Optimized pattern selection method, device, system and medium for photoresist model calibration

Abstract

The application provides a photoresist model calibration optimization pattern selection method, device, system and medium, the method comprises the following steps: obtaining a plurality of initial optimization patterns; Fourier transform is carried out to a plurality of initial optimization patterns to obtain the frequency spectrum corresponding to each initial optimization pattern respectively; the number of repetitions of the frequency spectrum corresponding to each initial optimization pattern and other frequency spectrums is detected, and each initial optimization pattern is sorted according to the number of repetitions from large to small; the feature size of each initial optimization pattern is sorted; the pattern of the first preset ranking, the pattern of the second preset ranking and the pattern with the smallest feature size in each initial optimization pattern are selected as the final optimization pattern. Therefore, the optimization pattern selection method based on the frequency spectrum repetition amount can avoid the phenomenon that the pattern selection is relatively blind in the experience-based selection method, reduces the pattern selection difficulty and time cost, and improves the model calibration accuracy.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of computer, in particular to a photoresist model calibration optimization pattern selection method, device, system and medium. BACKGROUND

[0002] A photoetching machine is an important device in semiconductor production and manufacturing, and is widely used in large-scale integrated circuit manufacturing. A photoetching process copies patterns on a mask to a photoresist coated on a silicon wafer through optical exposure, and then further transfers the patterns to the silicon wafer through development, etching and other processes. The photoetching process directly determines the feature size in an integrated circuit device, and is a key process in large-scale integrated circuit manufacturing.

[0003] A photoetching process can be described by an optical model and a photoresist model. The photoresist model describes a series of photochemical reactions of a photoresist excited by photons in an optical latent image, and finally leaves a photoresist pattern after baking and developing. In order to more accurately calculate the photoetching result, the photoresist model has developed from a threshold model to an accurate model for a specific photoetching process and a specific photoresist. However, in the development of photoetching, new photoetching technologies and photoresists always appear, which means that the original model parameters are no longer suitable for new photoetching processes, and model calibration is needed. In the model calibration process, the problem of optimization pattern selection will inevitably be faced.

[0004] Therefore, how to appropriately select the optimization pattern for photoresist model calibration, so as to improve the accuracy of the photoresist model, is a technical problem to be solved in the field. SUMMARY

[0005] In view of this, this summary section is provided to introduce the concepts in a simple form, which will be described in detail in the specific embodiments section. This summary section is not intended to identify key or essential features of the claimed technical solution, nor is it intended to limit the scope of the claimed technical solution.

[0006] The purpose of the present application is to provide a photoresist model calibration optimization pattern selection method, device, system and medium, which reduces the difficulty and time cost of pattern selection, and also improves the model calibration accuracy.

[0007] To achieve the above-mentioned purpose, the present application has the following technical solutions:

[0008] In a first aspect, the embodiments of the present application provide a photoresist model calibration optimization pattern selection method, comprising:

[0009] obtaining a plurality of initial optimization patterns;

[0010] performing Fourier transform on the plurality of initial optimization patterns to obtain a frequency spectrum corresponding to each of the initial optimization patterns respectively;

[0011] detecting a number of repetitions of the frequency spectrum corresponding to each of the initial optimization patterns with other frequency spectrums, and sorting the initial optimization patterns according to the number of repetitions from large to small;

[0012] sorting feature sizes of the initial optimization patterns;

[0013] selecting a pattern of a first preset ranking, a pattern of a second preset ranking and a pattern of a smallest feature size in the initial optimization patterns as a final optimization pattern.

[0014] In a possible implementation, the performing Fourier transform on the plurality of initial optimization patterns to obtain a frequency spectrum corresponding to each of the initial optimization patterns respectively comprises:

[0015] performing Fourier transform on the feature size, the period and the spatial coordinates of each of the initial optimization patterns to obtain a frequency domain coordinate of each of the initial optimization patterns.

