Method, device and equipment for optimizing layout photoetching process window and medium

Abstract

The invention provides an optimization method and device for a layout photoetching process window, equipment and a medium. The optimization method comprises the following steps: dividing an original layout to obtain a plurality of first layout fragments; performing process window optimization on the first layout fragment by utilizing a process window optimization algorithm to obtain a second layout fragment; the first focal depth corresponding to the first layout segment is smaller than the second focal depth corresponding to the second layout segment; the first focal depth and the second focal depth are obtained by respectively inputting the first layout fragment and the second layout fragment into a convolutional network model for prediction. By using the process window optimization algorithm, the focal depth of the first layout fragment can be optimized, namely, the process window is expanded, optimization is carried out before the mask design stage, the process window can be improved as much as possible, and the process window optimization algorithm and the convolutional network model are combined in the scheme, so that the process window can be expanded as much as possible, and the optimization accuracy can be improved as much as possible.

Description

Technical Field

[0001] This application relates to the field of semiconductor technology, and particularly to an optimization method, device, equipment and medium for the lithography process window of a layout. Background Art

[0002] The manufacturing process of integrated circuits highly depends on the lithography compliance of layout patterns. The simplified design process of digital circuits includes logic design, layout design and mask design. By adopting various resolution enhancement technologies, such as optical proximity correction, sub-resolution assist feature insertion and inverse lithography technology, etc., the lithography printability is optimized in the mask design stage, that is, the lithography process window is optimized. However, these technologies are carried out in the mask design stage where the placement and routing of the layout have been determined, and the optimization effect is not significant. Therefore, the space for enhancing lithography printability is extremely limited, and there are certain limitations in optimizing the lithography process window. Summary of the Invention

[0003] In view of this, the purpose of this application is to provide an optimization method, device, equipment and medium for the lithography process window of a layout, which can expand the process window as much as possible and improve the optimization accuracy. The specific solutions are as follows:

[0004] On the one hand, this application provides an optimization method for the lithography process window of a layout, including:

[0005] Dividing the original layout to obtain multiple first layout segments;

[0006] Performing process window optimization on the first layout segment by using a process window optimization algorithm to obtain a second layout segment; the first depth of focus corresponding to the first layout segment is less than the second depth of focus corresponding to the second layout segment; the first depth of focus and the second depth of focus are obtained by inputting the first layout segment and the second layout segment into a convolutional network model for prediction respectively.

[0007] Specifically, performing process window optimization on the first layout segment by using a process window optimization algorithm to obtain a second layout segment includes:

[0008] When the graphic information of the first layout segment is lower than the preset graphic information, performing process window optimization on the first layout segment by using a brute-force cracking algorithm to obtain the second layout segment.

[0009] Specifically, the method further includes:

[0010] When the graphic information of the first layout segment is not lower than the preset graphic information, performing process window optimization on the first layout segment by using a gradient optimization algorithm to obtain the second layout segment.

[0011] Specifically, the training process of the convolutional network model includes:

[0012] Obtain multiple third - edition picture segments;

[0013] Optically simulate the multiple third - edition picture segments to obtain multiple third focal depths, where each third focal depth corresponds to each third - edition picture segment;

[0014] Use the multiple third - edition picture segments and the multiple third focal depths to train a preset model, and after the training is completed, obtain the convolutional network model.

[0015] On the other hand, an embodiment of the present application also provides an optimization device for a layout lithography process window, including:

[0016] A division unit, configured to divide the original layout to obtain multiple first - edition picture segments;

[0017] A first optimization unit, configured to optimize the process window of the first - edition picture segments using a process window optimization algorithm to obtain second - edition picture segments; the first focal depth corresponding to the first - edition picture segments is less than the second focal depth corresponding to the second - edition picture segments; the first focal depth and the second focal depth are obtained by respectively inputting the first - edition picture segments and the second - edition picture segments into the convolutional network model for prediction.

[0018] Specifically, the first optimization unit is configured to:

[0019] When the graphic information of the first - edition picture segments is lower than the preset graphic information, optimize the process window of the first - edition picture segments using a brute - force cracking algorithm to obtain the second - edition picture segments.

