A training method of a sub-resolution auxiliary pattern exposure model and a related device

Abstract

The application discloses a sub-resolution auxiliary pattern exposure model training method and related devices, applied to the technical field of auxiliary pattern exposure, comprising obtaining a printed image formed after sub-resolution auxiliary pattern exposure and extracting a printed contour; the printed contour retains a contour corresponding to the sub-resolution auxiliary pattern; the printed image is grid divided, and a grid is formed on the printed image; a correction scale is arranged in each grid; an area proportion covered by the auxiliary pattern printed contour in each grid is determined based on the correction scale; an initial model is trained with the area proportion as an input value to obtain an exposure model. By dividing the grid in the printed image, the area proportion of the auxiliary pattern printed contour in each grid is calculated, the area proportion is not binary data, and the printed degree of the sub-resolution auxiliary pattern can be accurately represented. The initial model is trained by the area proportion, so that the exposure model after training has higher accuracy.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of auxiliary pattern exposure, in particular to a sub-resolution assist feature (SRAF) exposure model training method, a SRAF correction method, a SRAF exposure model training device and a computer readable storage medium. BACKGROUND

[0002] Sub-resolution assist features (SRAF) are widely used in optical proximity correction process to improve the process window of lithography. Generally, SRAF is placed near the main pattern and its size is significantly smaller than the main pattern. Since SRAF is added to the position without pattern in the integrated circuit layout design, it cannot be printed after exposure, otherwise it will cause defects.

[0003] Generally, larger SRAF size is more effective in improving the process window of lithography, but it also has a higher risk of being printed after exposure. In order to prevent SRAF from being printed on the wafer after exposure to cause defects, an exposure model needs to be established to predict the profile of SRAF after exposure of different sizes and positions, so as to select the most appropriate size and placement position. Even if SRAF is printed after exposure, it usually does not form a complete pattern through the photoresist, so the general optical proximity correction model cannot well predict the exposure behavior of SRAF. Therefore, it is necessary to collect exposure data of SRAF of different sizes and designs to correct the special SRAF exposure model. Therefore, how to provide a training scheme of SRAF exposure model to improve the prediction accuracy of the exposure model is a problem that needs to be solved by those skilled in the art. SUMMARY

[0004] The purpose of the present application is to provide a SRAF exposure model training method which can effectively improve the prediction accuracy of the exposure model; another purpose of the present application is to provide a SRAF correction method, a SRAF exposure model training device and a computer readable storage medium which can effectively improve the prediction accuracy of the exposure model.

[0005] To solve the above technical problems, the present application provides a SRAF exposure model training method, comprising:

[0006] obtaining a printed image formed after SRAF exposure and extracting a printed profile;

[0007] dividing the printed image into grids to form a grid in the printed image; a correction ruler is arranged in each grid;

[0008] determining an area ratio of the sub-resolution assist pattern exposed profile coverage in each of the grids based on the calibration ruler;

[0009] training an initial model according to the area ratio to obtain an exposure model.

[0010] Optionally, the training of the initial model according to the area ratio to obtain the exposure model comprises:

[0011] generating assist pattern data for representing the assist pattern exposed profile topography according to the area ratio and the grids;

[0012] training an initial model according to the assist pattern data to obtain an exposure model.

[0013] Optionally, the generating of the assist pattern data for representing the assist pattern exposed profile topography according to the area ratio and the grids comprises:

[0014] filling the area ratio into the corresponding grid to form the assist pattern data.

[0015] Optionally, the training of the initial model according to the assist pattern data to obtain the exposure model comprises:

[0016] generating training samples according to the assist pattern data and topography data of the corresponding sub-resolution assist pattern; the training samples comprise a plurality of training data groups, and the training data comprises the assist pattern data and the corresponding topography data;

[0017] training an initial model according to the training samples to obtain an exposure model.

[0018] Optionally, the obtaining of the exposed image of the sub-resolution assist pattern and the extraction of the exposed profile comprise:

[0019] obtaining a scanning electron microscope (SEM) image of the sub-resolution assist pattern after exposure by SEM measurement.

[0020] Optionally, after the SEM image is obtained, the method further comprises:

[0021] performing profile extraction on the SEM image to obtain an exposure profile.