[0016] In a possible implementation, the number of the initial optimization patterns is more than three.

[0017] In a possible implementation, after the photoresist model is calibrated by using the final optimization pattern, the method further comprises:

[0018] calculating a root mean square value of an average error of the photoresist model after calibration on the final optimization pattern.

[0019] In a second aspect, an embodiment of the present application provides an optimization pattern selection device for photoresist model calibration, comprising:

[0020] an acquisition unit configured to acquire a plurality of initial optimization patterns;

[0021] a transform unit configured to perform Fourier transform on the plurality of initial optimization patterns to obtain a frequency spectrum corresponding to each of the initial optimization patterns respectively;

[0022] a repetition number sorting unit configured to detect a number of repetitions of the frequency spectrum corresponding to each of the initial optimization patterns with other frequency spectrums, and sort the initial optimization patterns according to the number of repetitions from large to small;

[0023] a feature size sorting unit configured to sort feature sizes of the initial optimization patterns;

[0024] The selecting unit is configured to select a pattern with a first preset ranking before sorting, a pattern with a second preset ranking after sorting, and a pattern with a minimum feature size in each of the initial optimization patterns as the final optimization pattern.

[0025] In a possible implementation, the transforming unit is specifically configured to:

[0026] The Fourier transform is performed on the feature size, the period, and the spatial coordinates of each of the initial optimization patterns to obtain frequency domain coordinates of each of the initial optimization patterns.

[0027] In a possible implementation, the number of the initial optimization patterns is more than three.

[0028] In a possible implementation, the device further includes:

[0029] The root mean square value of the average error of the final optimization pattern is calculated based on the calibrated photoresist model.

[0030] In a third aspect, an embodiment of the present application provides a photoresist model calibration optimization pattern selection system, including:

[0031] A memory configured to store a computer program.

[0032] A processor configured to implement the steps of the photoresist model calibration optimization pattern selection method when the computer program is executed.

[0033] In a fourth aspect, an embodiment of the present application provides a computer readable medium, and the computer readable medium stores a computer program. When the computer program is executed by a processor, the steps of the photoresist model calibration optimization pattern selection method are implemented.

[0034] Compared with the prior art, the embodiments of the present application have the following beneficial effects:

[0035] The embodiment of the present application provides a kind of photoresist model calibration optimization pattern selection method, device, system and medium, the method comprises: obtaining multiple initial optimization patterns;The Fourier transform of multiple initial optimization patterns is obtained to the frequency spectrum corresponding to each initial optimization pattern respectively;The number of repetition of the frequency spectrum corresponding to each initial optimization pattern respectively and other frequency spectrum is detected, and each initial optimization pattern is sorted according to the number of repetition from large to small;The feature size of each initial optimization pattern is sorted;Select the pattern of first preset rank before sorting, the pattern of second preset rank after sorting and the pattern of minimum feature size in each initial optimization pattern as final optimization pattern.It is therefore proposed in the present application based on the optimization pattern selection mode of the amount of spectrum repetition, which can avoid the phenomenon that pattern selection is more blind in the experience-based selection mode, reduce the difficulty and time cost of pattern selection, and also improve the model calibration accuracy.Compared with the traditional selection mode based on spectrum classification, the accuracy of model calibration is also improved. BRIEF DESCRIPTION OF DRAWINGS

[0036] In order to more clearly illustrate the technical solutions of the embodiments of the present application or the prior art, the drawings needed to be used in the embodiments or prior art description will be briefly introduced. Obviously, the drawings in the following description are some embodiments of the present application, and those skilled in the art can also obtain other drawings according to these drawings without creative labor.

[0037] The above and other features, advantages, and aspects of the embodiments of the present disclosure will become more apparent with reference to the following detailed description in conjunction with the accompanying drawings. Throughout the drawings, the same or similar reference numerals refer to the same or similar elements. It should be understood that the drawings are schematic, and the original and elements are not necessarily drawn according to the scale.