[0020] Specifically, the device further includes:

[0021] A second optimization unit, configured to optimize the process window of the first - edition picture segments using a gradient optimization algorithm to obtain the second - edition picture segments when the graphic information of the first - edition picture segments is not lower than the preset graphic information.

[0022] Specifically, the training process of the convolutional network model includes:

[0023] Obtain multiple third - edition picture segments;

[0024] Optically simulate the multiple third - edition picture segments to obtain multiple third focal depths, where each third focal depth corresponds to each third - edition picture segment;

[0025] Use the multiple third - edition picture segments and the multiple third focal depths to train a preset model, and after the training is completed, obtain the convolutional network model.

[0026] In another aspect, an embodiment of the present application provides a computer device, which includes a processor and a memory:

[0027] The memory is used to store program code and transmit the program code to the processor;

[0028] The processor is used to execute the method described in the above aspect according to the instructions in the program code.

[0029] In another aspect, an embodiment of the present application provides a computer-readable storage medium, which is used to store a computer program, and the computer program is used to execute the method described in the above aspect.

[0030] An embodiment of the present application provides an optimization method, device, equipment and medium for a layout lithography process window, which can divide an original layout to obtain multiple first layout segments; use a process window optimization algorithm to optimize the process window of the first layout segments to obtain second layout segments; the first depth of focus corresponding to the first layout segments is less than the second depth of focus corresponding to the second layout segments; the first depth of focus and the second depth of focus are obtained by inputting the first layout segments and the second layout segments into a convolutional network model for prediction respectively. By using the process window optimization algorithm, the depth of focus of the first layout segments can be optimized, that is, the process window can be expanded, and the optimization is carried out before the mask design stage, which can maximize the process window. This solution combines the process window optimization algorithm and the convolutional network model, which can maximize the process window and improve the optimization accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the following drawings are some embodiments of the present application. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0032] Figure 1 Shows a schematic flowchart of an optimization method for a layout lithography process window provided by an embodiment of the present application;

[0033] Figure 2 Shows a schematic flowchart of another optimization method for a layout lithography process window provided by an embodiment of the present application;

[0034] Figure 3 Shows a schematic diagram of a lithography process window provided by an embodiment of the present application;

[0035] Figure 4 Shows a schematic diagram of a gradient optimization algorithm provided by an embodiment of the present application;

[0036] Figure 5 It is a structural block diagram of an optimization device for a layout lithography process window provided by an embodiment of the present application;

[0037] Figure 6 It is a structural diagram of a computer device provided by an embodiment of the present application. Specific embodiments

[0038] To make the above objects, features, and advantages of the present application more obvious and understandable, the specific embodiments of the present application will be described in detail below with reference to the accompanying drawings.

[0039] Many specific details are set forth in the following description to facilitate a thorough understanding of the present application, but the present application may also be implemented in other ways different from those described herein. Those skilled in the art can make similar generalizations without departing from the spirit of the present application. Therefore, the present application is not limited by the specific embodiments disclosed below.

[0040] For ease of understanding, a method, device, equipment, and medium for optimizing a layout lithography process window provided by an embodiment of the present application will be described in detail below with reference to the accompanying drawings.

[0041] Refer to Figure 1 As shown, it is a schematic flowchart of a method for optimizing a layout lithography process window provided by an embodiment of the present application. The method may include the following steps.

[0042] S101, divide the original layout to obtain a plurality of first layout segments.

[0043] In an embodiment of the present application, the original layout is the layout to be optimized. The original layout can be divided and cut to obtain a plurality of layout segments (clips), denoted as the first layout segments. That is to say, the first layout segments are smaller-sized layout segments cut from the original layout. Subsequently, the lithography process window of each first layout segment can be optimized. Compared with directly optimizing the original layout with a larger size, optimizing each layout segment can optimize the process window of each area, with more optimization times, more meticulousness, and better optimization effect of the lithography process window.

[0044] Specifically, refer to Figure 2 As shown, it is another method for optimizing a layout lithography process window provided by an embodiment of the present application. The original layout (initial layout) can be segmented to obtain the first layout segments (initial clips).

[0045] S102. Optimize the process window of the first - version image segment using a process window optimization algorithm to obtain a second - version image segment. The first depth of focus corresponding to the first - version image segment is less than the second depth of focus corresponding to the second - version image segment. The first depth of focus and the second depth of focus are obtained by respectively inputting the first - version image segment and the second - version image segment into a convolutional neural network model for prediction.

