminal side or the server side. As for the hardware structure of the electronic terminal, please refer to Figure 5, is an optional hardware structure diagram of an electronic terminal 1000 provided in an embodiment of the present invention. The terminal 1000 may be a mobile phone, a computer device, a tablet device, a personal digital processing device, a factory background processing device, etc. The terminal 1000 includes: at least one processor 1001, a memory 1002, at least one network interface 10010 and a user interface 1009. The various components in the device are coupled together through a bus system 1005. It can be understood that the bus system 1005 is used to realize the connection and communication between these components. In addition to the data bus, the bus system 1005 also includes a power bus, a control bus and a status signal bus. However, for the sake of clarity, in Figure 5 In the specification, various buses are labeled as bus systems.

[0099] The user interface 1009 may include a display, a keyboard, a mouse, a trackball, a click gun, keys, buttons, a touch pad or a touch screen.

[0100] It is understood that the memory 1002 can be a volatile memory or a non-volatile memory, and can also include both volatile and non-volatile memories. Among them, the non-volatile memory can be a read-only memory (ROM), a programmable read-only memory (PROM), which is used as an external cache. By way of exemplary but not limiting explanation, many forms of RAM are available, such as static random access memory (SRAM), synchronous static random access memory (SSRAM). The memory described in the embodiments of the present invention is intended to include but is not limited to these and any other suitable categories of memory.

[0101] The memory 1002 in the embodiment of the present invention is used to store various categories of data to support the operation of the terminal 1000. Examples of these data include: any executable program for operating on the terminal 1000, such as an operating system 10021 and an application 10022; the operating system 10021 includes various system programs, such as a framework layer, a core library layer, a driver layer, etc., for implementing various basic services and processing hardware-based tasks. The application 10022 may include various applications, such as a media player (MediaPlayer), a browser (Browser), etc., for implementing various application services. The clustering-based test graphics drawing optimization method provided in the embodiment of the present invention may be included in the application 10022.

[0102] The method disclosed in the above embodiment of the present invention can be applied to the processor 1001, or implemented by the processor 1001. The processor 1001 may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by the hardware integrated logic circuit in the processor 1001 or the instruction in the form of software. The above processor 1001 may be a general processor, a digital signal processor (DSP, Digital Signal Processor), or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. The processor 1001 can implement or execute the methods, steps and logic block diagrams disclosed in the embodiment of the present invention. The processor 1001 may be a microprocessor or any conventional processor, etc. In combination with the steps of the accessory optimization method provided in the embodiment of the present invention, it can be directly embodied as a hardware decoding processor to execute, or it can be executed by a combination of hardware and software modules in the decoding processor. The software module can be located in a storage medium, which is located in a memory, and the processor reads the information in the memory and completes the steps of the above method in combination with its hardware.

[0103] In an exemplary embodiment, the terminal 1000 may be implemented by one or more application specific integrated circuits (ASIC), DSP, programmable logic device (PLD), complex programmable logic device (CPLD) to execute the aforementioned method.

[0104] Those skilled in the art can understand that all or part of the steps of implementing the above-mentioned method embodiments can be completed by hardware related to the computer program. The aforementioned computer program can be stored in a computer-readable storage medium. When the program is executed, the steps of the above-mentioned method embodiments are executed; and the aforementioned storage medium includes: ROM, RAM, magnetic disk or optical disk and other media that can store program codes.

[0105] In the embodiments provided in the present application, the computer readable and writable storage medium may include a read-only memory, a random access memory, an EEPROM, a CD-ROM or other optical disk storage device, a disk storage device or other magnetic storage device, a flash memory, a USB flash drive, a mobile hard disk, or any other medium that can be used to store the desired program code in the form of an instruction or data structure and can be accessed by a computer. In addition, any connection can be appropriately referred to as a computer-readable medium. For example, if the instruction is sent from a website, a server or other remote source using a coaxial cable, an optical fiber cable, a twisted pair, a digital subscriber line (DSL), or wireless technologies such as infrared, radio, and microwaves, the coaxial cable, optical fiber cable, twisted pair, DSL, or wireless technologies such as infrared, radio, and microwaves are included in the definition of the medium. However, it should be understood that computer readable and writable storage media and data storage media do not include connections, carriers, signals, or other temporary media, but are intended to be non-temporary, tangible storage media. Disk and disc, as used in this application, includes compact disc (CD), laser disc, optical disc, digital versatile disc (DVD), floppy disk and Blu-ray disc where disks usually reproduce data magnetically, while discs reproduce data optically with lasers.