[0038] Figure 1 A flowchart of a photoresist model calibration optimization pattern selection method provided by the embodiment of the present application is shown.

[0039] Figure 2 A schematic diagram of a photoresist model calibration optimization pattern selection device provided by the embodiment of the present application is shown. DETAILED DESCRIPTION

[0040] In order to make the above-mentioned purposes, features and advantages of the present application more apparent and easy to understand, the specific embodiments of the present application will be described in detail below with reference to the drawings.

[0041] Many particular details are set forth in the following description in order to provide a thorough understanding of the application. However, the application can be practiced according to the claims without some or all of these details. Indeed, the application is well suited to the wide alternative details and alternatives that can be implemented without departing from the scope of the present application. Accordingly, the particular details set forth in the following description should not be construed as limiting the scope of the present application.

[0042] As described in the background section, lithography is an important device in semiconductor production and manufacturing, and is widely used in the manufacturing of large scale integrated circuits. Lithography process copies the pattern on the reticle to the photoresist coated on the wafer by optical exposure, and then transfers the pattern to the wafer by developing, etching and other processes. The lithography process directly determines the feature size in the integrated circuit device, and is a key process in the manufacturing of large scale integrated circuits.

[0043] The lithography process can be described by optical model and photoresist model. The photoresist model describes that a series of photochemical reactions occur in the photoresist excited by photons in the optical latent image, and after baking and developing processes, the photoresist pattern is finally left. In order to more accurately calculate the lithography result, the photoresist model has developed from the original threshold model to the accurate model for specific lithography process and specific photoresist. However, in the development of lithography, there are always new types of lithography technology and photoresist, which means that the original model parameters are no longer applicable to the new lithography process, and the model calibration is needed. In the model calibration process, the problem of optimization pattern selection will inevitably be faced.

[0044] The optimization pattern selection is different, and the result of model calibration is different. The empirical selection method has obvious defects on the calibration result. In the process of light source mask optimization, the method based on spectral classification is used to select the optimization pattern, which has good effect, but for the optimization pattern selection in the photoresist model calibration process, the effect is not good.

[0045] To solve the above technical problems, the embodiment of the present application provides a photoresist model calibration optimization pattern selection method, device, system and medium, the method comprising: obtaining a plurality of initial optimization patterns; Fourier transforming the plurality of initial optimization patterns to obtain the frequency spectrum corresponding to each initial optimization pattern respectively; detecting the number of repetitions of the frequency spectrum corresponding to each initial optimization pattern with other frequency spectrums, and sorting each initial optimization pattern according to the number of repetitions from large to small; sorting the feature size of each initial optimization pattern; selecting the pattern of the first preset rank, the pattern of the second preset rank and the pattern with the smallest feature size in each initial optimization pattern as the final optimization pattern. Therefore, the optimization pattern selection method based on the frequency spectrum repetition amount can avoid the phenomenon of blind pattern selection in the experience-based selection method, reduce the pattern selection difficulty and time cost, and improve the model calibration accuracy. Compared with the traditional selection method based on frequency spectrum classification, the accuracy of model calibration is also improved.

[0046] Exemplary method

[0047] Referring to Figure 1 The flowchart of the photoresist model calibration optimization pattern selection method provided by the embodiment of the present application is shown, which comprises:

[0048] S101: obtaining a plurality of initial optimization patterns.

[0049] In the embodiment of the present application, to calibrate the photoresist model, a plurality of initial optimization patterns need to be obtained first.

[0050] For example, the embodiment of the present application calibrates a new 248nm photoresist model, and the initial optimization patterns used can include 26 one-dimensional line patterns with a period of 270nm and a line width of 140nm, a period of 1500nm and a line width of 155nm, etc.