[0022] Optionally, after the exposure profile is obtained, the method further comprises:

[0023] filtering out an exposure profile of a main pattern from the exposure profile according to a mask layout to generate an assist pattern exposed profile corresponding to the sub-resolution assist pattern.

[0024] The application further provides a sub-resolution auxiliary pattern correction method, comprising:

[0025] acquiring to-be-adjusted data of a to-be-adjusted sub-resolution auxiliary pattern; the to-be-adjusted data at least represents the position and topography of the to-be-adjusted sub-resolution auxiliary pattern;

[0026] calling an exposure model to generate actual data according to the to-be-adjusted data; the exposure model is an exposure model trained by the training method of the sub-resolution auxiliary pattern exposure model according to any one of the above;

[0027] adjusting the to-be-adjusted sub-resolution auxiliary pattern according to the actual data to form an actual sub-resolution auxiliary pattern.

[0028] The application further provides a training device of a sub-resolution auxiliary pattern exposure model, comprising:

[0029] a memory for storing a computer program;

[0030] a processor for executing the computer program to realize the steps of the training method of the sub-resolution auxiliary pattern exposure model according to any one of the above and / or the steps of the sub-resolution auxiliary pattern correction method.

[0031] The application further provides a computer readable storage medium, wherein the computer readable storage medium stores a computer program, and the computer program is executed by a processor to realize the steps of the training method of the sub-resolution auxiliary pattern exposure model according to any one of the above and / or the steps of the sub-resolution auxiliary pattern correction method.

[0032] The training method of the sub-resolution auxiliary pattern exposure model provided by the application comprises the following steps: acquiring a printed image formed after sub-resolution auxiliary pattern exposure and extracting a printed contour; the printed contour retains a sub-resolution auxiliary pattern printed contour corresponding to the sub-resolution auxiliary pattern; the printed image is grid-divided to form a grid; a correction scale is arranged in each grid; an area proportion covered by the auxiliary pattern printed contour in each grid is determined based on the correction scale; and an initial model is trained according to the area proportion to obtain an exposure model.

[0033] By dividing a grid in the printed image, the area proportion of the auxiliary pattern printed contour in each grid is calculated, which can represent the topography of the auxiliary pattern printed contour and is not binary data, and can accurately represent the printing degree of the sub-resolution auxiliary pattern. The initial model is trained by taking the area proportion as an input value, and the exposure model trained by the larger data amount has higher accuracy, thereby improving the prediction accuracy of the exposure model.

[0034] The application also provides a sub-resolution auxiliary pattern correction method, a sub-resolution auxiliary pattern exposure model training device, and a computer readable storage medium, which also have the above beneficial effects and will not be described here. BRIEF DESCRIPTION OF DRAWINGS

[0035] 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 the prior art description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0036] Figure 1 A structure diagram of a sub-resolution auxiliary pattern of a two-dimensional mask pattern in the prior art;

[0037] Figure 2 A structure diagram of a sub-resolution auxiliary pattern of a two-dimensional mask pattern in the prior art; Figure 1 A schematic diagram of a pattern after printing;

[0038] Figure 3 A flowchart of a sub-resolution auxiliary pattern exposure model training method provided by an embodiment of the present application;

[0039] Figure 4 A structure diagram of a printed contour after grid division;

[0040] Figure 5 A flowchart of a specific sub-resolution auxiliary pattern exposure model training method provided by an embodiment of the present application;

[0041] Figure 6 A schematic diagram of auxiliary pattern data corresponding to Figure 4

[0042] Figure 7 A structure block diagram of a sub-resolution auxiliary pattern exposure model training device provided by an embodiment of the present application;

[0043] Figure 8 A flowchart of a sub-resolution auxiliary pattern correction method provided by an embodiment of the present application;

[0044] Figure 9 A structure block diagram of a sub-resolution auxiliary pattern correction device provided by an embodiment of the present application;

[0045] Figure 10 A structure block diagram of a sub-resolution auxiliary pattern exposure model training device provided by an embodiment of the present application. DETAILED DESCRIPTION

[0046] ​The core of the present application is to provide a training method of sub-resolution assist feature exposure model. In the prior art, generally speaking, larger sub-resolution assist feature size is more effective for improving the process window of lithography, but the risk of printing after exposure is also greater. Please refer to Figure 1 , Figure 1 is a structural diagram of sub-resolution assist feature of two-dimensional mask pattern in the prior art. Figure 1 The addition example of sub-resolution assist feature of two-dimensional mask pattern is given, in which the image in the middle is the main feature, and the features around the periphery are assist features, i.e. sub-resolution assist features. It can be seen that the sub-resolution assist features are placed near the main features, and the size is obviously smaller than the main features. Since the sub-resolution assist features are added to the positions originally without patterns in integrated circuit layout design, they cannot be printed after exposure, otherwise defects will be caused.