[0046] Specifically, for each first - version image segment, the process window optimization algorithm can be used to optimize the process window, thereby obtaining an optimized version of the image segment, denoted as the second - version image segment. The depth of focus (DOF) corresponding to the image segment can be used to evaluate the lithography process window. The larger the depth of focus, the larger the lithography process window. The depth of focus of the first - version image segment is denoted as the first depth of focus, and the depth of focus of the second - version image segment is denoted as the second depth of focus. The second depth of focus is greater than the first depth of focus. Next, a brief description of the process window is given.

[0047] In a projection optical lithography system, for a given feature size, the range of the imaging position deviation from the best - focus plane position that can ensure the line quality is defined as the depth of focus. For the lithography process, the larger the depth of focus, the more favorable the exposure of the lithography pattern. The relationship between the depth of focus, the exposure wavelength, and the numerical aperture of the projection objective is as follows:

[0048]

[0049] In the formula, k 2 is the depth - of - focus process factor. As the focus - plane position changes, the quality of the exposed lines also changes. Thus, there must be a position where the quality of the exposed lines is the best, and this position is called the best - focus plane. The value of the actual imaging plane deviating from the best - focus plane is called the defocus amount.

[0050] That is to say, the depth of focus is the range where the imaging position is allowed to deviate from the best - focus plane position, and the defocus amount is the range where the actual imaging position deviates from the best - focus plane position.

[0051] The purpose of the lithography process is to copy the pattern on the mask to the photoresist with a certain thickness. In order to obtain lines with steep sidewalls, the imaging intensity should be as consistent as possible within the photoresist thickness range, which requires that the depth of focus of the projection lithography be greater than the thickness of the photoresist.

[0052] Related to the formula of lithography resolution, the expression of the depth of focus can be rewritten as:

[0053]

[0054] The critical dimension CD of the lithographic exposure pattern has large fluctuations with the changes in exposure dose and defocus amount. In the actual lithography process, the electrical performance of the integrated circuit chip allows for a certain error in the critical dimension of the lithographic exposure pattern, and this allowable error range is usually ±10% CD. According to this standard, the eligible points can be marked on the Poisson curve graph of the lithographic imaging and connected, and we can obtain as Figure 3 shown in the process window.

[0055] Figure 3 The part of the maximum rectangle or ellipse envelope allowed by the curve shown in Figure 3 is called the process window. In the lithography process, an elliptical process window is generally used. At this time, within the closed area, only an ellipse with the largest area can be obtained. It should be noted that the rectangles or circles shown in the figure are drawn within the closed area formed by two curves, and these two curves represent the exposure doses under the conditions of CD(1 + 10%) and CD(1 - 10%) respectively at different focal plane positions.

[0056] In the actual lithography process, the general standard for measuring the process window is the depth of focus value (that is, fixing the Y-axis length of the ellipse and measuring the X-axis length of the ellipse) when the exposure dose changes by 5%. The larger this value is, the larger the process window is. Therefore, the size of the process window can be measured by the depth of focus value.

[0057] In the embodiment of the present application, a convolutional neural network (CNN) model can be used to predict the depth of focus. The first version of the picture segment is input into the convolutional neural network model to obtain the first depth of focus, and the second version of the picture segment is input into the convolutional neural network model to obtain the second depth of focus. Specifically, during the optimization process, the first version of the picture segment and the first depth of focus can be input into the process window optimization algorithm for optimization using the algorithm. After obtaining the new version of the picture segment, the convolutional neural network model is used to predict the depth of focus value until the optimal version of the picture segment is found, that is, the second version of the picture segment is obtained, and the second version of the picture segment has the second depth of focus.

[0058] In this way, by using the process window optimization algorithm, the depth of focus of the first version of the picture segment can be optimized, that is, the process window can be enlarged, and the optimization process is carried out before the mask design stage, which can improve the process window as much as possible. In addition, this solution combines the process window optimization algorithm and the convolutional neural network model, which can expand the process window as much as possible, and the optimization efficiency is relatively high, and the optimization accuracy can also be improved.

