Density adaptive virtual graph insertion method, computer equipment and storage medium

Abstract

The invention relates to a density self-adaptive virtual graph insertion method, computer equipment and a storage medium, and relates to the density self-adaptive virtual graph insertion method, which comprises the following steps of: S1, reading a layout unit of a chip; s2, setting a sampling window, traversing the array region through the sampling window, and obtaining the maximum pattern density of the array region; s3, virtual graphs are inserted into the blank areas, and the distance between every two adjacent virtual graphs is alpha * S; s4, adopting a global optimal solution solving algorithm, setting a target function, randomly iterating alpha at a preset step length, and calculating a current solution of the target function; and S5, repeating the step S4 for multiple times until the pattern density of the virtual pattern in the blank region reaches a target density which is the maximum pattern density of the array region. According to the invention, the virtual graph is inserted into the blank area, and the graph density of the inserted virtual graph is close to the maximum graph density of the array area, so that the process consistency of the chip layout unit is ensured.

Description

Technical Field

[0001] This invention belongs to the field of semiconductor technology, and particularly relates to a density-adaptive virtual graphics insertion method, a computer device, and a storage medium. Background Technology

[0002] In large-scale integrated circuit manufacturing, uneven pattern density in the chip physical layout can affect the consistency of subsequent chip layout processes. To improve chip production yield, CMP (Chip Motion Processing) requires relatively uniform chip layout cell density. This is typically achieved by inserting virtual patterns into blank areas of the chip. These virtual patterns are usually rectangular, and currently, all inserted virtual patterns are of a unique size. If the pattern density of the virtual patterns inserted into the blank areas of the chip is significantly lower than the maximum pattern density of the array region, it will have a very serious impact on device performance. Summary of the Invention

[0003] This application provides a density-adaptive virtual graphics insertion method, a computer device, and a storage medium. The method includes: Step S1: Reading the layout cells of a chip; Step S2: Setting a sampling window, traversing the array region through the sampling window to obtain the maximum graphics density of the array region; Step S3: Inserting virtual graphics in blank areas, with a spacing of α*S between adjacent virtual graphics; Step S4: Using a global optimal solution algorithm, setting an objective function, randomly iterating α with a preset step size, and calculating the current solution of the objective function; Step S5: Repeating Step S4 multiple times until the graphics density of the virtual graphics in the blank areas reaches the target density, where the target density is the maximum graphics density of the array region. This application inserts virtual graphics in blank areas, and the inserted virtual graphics' density approximates the maximum graphics density of the array region, ensuring the process consistency of the chip layout cells.

[0004] Other objects and advantages of the present invention can be further understood from the technical features disclosed herein.

[0005] To achieve one or more of the above objectives or other objectives, the present invention provides a density-adaptive virtual graphics insertion method, a computer device, and a storage medium.

[0006] A density-adaptive virtual graphics insertion method includes: Step S1: Read the layout units of the chip, wherein the layout units of the chip include array areas and blank areas; Step S2: Set a sampling window, and traverse the array region through the sampling window to obtain the maximum graphic density of the array region; Step S3: Insert virtual graphics within the blank area, with a spacing of α*S between adjacent virtual graphics; Step S4: Use a global optimal solution algorithm, set the objective function, randomly iterate α with a preset step size, and calculate the current solution of the objective function; Step S5: Repeat step S4 multiple times until the graphic density of the virtual graphic in the blank area reaches the target density, where the target density is the maximum graphic density of the array area.

[0007] α is the spacing coefficient, and S is the initial spacing of the virtual graphics.

[0008] The initial value of the spacing coefficient α is 1.

[0009] The global optimal solution algorithm used is one of the following: simulated annealing algorithm, genetic algorithm, and binary brute-force search algorithm.

[0010] Step S4 further includes: Set constraint terms and determine whether the current solution of the objective function conforms to the preset interval of the constraint terms. If it does, continue iterating α.