[0047] Please refer to Figure 2 , Figure 2 is Figure 1 the schematic diagram after printing the features. Even if the sub-resolution assist features are printed after exposure, they usually do not form complete patterns through the photoresist, so the general optical proximity correction model cannot well predict the exposure behavior of the sub-resolution assist features. Therefore, it is necessary to collect exposure data of sub-resolution assist features of different sizes and designs to correct the special sub-resolution assist feature exposure model. The input data of the existing sub-resolution assist feature exposure model is binary data (0 / 1), and generally 1 represents printing and 0 represents no printing, which greatly affects the accuracy of the model. For example, the input data of the left feature is 0, while the input data of the middle and right features is 1. However, in fact, the printing degree of the sub-resolution assist features of the middle and right features is obviously different, and different input data should be used to represent them. Figure 2

[0048] The training method of sub-resolution assist feature exposure model provided by the present application comprises: obtaining the printed image formed after exposure of the sub-resolution assist feature and extracting the printed contour; the printed contour retains the assist feature printed contour corresponding to the sub-resolution assist feature; the printed image is divided into grids, and the printed image forms a grid; a correction scale is arranged in each grid; the area ratio covered by the assist feature printed contour in each grid is determined based on the correction scale; and the initial model is trained according to the area ratio to obtain the exposure model.

[0049] ​By dividing a grid in the printed image, the area ratio of the auxiliary pattern printing contour in each grid is calculated, which can represent the topography of the auxiliary pattern printing contour, and it is not binary data, which can accurately represent the printing degree of the sub-resolution auxiliary pattern. The initial model is trained by taking the area ratio as the input value. Since a large amount of data is included, the exposure model trained has higher accuracy, and the prediction accuracy of the exposure model is improved.

[0050] In order to enable those skilled in the art to better understand the present application, the present application will be further described in detail below in conjunction with the accompanying drawings and specific embodiments. Obviously, the described embodiments are only part of the embodiments of the present application, not all. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0051] Please refer to Figure 3 And Figure 4 , Figure 3 The flowchart of a sub-resolution auxiliary pattern exposure model training method provided by an embodiment of the present application; Figure 4 The structural schematic diagram of the printed contour after grid division.

[0052] See Figure 3 In an embodiment of the present application, the sub-resolution auxiliary pattern exposure model training method comprises:

[0053] S101: Obtain the printed image formed after sub-resolution auxiliary pattern exposure and extract the printed contour.

[0054] The printed image is the image printed after the mask provided with the sub-resolution auxiliary pattern is exposed. The printed contour extracted from the printed image usually retains the auxiliary pattern printing contour after the sub-resolution auxiliary pattern exposure. Of course, the printed contour above can also not exist the contour printed by the sub-resolution auxiliary pattern, which indicates that the structure corresponding to the sub-resolution auxiliary pattern will not be printed after exposure.

[0055] S102: Grid division is performed on the printed image, and the printed image is formed into a grid.

[0056] In an embodiment of the present application, a calibration scale is provided in each of the grids. The calibration scale is usually a virtual concept or a virtual structure. The position of the calibration scale provided in the image can be used to measure the contour of the pattern.

[0057] See Figure 4In this step, the printed image is divided into grids, and a grid is set in the printed image, or the printed image is divided into multiple grids. Each grid is provided with a correction scale, that is, the contour covered by the grid can be measured. In this step, the smaller the grid division, the more accurate the data obtained by subsequent calculation, and the more sampling points.

[0058] S103: Determine the area ratio of the contour covered by the sub-resolution auxiliary pattern after exposure based on the correction scale.