[0059] In the embodiment of the present application, before using the convolutional neural network model to predict the depth of focus, the convolutional neural network model can be trained to improve the model prediction accuracy.

[0060] Specifically, multiple third - edition picture segments can be obtained, that is, clips collection is performed. The third - edition picture segments are segmented from the unoptimized picture segments. Multiple third - edition picture segments can be subjected to optical simulation to obtain multiple third depth - of - focus values. Each third depth - of - focus value corresponds to each third - edition picture segment, thereby obtaining a large number of third - edition picture segments and corresponding third depth - of - focus values, increasing the quantity of the training set. Then, the preset model is trained using multiple third - edition picture segments and multiple third depth - of - focus values. After the iteration stops, the training is completed to obtain a convolutional network model. It can be understood that after the model training is completed, the first - edition picture segment to be optimized can be directly input into the convolutional network model and the process window optimization algorithm, without being input into the optical simulation model.

[0061] To further improve the optimization efficiency and accuracy, different process window optimization algorithms can be selected for optimization according to the graphic information in the first - edition picture segment, that is, algorithm selection is performed.

[0062] In a possible implementation, when the graphic information of the first - edition picture segment is lower than the preset graphic information, the first - edition picture segment is optimized using the brute - force algorithm for the process window to obtain the second - edition picture segment (clip after LRE). Multiple second - edition picture segments can be spliced to obtain the optimized layout (layout after LRE).

[0063] Specifically, the graphic information of the first - edition picture segment is the size, shape, quantity, etc. of the graphics in the picture segment. For example, the graphic information can be the total quantity of the graphics in the first - edition picture segment, or the total area size of the graphics, etc. No specific limitation is made here. The preset graphic information can also be the preset total quantity of graphics, the preset total area size of graphics. That is to say, the graphic information of the first - edition picture segment and the preset graphic information are information of the same dimension, such as both being the total quantity of graphics.

[0064] When the graphic information of the first - edition picture segment is lower than the preset graphic information, the process window optimization algorithm can be the brute - force algorithm. Using the brute - force algorithm for optimization, the brute - force algorithm can try to explore all optimization schemes, perform various optimizations on the first - edition picture segment such as stretching and shifting the graphics, obtain a large number of new picture segments, and determine the picture segment with the largest lithography process window from them, thereby obtaining the second - edition picture segment.

[0065] In this way, when the graphic information in the first - edition picture segment is relatively simple, using the brute - force algorithm for optimization can improve the optimization effect and accurately determine the second - edition picture segment with the largest process window.

[0066] In another possible implementation, when the graphic information of the first version of the picture segment is not lower than the preset graphic information, the graphic information is relatively complex at this time, and it will be very time-consuming to optimize using the brute-force cracking algorithm. At this time, the gradient optimization algorithm (GREA) can be used to optimize the process window of the first version of the picture segment to obtain the second version of the picture segment.

[0067] Specifically, referring to Figure 4 As shown, it is a schematic diagram of a gradient optimization algorithm provided by an embodiment of the present application. When using the gradient optimization algorithm for optimization, X can represent the first version of the picture segment, X' can represent the optimized version of the picture segment (i.e., the temporary optimal solution), and X'' represents the second version of the picture segment (i.e., the optimal solution), that is, the optimized version of the picture segment with the largest lithography process window.

[0068] In the initial stage of 1-4, X, X', and X'' can be initialized. The optimization scheme S is assigned to X and X'. ΔF represents the DOF increment, that is, the difference in the depth of focus between the first version of the picture segment and the second version of the picture segment, which can be initially set to 1, and the key optimal solution X'' is cleared.

[0069] As long as X is not equal to X'', steps 6-12 are executed. As long as the DOF increment is greater than 0, X' is assigned to X, and then the algorithm continues to execute. The function G(x i ) can be expressed as the direction that can increase the DOF, and x i represents the graphics in the layout, such as small line segments, etc. The function L can represent operations on the graphics in the picture segment, such as extension, translation, scaling, etc., so as to obtain the optimized version of the picture segment X', that is, the temporary optimal solution.

[0070] Step 13 means performing local brute-force cracking near the temporary optimal solution instead of global brute-force cracking. Local brute-force search will limit the search range. As long as this temporary optimal solution is also the optimal solution in its neighborhood, the algorithm ends, and this solution is the optimal solution. Otherwise, the optimal solution in the neighborhood is used as the starting point and then optimized until the optimal solution is found.