[0051] In a possible implementation, the number of initial optimization patterns provided by the embodiment of the present application can include more than three. Since the more patterns selected during model calibration, the higher the model calibration accuracy will be, but the slower the speed will be, therefore a specific number of patterns need to be selected according to the specific situation. In order to better balance the requirements of model calibration accuracy and calibration speed.

[0052] S102: Fourier transforming the plurality of initial optimization patterns to obtain the frequency spectrum corresponding to each initial optimization pattern respectively.

[0053] In the embodiment of the present application, the plurality of initial optimization patterns can be Fourier transformed to obtain the frequency spectrum corresponding to each initial optimization pattern respectively.

[0054] Specifically, the position of each pattern spectrum can be obtained by using the Fourier transform formula of the periodic line. The line pattern can be regarded as a gate function rect (rectangular function), and the periodic repetition is the convolution with the comb function. After Fourier transform, it is the point multiplication of the sinc function and the comb function:

[0055]

[0056] That is, the line width of the line determines the drop amplitude of the sinc function in the frequency domain, and the period of the line determines the position of the sampling point in the frequency domain. According to the above formula, the spectrum distribution of all patterns can be obtained.

[0057] In the above formula (1), a is the characteristic size (CD, critical dimension) of the initial optimization pattern, b is the period (Pitch) of the initial optimization pattern, x is the spatial coordinate of the initial optimization pattern, f x is the frequency domain coordinate of the initial optimization pattern.

[0058] That is, the frequency domain coordinates of each initial optimization pattern are obtained by Fourier transform according to the characteristic size, period and spatial coordinate of each initial optimization pattern in formula (1), so as to obtain the frequency spectrum corresponding to each initial optimization pattern respectively.

[0059] S103: Detect the number of repetitions of the frequency spectrum corresponding to each initial optimization pattern and other frequency spectra, and sort each initial optimization pattern according to the number of repetitions from large to small.

[0060] S104: Sort the characteristic size of each initial optimization pattern.

[0061] Because all the orders except the zero-order diffraction spectrum in the spectrum of the pattern carry the information of the pattern, and the frequency spectra of different patterns may be repeated, that is, there may be the same information between different patterns. Therefore, in the process of pattern selection, the pattern containing more pattern information in the frequency domain is selected. That is, the selected optimization pattern includes: some patterns with a large number of repeated frequency spectra and other patterns with a small number of repeated frequency spectra, and the anchor pattern (the pattern with the smallest characteristic size) of the technology node. In this way, a good effect of photoresist physical model calibration is obtained.

[0062] Among them, because the pattern with the smallest characteristic size is the most difficult to be etched, the pattern with the smallest characteristic size can be selected as one of the final optimization patterns to obtain a good calibration result of the photoresist model.

[0063] Specifically, in the embodiment of the present application, the frequency spectrum repetition quantity of different line width and period line patterns commonly used in photoresist model calibration process can be sorted, and specific patterns can be selected.

[0064] For example, referring to Table 1, the number of frequency spectrum repetition of each initial optimization pattern provided in the embodiment of the present application and other patterns is shown.

[0065]

[0066] Table 1

[0067] As can be seen from Table 1, the initial optimization patterns can be sorted according to the number of frequency spectrum repetition from large to small as follows: S155P1500>S150P320>S155P440>S150P385>S155P520>S150P340=S150P380=S155P460.

[0068] In addition, in the embodiment of the present application, the feature size of each initial optimization pattern can be sorted, so that the selection of the optimization pattern can be based on the feature size of each initial optimization pattern.

[0069] S105: selecting the pattern ranked first, the pattern ranked second, and the pattern with the smallest feature size among the initial optimization patterns as the final optimization pattern.

[0070] In the embodiment of the present application, the pattern ranked first, the pattern ranked second, and the pattern with the smallest feature size among the initial optimization patterns can be selected as the final optimization pattern.

[0071] For example, referring to Table 2, several possible selection modes provided in the embodiment of the present application as the final optimization pattern are shown, which are divided into Option 1, Option 2, Option 3 and Option 4.