[0059] In the embodiment of the present application, the contour of the printed image after exposure of the sub-resolution auxiliary pattern is referred to as the auxiliary pattern printed contour. In this step, the proportion of each grid covered by the auxiliary pattern printed contour is measured based on the correction scale to obtain the area ratio. The area ratio is obviously not binary data represented by (0, 1), but data displayed by a specific area ratio value. Generally, in the embodiment of the present application, the specific area ratio can be represented by 0 to 100. When the area ratio is 100, it indicates that the grid is filled with the auxiliary pattern printed contour, and when the area ratio is 0, it indicates that the grid does not appear the auxiliary pattern printed contour. Of course, if the area ratio accuracy can reach one decimal place, the area ratio can also be represented by 0 to 1000, and so on. The specific range of the area ratio is not limited in the embodiment of the present application, but obviously the area ratio is not binary data represented by (0, 1).

[0060] S104: Train the initial model according to the area ratio to obtain an exposure model.

[0061] In this step, the initial model is trained based on the area ratio. The specific content of the initial model can refer to the existing technology of the correction dedicated sub-resolution auxiliary pattern exposure model, which will not be described here. Since the area ratio is not binary data represented by (0, 1), it has a large amount of data, so the initial model can be trained by the area ratio, so that the exposure model obtained finally has high prediction accuracy. The specific process of training the initial model needs to be determined according to the specific structure of the initial model, which is not limited here.

[0062] The training method of the sub-resolution auxiliary pattern exposure model provided in the embodiment of the application includes the following steps.

[0063] The specific content of the training method of the sub-resolution auxiliary pattern exposure model provided in the application will be described in detail in the following embodiments of the application.

[0064] Please refer to Figure 5 and Figure 6 , Figure 5 The flowchart of the specific training method of the sub-resolution auxiliary pattern exposure model provided in the embodiment of the application is as follows. Figure 6 The auxiliary pattern data schematic diagram corresponding to Figure 4 .

[0065] Referring to Figure 5 , in the embodiment of the application, the training method of the sub-resolution auxiliary pattern exposure model includes the following steps.

[0066] S201: Obtain a scanning electron microscope image after exposure of a sub-resolution auxiliary pattern by scanning electron microscope measurement.

[0067] In this step, the sample is first exposed, which is usually provided with the above-mentioned sub-resolution auxiliary pattern and a pattern located in the middle of the sub-resolution auxiliary pattern. After exposure of the above-mentioned sample, an image can be formed. In this step, the above-mentioned image is scanned by a scanning electron microscope, so as to obtain a scanning electron microscope image. The process of obtaining the scanning electron microscope image can refer to the prior art, which will not be described here.

[0068] S202: Profile extraction is performed on the scanning electron microscope image to obtain an exposure profile.

[0069] In this step, profile extraction is performed on the scanning electron microscope image to obtain an exposure profile. The profile extraction process can be extraction by binarization, or extraction of the profile in the above-mentioned scanning electron microscope image by a pre-trained profile extraction model or an image recognition model, to obtain an exposure profile.

[0070] S203: According to the mask layout, filter out the exposure profile corresponding to the main pattern from the exposure profile to generate an auxiliary pattern printing profile corresponding to the sub-resolution auxiliary pattern.

[0071] The mask layout has main patterns in the middle of the sub-resolution auxiliary patterns, and the sub-resolution auxiliary patterns usually correspond to the main patterns and are mainly used to adjust the photolithography process window. However, the exposure image not only has the auxiliary pattern exposure profile corresponding to the sub-resolution auxiliary patterns, but also has the main pattern exposure profile corresponding to the main patterns. In this step, the main pattern exposure profile in the exposure image is removed by referring to the mask layout, and an image only retaining the auxiliary pattern exposure profile is obtained.

[0072] S204: grid division is performed on the printed image, and a grid is formed in the printed image.

[0073] S205: based on the correction ruler, the area ratio of the auxiliary pattern exposure profile covered by the sub-resolution auxiliary pattern in each grid after exposure is determined.

[0074] The S204 to S205 described above are basically the same as the S102 to S103 in the above-mentioned embodiment of the application, and the details are described in the above-mentioned embodiment of the application, which will not be repeated here.