[0071] It can be seen that by using the gradient-based optimization algorithm, it is possible to find the second version of the picture segment with the largest lithography process window while reducing the amount of calculation and improving the efficiency.

[0072] The embodiment of the present application provides an optimization method for the process window of layout lithography, which can divide the original layout to obtain multiple first layout picture segments; perform process window optimization on the first layout picture segments by using a process window optimization algorithm to obtain second layout picture segments; the first depth of focus corresponding to the first layout picture segments is less than the second depth of focus corresponding to the second layout picture segments; the first depth of focus and the second depth of focus are obtained by inputting the first layout picture segments and the second layout picture segments into a convolutional network model respectively. By using the process window optimization algorithm, the depth of focus of the first layout picture segments can be optimized, that is, the process window can be expanded, and the optimization is carried out before the mask design stage, which can improve the process window as much as possible. This solution combines the process window optimization algorithm and the convolutional network model, which can expand the process window as much as possible and improve the optimization accuracy.

[0073] Based on the above optimization method for the process window of layout lithography, the embodiment of the present application further provides an optimization device for the process window of layout lithography, refer to Figure 5 As shown in the structure block diagram of an optimization device for the process window of layout lithography provided by the embodiment of the present application, the device may include:

[0074] A dividing unit 201, configured to divide the original layout to obtain multiple first layout picture segments;

[0075] A first optimization unit 202, configured to perform process window optimization on the first layout picture segments by using a process window optimization algorithm to obtain second layout picture segments; the first depth of focus corresponding to the first layout picture segments is less than the second depth of focus corresponding to the second layout picture segments; the first depth of focus and the second depth of focus are obtained by inputting the first layout picture segments and the second layout picture segments into a convolutional network model respectively.

[0076] Specifically, the first optimization unit is configured to:

[0077] When the graphic information of the first layout picture segments is lower than the preset graphic information, perform process window optimization on the first layout picture segments by using a brute-force cracking algorithm to obtain the second layout picture segments.

[0078] Specifically, the device further includes:

[0079] A second optimization unit, configured to perform process window optimization on the first layout picture segments by using a gradient optimization algorithm to obtain the second layout picture segments when the graphic information of the first layout picture segments is not lower than the preset graphic information.

[0080] Specifically, the training process of the convolutional network model includes:

[0081] Obtain multiple third layout picture segments;

[0082] Optically simulate multiple third - edition picture segments to obtain multiple third depth - of - focus values, with each third depth - of - focus value corresponding to each third - edition picture segment;

[0083] Use multiple third - edition picture segments and multiple third depth - of - focus values to train a preset model. After the training is completed, the convolutional network model is obtained.

[0084] An embodiment of the present application provides an optimization device for a layout lithography process window. A division unit is used to divide an original layout into multiple first - edition picture segments; a first optimization unit is used to optimize the process window of the first - edition picture segments using a process window optimization algorithm to obtain second - edition picture segments; the first depth - of - focus corresponding to the first - edition picture segments is less than the second depth - of - focus corresponding to the second - edition picture segments; the first depth - of - focus and the second depth - of - focus are obtained by respectively inputting the first - edition picture segments and the second - edition picture segments into a convolutional network model for prediction. By using the process window optimization algorithm, the depth - of - focus of the first - edition picture segments can be optimized, that is, the process window can be expanded, and the optimization is carried out before the mask design stage, which can maximize the process window. This solution combines the process window optimization algorithm and the convolutional network model, which can maximize the process window and improve the optimization accuracy.

[0085] On the other hand, an embodiment of the present application provides a computer device. Refer to Figure 6 As shown, it is a structural diagram of a computer device provided by an embodiment of the present application. The computer device includes a processor 310 and a memory 320:

[0086] The memory 320 is used to store program code and transmit the program code to the processor 310;

[0087] The processor 310 is used to execute the method provided in the above - mentioned embodiment according to the instructions in the program code.

[0088] This computer device may include a terminal device or a server, and the aforementioned device may be configured in this computer device.

[0089] On the other hand, an embodiment of the present application further provides a storage medium, which is used to store a computer program, and the computer program is used to execute the method provided in the above - mentioned embodiment.