[0072]

[0073] Table 2

[0074] For Option 1, the pattern S155P1500 ranked first in the frequency spectrum repetition quantity among the initial optimization patterns, the patterns S150P340, S150P380 and S155P460 ranked last in the frequency spectrum repetition quantity among the initial optimization patterns, and the pattern S140P270 with the smallest feature size among the initial optimization patterns can be selected as the final optimization pattern.

[0075] For Option 2, the following can be selected as the final optimized graphs: graph S155P1500, which has the highest spectral repetition rate among the initial optimized graphs; graph S155P460, which has the lowest spectral repetition rate among the initial optimized graphs; graph S155P520, which has the second lowest spectral repetition rate among the initial optimized graphs; graph S150P320, which has the second highest spectral repetition rate among the initial optimized graphs; and graph S140P270, which has the smallest feature size among the initial optimized graphs.

[0076] For Option 3, the following can be selected as the final optimized graphs: graph S155P1500, which has the highest spectral repetition rate among the initial optimized graphs; graph S155P460, which has the lowest spectral repetition rate among the initial optimized graphs; graph S150P320, which has the second highest spectral repetition rate among the initial optimized graphs; graph S150P440, which has the third highest spectral repetition rate among the initial optimized graphs; and graph S140P270, which has the smallest feature size among the initial optimized graphs.

[0077] For Option 4, the following can be selected as the final optimized graphs: graph S155P1500, which has the highest spectral repetition rate among the initial optimized graphs; graph S155P460, which has the lowest spectral repetition rate among the initial optimized graphs; graph S155P520, which has the second lowest spectral repetition rate among the initial optimized graphs; graph S155P385, which has the third lowest spectral repetition rate among the initial optimized graphs; and graph S140P270, which has the smallest feature size among the initial optimized graphs.

[0078] Furthermore, in one possible implementation of this application embodiment, after calibrating the photoresist model using the final optimized pattern, the method provided in this application embodiment may further include:

[0079] Calculate the root mean square value of the average error of the calibrated photoresist model to the final optimized pattern.

[0080] Specifically, after determining the final optimized pattern, this embodiment of the application can also use the final optimized pattern to perform model calibration. After exposure, development and baking, multiple photoresist parameters of the photoresist model are optimized. Starting from the initial value of each parameter, the variation range is set to the common range of each parameter in general photoresist, so as to realize the calibration and optimization of the photoresist model provided by this embodiment of the application using the final optimized pattern.

[0081] After the photoresist model is calibrated and optimized, the root mean square (RMS) of the average error of the calibrated photoresist model to the final optimized pattern can be calculated. In other words, the root mean square of the average error of the final optimized pattern can be used as the evaluation function of the photoresist model.

[0082] In addition, in this embodiment, other graphics can also be used to verify the calibrated photoresist model.

[0083] Specifically, the optimized and calibrated parameters can be set as the parameter values ​​of the photoresist model, and the RMS value of the average error of all patterns can be calculated. The calibrated model has a significant improvement in the accuracy of almost all patterns, and the accuracy of the model continues to improve as the number of test patterns increases, and all of them have obvious advantages over the uncalibrated model.

[0084] As shown in Table 3, the patterns obtained using the pattern selection method based on spectral coverage in this invention were used for photoresist physical model calibration. Simultaneously, the same number of patterns were obtained using both experience-based and spectral classification-based selection methods for model calibration. Other patterns were used for verification. Comparing the results of the three methods, as shown in Table 3, this invention demonstrates significant advantages in both the absolute value (average) of the verification error and the stability (variance) of the results.