[0075] S206: according to the area ratio and the grid, auxiliary pattern data for representing the topography of the auxiliary pattern exposure profile is generated.

[0076] Since the area ratio is the area ratio of each grid covered by the auxiliary pattern exposure profile, the area ratio corresponds to the grid one by one. In this step, grid data or matrix data can be formed by combining the grid and the area ratio, and the grid data or the matrix data can represent the topography of the auxiliary pattern exposure profile. Specifically, it can at least represent the length of the auxiliary pattern exposure profile. The grid data or the matrix data in this step is the auxiliary pattern data, and accordingly, the auxiliary pattern data can be generated according to the area ratio and the grid in this step.

[0077] Specifically, this step can include: filling the area ratio into the corresponding grid to form the auxiliary pattern data. After filling the area ratio into the corresponding grid, the auxiliary pattern data as shown in FIG. 8 can be formed, which can represent the length of the auxiliary pattern exposure profile in each direction, so that a model with high precision can be trained according to the auxiliary pattern data in the subsequent step. Figure 6

[0078] S207: training the initial model according to the auxiliary pattern data to obtain an exposure model.

[0079] ​In this step, the initial model is trained based on the above-mentioned auxiliary pattern data to obtain an exposure model with high accuracy. Specifically, the step can specifically include: generating a training sample according to the auxiliary pattern data and the topography data corresponding to the sub-resolution auxiliary pattern; the training sample includes multiple sets of training data, and the training data includes the auxiliary pattern data and the corresponding topography data; training the initial model according to the training sample to obtain an exposure model.

[0080] That is, before the above-mentioned auxiliary pattern data is input into the initial model, a training sample is first generated based on the auxiliary pattern data. The training sample specifically includes multiple sets of training data, and each set of training data includes the above-mentioned auxiliary pattern data and the topography data corresponding to the sub-resolution auxiliary pattern forming the auxiliary pattern data. At this time, the training data is equivalent to including the corresponding relationship between the auxiliary pattern data and the topography data of the sub-resolution auxiliary pattern. Training the initial model based on the training sample can further improve the accuracy of the exposure model.

[0081] The training method of the sub-resolution auxiliary pattern exposure model provided by the embodiment of the application can automatically generate quantized input data for modeling of the sub-resolution auxiliary pattern exposure model, and well reflects different printing degrees of the sub-resolution auxiliary pattern. Compared with the currently used 1 / 0 binary input data, the accuracy of the model can be greatly improved.

[0082] Next, a training device of a sub-resolution auxiliary pattern exposure model provided by an embodiment of the application will be described. The training device of the sub-resolution auxiliary pattern exposure model described below can be correspondingly referred to the training method of the sub-resolution auxiliary pattern exposure model described above.

[0083] Please refer to Figure 7 , Figure 7 The structure block diagram of the training device of the sub-resolution auxiliary pattern exposure model provided by the embodiment of the application is shown in the following figure.

[0084] Please refer to Figure 7 In the embodiment of the application, the training device of the sub-resolution auxiliary pattern exposure model can include:

[0085] The acquisition module 100 is configured to acquire a printed image formed after the sub-resolution auxiliary pattern is exposed and extract a printed contour.

[0086] The grid division module 200 is configured to divide the printed image into grids, and a calibration scale is arranged in each grid.

[0087] The area ratio module 300 is configured to determine an area ratio of the auxiliary pattern printing profile coverage of the sub-resolution auxiliary pattern after exposure based on the correction ruler.

[0088] The training module 400 is configured to train an initial model according to the area ratio to obtain an exposure model.

[0089] Preferably, in the embodiment of the present application, the training module 400 comprises:

[0090] The pattern data unit is configured to generate auxiliary pattern data for representing the auxiliary pattern printing profile topography according to the area ratio and the grid.

[0091] The training unit is configured to train an initial model according to the auxiliary pattern data to obtain an exposure model.

[0092] Preferably, in the embodiment of the present application, the pattern data unit is configured to:

[0093] The area ratio is filled into the corresponding grid to form the auxiliary pattern data.

[0094] Preferably, in the embodiment of the present application, the training unit comprises:

[0095] The training sample sub-unit is configured to generate training samples according to the auxiliary pattern data and topography data of the corresponding sub-resolution auxiliary pattern; the training samples comprise a plurality of groups of training data, and the training data comprises the auxiliary pattern data and the corresponding topography data.