[0011] The simulated annealing algorithm iterates over the spacing coefficient α from an initial value based on the objective function, with an iteration step size of 0.01 for the spacing coefficient α.

[0012] After reaching the predetermined number of iterations, the simulated annealing algorithm proceeds to the next temperature and adjusts the iteration step size of the spacing coefficient α to 0.001.

[0013] The adjustment factor for the iteration step size of the spacing coefficient α is 0.1.

[0014] A computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps of the density-adaptive virtual graphics insertion method described above.

[0015] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the density-adaptive virtual graphics insertion method described in any of the preceding claims.

[0016] Compared with the prior art, the beneficial effects of the present invention mainly include: This application inserts virtual graphics within blank areas, and the density of the inserted virtual graphics approximates the maximum density of the array area, ensuring the process consistency of the chip layout units.

[0017] To make the above and other objects, features and advantages of the present invention more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the specific embodiments of the present invention, the drawings used in the description of the embodiments 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.

[0019] Figure 1 This is a schematic flowchart of a density-adaptive virtual graphics insertion method provided in Embodiment 1 of this application.

[0020] Figure 2 This is a schematic diagram of the simulated annealing algorithm provided in Embodiment 1 of this application.

[0021] Figure 3 This is a schematic diagram of the genetic algorithm provided in Embodiment 1 of this application.

[0022] Figure 4 This is a schematic diagram of the binary brute-force search algorithm provided in Embodiment 1 of this application. Detailed Implementation

[0023] The foregoing and other technical contents, features, and effects of the present invention will be clearly presented in the following detailed description of a preferred embodiment with reference to the accompanying drawings. The directional terms mentioned in the following embodiments, such as up, down, left, right, front, or back, are merely for reference to the accompanying drawings. Therefore, the directional terms used are for illustrative purposes and not for limiting the present invention.

[0024] The embodiments of this application will now be described in detail with reference to the accompanying drawings. However, those skilled in the art will understand that many technical details have been provided in the embodiments of this application to facilitate a better understanding of the application. However, the technical solutions claimed in this application can be implemented even without these technical details and various variations and modifications based on the following embodiments.

[0025] Example 1 like Figure 1 As shown, a density-adaptive virtual graphics insertion method includes: Step S1: Read the layout cells of the chip, which include the array area and the blank area; Step S2: Set a sampling window, traverse the array region through the sampling window, and obtain the maximum graphic density of the array region; Step S3: Insert virtual graphics in the blank area, with a spacing of α*S between adjacent virtual graphics; where α is the spacing coefficient and S is the initial spacing between the virtual graphics. Step S4: Use a global optimal solution algorithm, set the objective function, randomly iterate α with a preset step size, and calculate the current solution of the objective function; Step S5: Repeat step S4 multiple times until the virtual graphic density in the blank area reaches the target density, which is the maximum graphic density of the array area.

[0026] Specifically, the global optimal solution algorithm used in this application is one of the following: simulated annealing algorithm, genetic algorithm, and binary brute-force search algorithm; like Figure 2 As shown, in a preferred embodiment of this application, the embodiment of this application solves the global optimal solution based on the simulated annealing algorithm, and the specific process includes: The initial conditions are set, including the size of the sampling window, the traversal step size of the sampling window, and the number of iterations. As a preferred embodiment of this application, the chip size of this embodiment is 100*100um, the size of the sampling window is 10um*10um, the traversal step size of the sampling window is 5um, and the number of iterations is 50. Set constraints, including target density, maximum graphic density of each sampling window, minimum graphic density of each sampling window, standard deviation, range, tolerance, and weight of each constraint. The value range of α is [0.7, 1.42]. Step S1: Determine the objective function based on the constraints. The objective function is: U α ∈±U tolerance Where Di is the graphic density within each sampling window, and S is... polygon S represents the total area of ​​the graphic within the sampling window. windows Let D be the total area of ​​the sampling window, n be the total number of sampling windows, and D be the total area of ​​the sampling window. target For the target density, U α = U represents uniformity. α Indicates uniformity under the current α; Step S2: At the current temperature, iterate α based on a preset step size and calculate the current solution of the objective function; wherein, the initial value of the spacing coefficient α is 1, and the iteration step size of the spacing coefficient α is 0.01; Step S3: Determine whether the current solution of the objective function meets the preset interval of the constraint terms. If it does, accept the current solution. Step S4: Iterate based on the preset step size α and calculate the current solution of the objective function; Step S5: Repeat steps S3 and S4 until the preset number of iterations is reached; Step S6: Proceed to the next temperature, repeat steps S2 and S5, and adjust the iteration step size of the spacing coefficient α to 0.001. The adjustment coefficient of the iteration step size of the spacing coefficient α is 0.1, until the termination temperature and / or the threshold of the objective function are reached, and output the global optimal solution.