[0106] In summary, the clustering-based test pattern drawing optimization method, system and terminal of the present invention first calculate the number of test patterns that can be placed according to the preset layout area and the area of ​​a single test pattern. Then, all possible design parameter combinations are enumerated, and those combinations that violate the design rules are excluded by screening. Then the target design parameter combination is determined from the remaining valid combinations. Further, according to the type of test pattern, these target design parameter combinations are clustered and analyzed, and arranged in order of category to generate the entire test pattern layout. The present invention not only reduces the waste of layout area by filtering invalid design parameter combinations, but also simplifies the inspection and browsing process of test patterns through cluster analysis. The generated test pattern layout excludes invalid patterns when it is created, eliminating subsequent processing steps and improving efficiency. In addition, the generated script retains a framework with high layout utilization, so that specific test patterns can be quickly replaced when needed, and rapid iteration of the layout is achieved. Therefore, the present invention effectively overcomes the various shortcomings in the prior art and has a high industrial utilization value.

[0107] The above embodiments are merely illustrative of the principles and effects of the present invention, and are not intended to limit the present invention. Anyone familiar with the technology may modify or change the above embodiments without violating the spirit and scope of the present invention. Therefore, all equivalent modifications or changes made by a person of ordinary skill in the art without departing from the spirit and technical ideas disclosed by the present invention shall still be covered by the claims of the present invention.

Claims

1. A clustering-based test graph drawing optimization method, characterized in that: The method comprises: Determine the number of test patterns that can be placed based on the planned test pattern drawing area and the area of ​​a single test pattern; Enumerate all combinations of test pattern design parameters and filter out combinations that violate design rules; Obtaining a target design parameter combination for the number of test patterns based on the combinations remaining after filtering; Based on the test pattern type, cluster analysis is performed on each target design parameter combination, and each target design parameter combination is arranged in order according to the category according to the clustering result to generate the entire test pattern layout; Wherein, the enumerating all combinations of test graphic design parameters and filtering combinations violating design rules includes: setting the range and step length of the test graphic design parameters of each test graphic type, enumerating all combinations of test graphic design parameters of each test graphic type; checking whether each combination violates the design rules, and filtering out the combinations violating the design rules; the setting of the range and step length of the test graphic design parameters of each test graphic type, and enumerating all combinations of test graphic design parameters of each test graphic type includes: defining each test graphic design parameter; setting the range and step length of the test graphic design parameters of each test graphic type; generating a design parameter vector for each test graphic type based on the set range and step length; wherein, the design parameter vector includes: combinations of all test graphic design parameters of the corresponding test graphic type; The method of obtaining the target design parameter combination for the number of test patterns based on the combinations remaining after filtering includes: comparing the number of combinations remaining after filtering and the determined number of test patterns that can be placed; if the remaining number of combinations is greater than the number of test patterns, reducing the number of combinations of enumerated test pattern design parameters by one or more of reducing the types of test patterns, narrowing the range of test pattern design parameters, and increasing the step size of test pattern design parameters until the target design parameter combination for the number of test patterns is obtained; if the remaining number of combinations is less than the number of test patterns, increasing the number of combinations of enumerated test pattern design parameters by one or more of increasing the types of test patterns, expanding the range of test pattern design parameters, and reducing the step size of test pattern design parameters until the target design parameter combination for the number of test patterns is obtained; if the remaining number of combinations is equal to the number of test patterns, taking the remaining combinations as the target design parameter combination; The method comprises: performing cluster analysis on each target design parameter combination based on the test pattern type, and arranging each target design parameter combination in order according to the category according to the clustering result, and generating the entire test pattern layout, including: determining the number of cluster categories and preliminary cluster centers based on the test pattern type, and classifying the design parameter combinations of the number of cluster categories by iterative clustering and cluster center adjustment; arranging the target design parameter combinations of each design parameter combination classification in order according to the category, and placing them in the test pattern layout in the order of arrangement, so as to obtain the entire test pattern layout.

2. The clustering-based test graph drawing optimization method according to claim 1, characterized in that: The determining of the number of cluster categories and preliminary cluster centers based on the test pattern type includes: Determine the number of clustering categories based on the types of existing test graphics; A target design parameter combination is randomly selected from each target design parameter combination of each test pattern type as a preliminary clustering center for the corresponding classification.