[0096] The training sub-unit is configured to train an initial model according to the training samples to obtain an exposure model.

[0097] Preferably, in the embodiment of the present application, the acquisition module 100 comprises:

[0098] The scanning unit is configured to obtain a scanning electron microscope image of the sub-resolution auxiliary pattern after exposure by a scanning electron microscope measurement.

[0099] Preferably, in the embodiment of the present application, the acquisition module 100 further comprises:

[0100] The profile extraction unit is configured to perform profile extraction on the scanning electron microscope image to obtain an exposure profile.

[0101] Preferably, in the embodiment of the present application, the acquisition module 100 further comprises:

[0102] a filtering unit configured to filter out, from the exposure profile, an exposure profile corresponding to a main pattern according to a mask layout, to generate an auxiliary pattern print profile corresponding to the sub-resolution auxiliary pattern.

[0103] The training device of the sub-resolution auxiliary pattern exposure model of the embodiment is used to implement the foregoing training method of the sub-resolution auxiliary pattern exposure model, and thus the specific embodiments in the training device of the sub-resolution auxiliary pattern exposure model can be seen from the foregoing embodiment part of the training method of the sub-resolution auxiliary pattern exposure model. For example, the acquisition module 100, the grid division module 200, the area proportion module 300, and the training module 400 are respectively used to implement steps S101 to S104 in the foregoing training method of the sub-resolution auxiliary pattern exposure model, and thus the specific embodiments can be referred to the description of the corresponding respective part embodiments, which will not be described herein again.

[0104] The following describes a sub-resolution auxiliary pattern correction method provided by the embodiment of the application, which can be correspondingly referred to the foregoing training method of the sub-resolution auxiliary pattern exposure model.

[0105] Please refer to Figure 8 , Figure 8 a flowchart of the sub-resolution auxiliary pattern correction method provided by the embodiment of the application.

[0106] Please refer to Figure 8 In the embodiment of the application, the sub-resolution auxiliary pattern correction method comprises:

[0107] S301: acquiring adjustment data of a sub-resolution auxiliary pattern to be adjusted.

[0108] In the embodiment of the application, the adjustment data at least represents a position and a topography of the sub-resolution auxiliary pattern to be adjusted. The foregoing adjustment data can generally represent the position and the topography of the sub-resolution auxiliary pattern before adjustment, such as length, width, and the like, so that the position and the topography of the sub-resolution auxiliary pattern can be adjusted in the subsequent steps.

[0109] S302: calling an exposure model to generate actual data according to the adjustment data.

[0110] In the embodiment of the application, the exposure model is an exposure model trained by the training method of the sub-resolution auxiliary pattern exposure model according to any one of the foregoing embodiments. The specific content of the exposure model has been described in detail in the foregoing embodiments of the application, which will not be described herein again.

[0111] In this step, through the exposure model, actual data can be generated based on the above-mentioned to-be-adjusted data, which generally represents the position and topography of the adjusted sub-resolution auxiliary pattern.

[0112] S303: Adjusting the to-be-adjusted sub-resolution auxiliary pattern according to the actual data to form an actual sub-resolution auxiliary pattern.

[0113] In this step, the to-be-adjusted sub-resolution auxiliary pattern is adjusted according to the actual data to form an adjusted actual sub-resolution auxiliary pattern, and generally no image is printed out when the actual sub-resolution auxiliary pattern is exposed.

[0114] The sub-resolution auxiliary pattern correction method provided by the embodiment of the application can make the trained exposure model have higher accuracy in the model training process, improve the prediction accuracy of the exposure model, and accordingly ensure that the adjusted sub-resolution auxiliary pattern will not print out the exposure image after exposure in the embodiment of the application.

[0115] The sub-resolution auxiliary pattern correction device provided by the embodiment of the application is described below, and the sub-resolution auxiliary pattern correction device described below can be referred to in the corresponding manner with the sub-resolution auxiliary pattern correction method described above.

[0116] Please refer to Figure 9 , Figure 9 The structure block diagram of the sub-resolution auxiliary pattern correction device provided by the embodiment of the application.