[0085]

[0086] Table 3

[0087] This application provides an optimized pattern selection method for photoresist model calibration. The method includes: acquiring multiple initial optimized patterns; performing Fourier transform on the multiple initial optimized patterns to obtain the spectrum corresponding to each initial optimized pattern; detecting the number of repetitions of the spectrum corresponding to each initial optimized pattern with other spectra, and sorting the initial optimized patterns according to the number of repetitions from largest to smallest; sorting the feature sizes of the initial optimized patterns; and selecting the first preset-ranked pattern, the second preset-ranked pattern, and the pattern with the smallest feature size among the initial optimized patterns as the final optimized pattern. Therefore, the optimized pattern selection method based on spectral repetition proposed in this application can avoid the blind pattern selection phenomenon in experience-based selection methods, reducing the difficulty and time cost of pattern selection, while also improving model calibration accuracy. Furthermore, compared with the traditional selection method based on spectrum classification, the model calibration accuracy is also significantly improved.

[0088] Exemplary device

[0089] See Figure 2The diagram shown is a schematic of an optimized pattern selection device for photoresist model calibration provided in an embodiment of this application, comprising:

[0090] Acquisition unit 201 is used to acquire multiple initial optimized graphics;

[0091] The transformation unit 202 is used to perform Fourier transform on the multiple initial optimized patterns to obtain the spectrum corresponding to each initial optimized pattern;

[0092] The repetition sorting unit 203 is used to detect the number of times the spectrum corresponding to each of the initial optimized graphics is repeated with other spectra, and sort each of the initial optimized graphics according to the number of repetitions from largest to smallest.

[0093] The feature size sorting unit 204 is used to sort the feature sizes of each of the initial optimized graphics;

[0094] Selection unit 205 is used to select the first preset ranking graphic, the second preset ranking graphic, and the graphic with the smallest feature size among the initial optimized graphics as the final optimized graphic.

[0095] In one possible implementation, the transformation unit is specifically used for:

[0096] The frequency domain coordinates of each initial optimized graphic are obtained by performing a Fourier transform based on the feature size, period, and spatial coordinates of each initial optimized graphic.

[0097] In one possible implementation, the number of initial optimized graphs includes three or more.

[0098] In one possible implementation, the device further includes:

[0099] Calculate the root mean square value of the average error of the calibrated photoresist model to the final optimized pattern.

[0100] This application provides an optimized pattern selection device for photoresist model calibration. The method applied to this device includes: acquiring multiple initial optimized patterns; performing Fourier transform on the multiple initial optimized patterns to obtain the spectrum corresponding to each initial optimized pattern; detecting the number of times the spectrum corresponding to each initial optimized pattern repeats with other spectra, and sorting the initial optimized patterns according to the number of repetitions from largest to smallest; sorting the feature sizes of the initial optimized patterns; and selecting the first preset-ranked pattern, the second preset-ranked pattern, and the pattern with the smallest feature size among the initial optimized patterns as the final optimized pattern. Therefore, the optimized pattern selection method based on spectral repetition proposed in this application can avoid the blind pattern selection phenomenon in experience-based selection methods, reducing the difficulty and time cost of pattern selection, while also improving model calibration accuracy. Furthermore, compared with the traditional selection method based on spectrum classification, the accuracy of model calibration is also significantly improved.

[0101] Based on the above embodiments, this application provides an optimized pattern selection system for photoresist model calibration, including:

[0102] Memory, used to store computer programs;

[0103] A processor, used to implement the steps of the optimized pattern selection method for photoresist model calibration as described above when executing the computer program.

[0104] Based on the above embodiments, this application also provides a computer-readable medium storing a computer program, which, when processed and executed, implements the steps of the optimized pattern selection method for photoresist model calibration as described above.

[0105] It should be noted that the computer-readable medium described in this disclosure can be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-readable storage medium can be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this disclosure, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in connection with an instruction execution system, apparatus, or device. In this disclosure, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium can be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wires, optical fibers, RF (radio frequency), etc., or any suitable combination thereof.

[0106] The aforementioned computer-readable medium may be included in the aforementioned system, or it may exist independently and not assembled into the system.