[0090] Those of ordinary skill in the art can understand that all or part of the steps to implement the above - mentioned method embodiments can be completed by program - instructed hardware. The aforementioned program can be stored in a computer - readable storage medium. When the program is executed, it executes the steps including the above - mentioned method embodiments; and the aforementioned storage medium can be at least one of the following media: read - only memory (English: Read - only Memory, abbreviation: ROM), RAM, magnetic disk, or optical disk, etc., which can store program code.

[0091] Each embodiment in this specification is described in a progressive manner. For the same or similar parts among the embodiments, reference can be made to each other, and the key point of each embodiment is to illustrate the differences from other embodiments. In particular, for the apparatus embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and reference can be made to the corresponding parts of the method embodiments for relevant content.

[0092] The above are only the preferred embodiments of the present application. Although the present application has been disclosed above with preferred embodiments, it is not intended to limit the present application. Any person skilled in the art can make many possible changes and modifications to the technical solution of the present application by using the methods and technical content disclosed above, or modify it into equivalent embodiments with equivalent changes. Therefore, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present application without departing from the technical solution of the present application still fall within the scope of protection of the technical solution of the present application.

Claims

1. An optimization method for the process window of layout lithography, characterized in that, it includes: Dividing the original layout to obtain multiple first layout segments; Performing process window optimization on the first layout segments by using a process window optimization algorithm to obtain second layout segments; the first depth of focus corresponding to the first layout segments is less than the second depth of focus corresponding to the second layout segments; the first depth of focus and the second depth of focus are obtained by inputting the first layout segments and the second layout segments into a convolutional network model for prediction respectively.

2. The method according to claim 1, characterized in that, Performing process window optimization on the first layout segments by using a process window optimization algorithm to obtain second layout segments includes: When the graphic information of the first layout segments is lower than the preset graphic information, performing process window optimization on the first layout segments by using a brute-force cracking algorithm to obtain the second layout segments.

3. The method according to claim 2, characterized in that, The method further includes: When the graphic information of the first layout segments is not lower than the preset graphic information, performing process window optimization on the first layout segments by using a gradient optimization algorithm to obtain the second layout segments.

4. The method according to claim 1, characterized in that, The training process of the convolutional network model includes: Obtaining multiple third layout segments; Performing optical simulation on the multiple third layout segments to obtain multiple third depths of focus, each third depth of focus corresponding to each third layout segment; Using the multiple third layout segments and the multiple third depths of focus to train a preset model, and completing the training to obtain the convolutional network model.

5. An optimization device for the process window of layout lithography, characterized in that, it includes: A dividing unit for dividing the original layout to obtain multiple first layout segments; A first optimization unit for performing process window optimization on the first layout segments by using a process window optimization algorithm to obtain second layout segments; the first depth of focus corresponding to the first layout segments is less than the second depth of focus corresponding to the second layout segments; the first depth of focus and the second depth of focus are obtained by inputting the first layout segments and the second layout segments into a convolutional network model for prediction respectively.

6. The device according to claim 5, characterized in that, The first optimization unit is used for: When the graphic information of the first layout segments is lower than the preset graphic information, performing process window optimization on the first layout segments by using a brute-force cracking algorithm to obtain the second layout segments.

7. The device according to claim 6, characterized in that, The device further includes: A second optimization unit for performing process window optimization on the first layout segments by using a gradient optimization algorithm when the graphic information of the first layout segments is not lower than the preset graphic information to obtain the second layout segments.

8. The device according to claim 5, characterized in that, The training process of the convolutional network model includes: Obtaining multiple third layout segments; Performing optical simulation on the multiple third layout segments to obtain multiple third depths of focus, each third depth of focus corresponding to each third layout segment; The preset model is trained using the multiple third-edition picture segments and the multiple third depth of focus, and the convolutional network model is obtained after the training is completed.

9. A computer device, characterized in that the computer device includes a processor and a memory: the memory is used to store program codes and transmit the program codes to the processor; the processor is used to execute the method according to any one of claims 1-4 based on the instructions in the program codes.

10. A computer-readable storage medium, characterized in that the computer-readable storage medium is used to store a computer program, and the computer program is used to execute the method according to any one of claims 1-4.

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