[0117] See Figure 9 In the embodiment of the application, the sub-resolution auxiliary pattern correction device can include:

[0118] The to-be-adjusted data module 500 is configured to obtain to-be-adjusted data of a to-be-adjusted sub-resolution auxiliary pattern, and the to-be-adjusted data at least represents the position and topography of the to-be-adjusted sub-resolution auxiliary pattern.

[0119] The exposure model calling module 600 is configured to call an exposure model and generate actual data according to the to-be-adjusted data, and the exposure model is an exposure model trained according to the training method of the sub-resolution auxiliary pattern exposure model in any of the above embodiments.

[0120] The adjustment module 700 is configured to adjust the to-be-adjusted sub-resolution auxiliary pattern according to the actual data to form an actual sub-resolution auxiliary pattern.

[0121] The sub-resolution auxiliary pattern correction device of the embodiment is used to implement the foregoing sub-resolution auxiliary pattern correction method, and therefore the specific implementation of the sub-resolution auxiliary pattern correction device can be seen from the foregoing embodiment part of the sub-resolution auxiliary pattern correction method. For example, the to-be-adjusted data module 500, the exposure model calling module 600, and the adjustment module 700 are respectively used to implement steps S301 to S303 in the foregoing sub-resolution auxiliary pattern correction method, and therefore the specific implementation can be referred to the description of the corresponding embodiment part, which will not be described herein again.

[0122] A sub-resolution auxiliary pattern exposure model training device provided by an embodiment of the application is introduced below. The sub-resolution auxiliary pattern exposure model training device described below can be correspondingly referred to the foregoing sub-resolution auxiliary pattern exposure model training method and the sub-resolution auxiliary pattern exposure model training device.

[0123] Please refer to Figure 10 , Fig. 01 is a structure block diagram of a sub-resolution auxiliary pattern exposure model training device provided by an embodiment of the application.

[0124] Refer to Figure 10 The sub-resolution auxiliary pattern exposure model training device can include a processor 11 and a memory 12.

[0125] The memory 12 is used to store a computer program, and the processor 11 is used to execute the computer program to implement the foregoing sub-resolution auxiliary pattern exposure model training method in the embodiment of the application and / or the steps of the foregoing sub-resolution auxiliary pattern correction method in the embodiment of the application.

[0126] The processor 11 in the sub-resolution auxiliary pattern exposure model training device of the embodiment is used to install the foregoing sub-resolution auxiliary pattern exposure model training device in the embodiment of the application, and the processor 11 in combination with the memory 12 can implement the foregoing sub-resolution auxiliary pattern exposure model training method in any embodiment of the application and / or the foregoing sub-resolution auxiliary pattern correction method in the embodiment of the application. Therefore, the specific implementation of the sub-resolution auxiliary pattern exposure model training device can be seen from the foregoing embodiment part of the sub-resolution auxiliary pattern exposure model training method and the sub-resolution auxiliary pattern correction method, and the specific implementation can be referred to the description of the corresponding embodiment part, which will not be described herein again.

[0127] The application further provides a computer readable storage medium, wherein a computer program is stored on the computer readable storage medium, and the computer program, when executed by a processor, implements the training method of the sub-resolution auxiliary pattern exposure model and / or the sub-resolution auxiliary pattern correction method described in any of the application embodiments. The remaining content can be referred to the prior art, and will not be described here.

[0128] The various embodiments are described in a progressive manner in the specification, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the various embodiments can be referred to each other. For the device disclosed in the embodiments, since it corresponds to the method disclosed in the embodiments, the description is relatively simple, and the relevant parts can be referred to the method part.

[0129] The skilled person can further realize that the units and algorithm steps of the examples described in combination with the embodiments disclosed herein can be realized by electronic hardware, computer software or a combination of both. In order to clearly illustrate the interchangeability of hardware and software, the components and steps of the examples have been described in a general manner in the above description. Whether the functions are realized in hardware or software depends on the specific application and design constraints of the technical solution. The skilled person can use different methods to realize the described functions for each specific application, but such implementation should not be considered beyond the scope of the application.

[0130] The steps of the method or algorithm described in combination with the embodiments disclosed herein can be directly implemented by hardware, a software module executed by a processor, or a combination of both. The software module can be placed in a random access memory (RAM), a memory, a read-only memory (ROM), an electrically programmable ROM, an electrically erasable programmable ROM, a register, a hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.