[0027] like Figure 3 As shown, the specific process of finding the global optimal solution based on the genetic algorithm is as follows: Step S1: Obtain the n-bit binary code, where the value of α is in the range of [0.7, 1.42]. The larger n is, the more accurate it is, and it must be greater than 7. Step S2: Randomly generate 2^n-1 samples and calculate the insertion density of the virtual graphic; Step S3: Sort the virtual graphic insertion densities calculated from the samples in ascending order, and retain 25%-75% of the samples; Step S4: Use 3-5 bit exchange coding to perform crossover mutation on the retained samples to obtain mutated samples; Step S5: Perform a repeatability check on the samples from Step S3 and Step S4; Step S6: Calculate the insertion density of the virtual graphic based on the mutated samples, and merge and sort it with the samples retained in step S3; Step S7: Repeat steps S3 to S5, repeating the process n-2 times; Step S8: Take D from the final remaining samples targert ±D tolerance U target ±U tolerance The best-performing sample; where D is the density, and there is a target value (D). target ) and tolerance (D tolerance The constraint is D∈[D] target -D tolerance D target +D tolerance ]; U represents uniformity, and there is a target value (U target ) and tolerance (U tolerance ), U=Σ(D windows -D avg ) 2 D windows For the target density, D avg U is the average density of all window graphics, and U is the low-optimality index (i.e., the smaller U is, the better). The constraint is U∈[U... target -U tolerance U target +U tolerance ].

[0028] like Figure 4As shown, the specific process of finding the global optimal solution based on the binary brute-force search algorithm is as follows: Step S1: Divide the interval (0.7, 1.42) into 10 equal parts and calculate the insertion density of the virtual graphic; Step S2: n=0; Step S3: Take the optimal density α value, α = α ± (0.7 / 2 to the power of n) calculate the insertion density of the virtual graphic, and then take the optimal density α value; Step S4: n = n + 1, iterate N times, the larger N is, the better the accuracy, until n = N; Step S5: Take D from the sample targert ±D tolerance U target ±U tolerance The best-performing sample; where D is the density, and there is a target value (D). target ) and tolerance (D tolerance The constraint is D∈[D] target -D tolerance D target +D tolerance ]; U represents uniformity, and there is a target value (U target ) and tolerance (U tolerance ), U=Σ(D windows -D avg ) 2 D windows For the target density, D avg U is the average density of all window graphics, and U is the low-optimality index (i.e., the smaller U is, the better). The constraint is U∈[U... target -U tolerance U target +U tolerance ].

[0029] Example 2 Embodiment 2 of this application provides a computer device for implementing the steps of the above-described density-adaptive virtual graphics insertion method. This computer device may include: a processor, a communications interface, a memory, and a communication bus. The processor, communications interface, and memory communicate with each other via the communication bus. The processor can call logical instructions from the memory. These logical instructions can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium.