[0107] In particular, according to embodiments of this disclosure, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of this disclosure include a computer program product comprising a computer program carried on a non-transitory computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts.

[0108] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on its differences from other embodiments. In particular, the apparatus embodiments are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions of the method embodiments.

[0109] The above description is merely a preferred embodiment of this application. Although this application has disclosed preferred embodiments above, it is not intended to limit this application. Any person skilled in the art can make many possible variations and modifications to the technical solutions of this application using the methods and techniques disclosed above, or modify them into equivalent embodiments with equivalent changes, without departing from the scope of the technical solutions of this application. Therefore, any simple modifications, equivalent changes, and modifications made to the above embodiments based on the technical essence of this application without departing from the content of the technical solutions of this application shall still fall within the protection scope of the technical solutions of this application.

Claims

1. A method for optimizing pattern selection in photoresist model calibration, characterized in that, include: Obtain multiple initial optimized graphs; Perform a Fourier transform on the multiple initial optimized patterns to obtain the spectrum corresponding to each initial optimized pattern; The number of times the spectrum corresponding to each of the initial optimized patterns repeats with other spectra is detected, and the initial optimized patterns are sorted according to the number of repetitions from largest to smallest. The feature dimensions of each of the initial optimized graphics are sorted. The first preset-ranked graphic and the second preset-ranked graphic among the initial optimized graphics, sorted according to the number of repetitions, and the graphic with the smallest feature size among the initial optimized graphics, are selected as the final optimized graphic.

2. The method according to claim 1, characterized in that, The step of performing a Fourier transform on the multiple initial optimized patterns to obtain the spectrum corresponding to each initial optimized pattern includes: The frequency domain coordinates of each initial optimized graphic are obtained by performing a Fourier transform based on the feature size, period, and spatial coordinates of each initial optimized graphic.

3. The method according to claim 1, characterized in that, The number of initial optimized graphics includes three or more.

4. The method according to claim 1, characterized in that, After calibrating the photoresist model using the final optimized pattern, the process also includes: Calculate the root mean square value of the average error of the calibrated photoresist model to the final optimized pattern.

5. An optimized pattern selection device for photoresist model calibration, characterized in that, include: The acquisition unit is used to acquire multiple initial optimized graphs; The transformation unit is used to perform Fourier transform on the multiple initial optimized patterns to obtain the spectrum corresponding to each initial optimized pattern; The repetition sorting unit is used to detect the number of times the spectrum corresponding to each of the initial optimized graphics is repeated with other spectra, and sort the initial optimized graphics according to the number of repetitions from largest to smallest. A feature size sorting unit is used to sort the feature sizes of each of the initial optimized graphics; The selection unit is used to select the first preset-ranked graphic and the second preset-ranked graphic from the sorting of the initial optimized graphics according to the number of repetitions, and the graphic with the smallest feature size from the initial optimized graphics, as the final optimized graphic.

6. The apparatus according to claim 5, characterized in that, The transformation unit is specifically used for: The frequency domain coordinates of each initial optimized graphic are obtained by performing a Fourier transform based on the feature size, period, and spatial coordinates of each initial optimized graphic.

7. The apparatus according to claim 5, characterized in that, The number of initial optimized graphics includes three or more.

8. The apparatus according to claim 5, characterized in that, The device further includes: Calculate the root mean square value of the average error of the calibrated photoresist model to the final optimized pattern.

9. An optimized pattern selection system for photoresist model calibration, characterized in that, include: Memory, used to store computer programs; A processor, configured to implement the steps of the optimized pattern selection method for photoresist model calibration as described in any one of claims 1-4 when executing the computer program.

10. A computer-readable medium, characterized in that, The computer-readable medium stores a computer program that, when processed and executed, implements the steps of the optimized pattern selection method for photoresist model calibration as described in any one of claims 1-4.

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