[0131] Finally, it should be noted that, in this document, relational terms such as first and second, and the like, are used solely to distinguish one entity or action from another entity or action, without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises", "comprising", or any other variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but can include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element preceded by "comprises a" does not, without more constraints, foreclose the existence of additional identical elements in the process, method, article, or apparatus that comprises the recited element.

[0132] The above describes in detail the training method of a sub-resolution auxiliary pattern exposure model and the related device provided by the present application. The principles and implementation manners of the present application are described by using specific examples in this paper, and the above description of the examples is only used to help understand the method of the present application and its core idea. It should be pointed out that, for those skilled in the art, some improvements and modifications can be made to the present application without departing from the principles of the present application, and these improvements and modifications also fall within the protection scope of the claims of the present application.

Claims

1. A training method for a sub-resolution assisted image exposure model, characterized in that, The method comprises: obtaining a printed image formed after exposure of a sub-resolution auxiliary pattern and extracting a printed profile; dividing the printed image into grids, and forming a grid in the printed image; a correction scale is arranged in each grid; determining the area ratio of the printed profile of the sub-resolution auxiliary pattern after exposure of the sub-resolution auxiliary pattern in each grid based on the correction scale; training an initial model according to the area ratio to obtain an exposure model; the training of the initial model according to the area ratio to obtain the exposure model comprises: generating auxiliary pattern data for representing the profile topography of the printed profile of the auxiliary pattern according to the area ratio and the grid; training an initial model according to the auxiliary pattern data to obtain an exposure model; generating auxiliary pattern data for representing the profile topography of the printed profile of the auxiliary pattern according to the area ratio and the grid comprises: filling the area ratio into the corresponding grid to form the auxiliary pattern data; the auxiliary pattern data represents the length of the printed profile of the auxiliary pattern in each direction.

2. The method of claim 1, wherein, the training of the initial model according to the auxiliary pattern data to obtain the exposure model comprises: generating training samples according to the auxiliary pattern data and the topography data of the corresponding sub-resolution auxiliary pattern; the training samples comprise a plurality of training data, and the training data comprise the auxiliary pattern data and the corresponding topography data; training an initial model according to the training samples to obtain an exposure model.

3. The method of claim 1, wherein, the method of obtaining a printed image formed after exposure of a sub-resolution auxiliary pattern and extracting a printed profile comprises: obtaining a scanning electron microscope image after exposure of a sub-resolution auxiliary pattern by scanning electron microscope measurement.

4. The method of claim 3, wherein, after obtaining the scanning electron microscope image, the method further comprises: extracting a profile from the scanning electron microscope image to obtain an exposure profile.

5. The method of claim 4, wherein, after obtaining the exposure profile, the method further comprises: filtering out the exposure profile corresponding to the main pattern from the exposure profile according to the mask layout to generate a printed profile of the auxiliary pattern corresponding to the sub-resolution auxiliary pattern.

6. A sub-resolution assist pattern correction method, characterized by, The method comprises: obtaining adjustment data of a sub-resolution auxiliary pattern to be adjusted; the adjustment data at least represents the position and topography of the sub-resolution auxiliary pattern to be adjusted; calling an exposure model to generate actual data according to the adjustment data; the exposure model is an exposure model trained according to the training method of the sub-resolution auxiliary pattern exposure model of any one of claims 1 to 5; adjusting the sub-resolution auxiliary pattern to be adjusted according to the actual data to form an actual sub-resolution auxiliary pattern.

7. A training device of a sub-resolution assist pattern exposure model, comprising: The device comprises: a memory for storing a computer program; a processor for executing the computer program to realize the steps of the training method of the sub-resolution auxiliary pattern exposure model of any one of claims 1 to 5, and / or the steps of the sub-resolution auxiliary pattern correction method of claim 6.

8. A computer-readable storage medium, characterized in that, The computer readable storage medium stores a computer program, and the computer program is executed by the processor to implement the steps of the training method of the sub-resolution assisted pattern exposure model according to any one of claims 1 to 5, and / or the steps of the sub-resolution assisted pattern correction method according to claim 6.

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