[0030] Example 3 Embodiment 3 of this application provides a computer program product, which includes a computer program stored on a non-transitory computer-readable storage medium. The computer program includes program instructions, and when the program instructions are executed by the computer, the computer is able to perform the steps of the density-adaptive virtual graphics insertion method described above.

[0031] Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0032] Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., including several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods of various embodiments or some parts of the embodiments.

[0033] In summary, this application provides a density-adaptive virtual graphics insertion method, a computer device, and a storage medium. The method includes: Step S1: Reading the layout cells of a chip; Step S2: Setting a sampling window, traversing the array region through the sampling window to obtain the maximum graphics density of the array region; Step S3: Inserting virtual graphics in blank areas, with a spacing of α*S between adjacent virtual graphics; Step S4: Using a global optimal solution algorithm, setting an objective function, randomly iterating α with a preset step size, and calculating the current solution of the objective function; Step S5: Repeating Step S4 multiple times until the graphics density of the virtual graphics in the blank areas reaches the target density, where the target density is the maximum graphics density of the array region. This application inserts virtual graphics in blank areas, and the inserted virtual graphics' density approximates the maximum graphics density of the array region, ensuring the process consistency of the chip layout cells.

[0034] This application inserts virtual graphics within blank areas, and the density of the inserted virtual graphics approximates the maximum density of the array area, ensuring the process consistency of the chip layout units.

[0035] The common English terms or letters used in this invention for clarity of description are for illustrative purposes only and are not limiting interpretations or specific uses. They should not be used to limit the scope of protection of this invention based on their possible Chinese translations or specific letters.

[0036] It should also be noted that in this article, relational terms such as “first” and “second” are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations.

Claims

1. A method for adaptive virtual graphics insertion with density, characterized by, include: Step S1: Read the layout units of the chip, wherein the layout units of the chip include array areas and blank areas; Step S2: Set a sampling window, and traverse the array region through the sampling window to obtain the maximum graphic density of the array region; Step S3: Insert virtual graphics within the blank area, with a spacing of α*S between adjacent virtual graphics; Step S4: Use a global optimal solution algorithm, set the objective function, randomly iterate α with a preset step size, and calculate the current solution of the objective function; Step S5: Repeat step S4 multiple times until the graphic density of the virtual graphic in the blank area reaches the target density, where the target density is the maximum graphic density of the array area.

2. The method of claim 1, wherein the density adaptive virtual graphics insertion method is characterized by, α is the spacing coefficient, and S is the initial spacing of the virtual graphics.

3. The method of claim 2, wherein the density adaptive virtual graphics insertion method is characterized by, The initial value of the spacing coefficient α is 1.

4. The method of claim 1, wherein the density adaptive virtual graphics insertion method is characterized by, The global optimal solution algorithm used is one of the following: simulated annealing algorithm, genetic algorithm, and binary brute-force search algorithm.

5. The method of claim 4, wherein the density adaptive virtual graphics insertion method is characterized by, Step S4 further includes: Set constraint terms and determine whether the current solution of the objective function conforms to the preset interval of the constraint terms. If it does, continue iterating α.

6. The method of claim 5, wherein the density adaptive virtual graphics insertion method is characterized by, The simulated annealing algorithm iterates over the spacing coefficient α from an initial value based on the objective function, with an iteration step size of 0.01 for the spacing coefficient α.

7. The method of claim 6, wherein the density adaptive virtual graphics insertion method is characterized by, After reaching the predetermined number of iterations, the simulated annealing algorithm proceeds to the next temperature and adjusts the iteration step size of the spacing coefficient α to 0.

001.

8. The method of claim 7, wherein the density adaptive virtual graphics insertion method is characterized by, The adjustment factor for the iteration step size of the spacing coefficient α is 0.

1.

9. A computer device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the steps of the density-adaptive virtual graphics insertion method as described in any one of claims 1 to 8.

10. A computer-readable storage medium having stored thereon a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the density-adaptive virtual graphics insertion method as described in any one of claims 1 to 8.

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