3. The clustering-based test graph drawing optimization method according to claim 1, characterized in that: The design parameter combination classification for determining the number of cluster categories by iterative clustering and cluster center adjustment includes: Based on the preliminary cluster centers, cluster the target design parameter combinations of non-cluster centers and perform cluster center adjustment operations to obtain the classification of each design parameter combination and the new cluster center; Check whether the cluster center is stable; If it is stable, the obtained design parameter combination classification is taken as the final design parameter combination classification; If it is unstable, based on the new cluster center, cluster the target design parameter combinations of non-cluster centers and perform cluster center adjustment operations until a stable cluster center is detected to obtain the final design parameter combination classification.

4. The clustering-based test graph drawing optimization method according to claim 3, characterized in that: The clustering and cluster center adjustment operation includes: For each target design parameter combination of non-cluster center, the distance from it to each cluster center is calculated respectively, and the target design parameter combination is classified into the same category as the cluster center with the smallest distance to it, so as to obtain the classification of each design parameter combination; Calculate the average value of each test graphic design parameter in each design parameter combination category, and combine to obtain a design parameter average value combination of the corresponding design parameter combination category; The distances from each target design parameter combination in each design parameter combination classification to its corresponding design parameter average value combination are calculated, and the target design parameter combination with the smallest distance is used as the new cluster center of the design parameter combination classification.

5. A clustering-based test graph drawing optimization system, characterized in that: The system comprises: A test pattern quantity determination module is configured to determine the number of test patterns that can be placed according to the planned test pattern drawing area and the area of ​​a single test pattern; A combination enumeration and filtering module, connected to the configuration test pattern quantity determination module, is used to enumerate all combinations of test pattern design parameters and filter combinations that violate design rules; A combination determination module, connected to the combination enumeration and filtering module, for obtaining a target design parameter combination of the number of test patterns based on the combinations remaining after filtering; A test pattern generation module, connected to the combination enumeration and filtering module and the combination determination module, is used to perform cluster analysis on each target design parameter combination based on the test pattern type, and arrange each target design parameter combination in order according to the category according to the clustering result to generate the entire test pattern layout; Wherein, the enumerating all combinations of test graphic design parameters and filtering combinations violating design rules includes: setting the range and step length of the test graphic design parameters of each test graphic type, enumerating all combinations of test graphic design parameters of each test graphic type; checking whether each combination violates the design rules, and filtering out the combinations violating the design rules; the setting of the range and step length of the test graphic design parameters of each test graphic type, and enumerating all combinations of test graphic design parameters of each test graphic type includes: defining each test graphic design parameter; setting the range and step length of the test graphic design parameters of each test graphic type; generating a design parameter vector for each test graphic type based on the set range and step length; wherein, the design parameter vector includes: combinations of all test graphic design parameters of the corresponding test graphic type; The method of obtaining the target design parameter combination for the number of test patterns based on the combinations remaining after filtering includes: comparing the number of combinations remaining after filtering and the determined number of test patterns that can be placed; if the remaining number of combinations is greater than the number of test patterns, reducing the number of combinations of enumerated test pattern design parameters by one or more of reducing the types of test patterns, narrowing the range of test pattern design parameters, and increasing the step size of test pattern design parameters until the target design parameter combination for the number of test patterns is obtained; if the remaining number of combinations is less than the number of test patterns, increasing the number of combinations of enumerated test pattern design parameters by one or more of increasing the types of test patterns, expanding the range of test pattern design parameters, and reducing the step size of test pattern design parameters until the target design parameter combination for the number of test patterns is obtained; if the remaining number of combinations is equal to the number of test patterns, taking the remaining combinations as the target design parameter combination; The method comprises: performing cluster analysis on each target design parameter combination based on the test pattern type, and arranging each target design parameter combination in order according to the category according to the clustering result, and generating the entire test pattern layout, including: determining the number of cluster categories and preliminary cluster centers based on the test pattern type, and classifying the design parameter combinations of the number of cluster categories by iterative clustering and cluster center adjustment; arranging the target design parameter combinations of each design parameter combination classification in order according to the category, and placing them in the test pattern layout in the order of arrangement, so as to obtain the entire test pattern layout.

6. An electronic terminal, characterized in that: include: one or more memories and one or more processors; The one or more memories are used to store computer programs; The one or more processors, connected to the memory, are configured to run the computer program to perform the method according to any one of claims 1 to 4.

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