Photoresist model training method and device, equipment and computer storage medium

Abstract

The application discloses a photoresist model training method, device and equipment and a computer storage medium. The method comprises the following steps: acquiring a photoetching design layout, a mask layout and a photoetching process parameter information set; constructing an initial photoresist model according to a first parameter; performing first training on the initial photoresist model for a preset number of iterations to obtain a first photoresist model; screening an index value of a graphic index in a last iteration process from a training log; in a case where the index value of the graphic index is greater than a preset threshold, updating the parameter in the initial photoresist model to a second parameter to obtain an updated initial photoresist model, and returning to performing the first training on the initial photoresist model for the preset number of iterations according to the mask layout and the photoetching design layout to obtain the first photoresist model until the index value is less than or equal to the preset threshold, and obtaining a trained photoresist model. According to the embodiment of the application, the efficiency of photoresist model training can be improved.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of semiconductor manufacturing, and particularly relates to a photoresist model training method and device, equipment and a computer storage medium. BACKGROUND

[0002] Computational lithography is an advanced technology that uses computer simulation and optimization of lithography processes by establishing precise mathematical models to simulate optical, chemical and physical phenomena during the lithography process.

[0003] Computational lithography can include various models, such as optical models and chemical models. The optical model can be used to describe the process of light from the light source, passing through the mask, projection lens and other optical elements, and forming an image on the wafer surface. The chemical model can be used to describe the photochemical reaction process after the light is projected onto the photoresist.

[0004] When training the model in computational lithography according to the preset number of iterations, if the trained model does not meet the expected expectation, it is necessary to backtrack to find the problem, and after adjusting the corresponding parameters, the model is retrained. The number of iterations of model training is generally a large number, therefore, if the parameters are adjusted multiple times and the model is trained with the preset number of iterations each time, it will cause a waste of a large amount of time and resources. SUMMARY

[0005] The embodiments of the present application provide a photoresist model training method, device, equipment and computer storage medium, which realizes fast training of the photoresist model through fewer iterations, and determines the influence of the parameters in the model on the lithography pattern according to the index value of the pattern index in the training log, thereby quickly excluding the model parameters that have no optimization effect on the lithography pattern, and further improving the efficiency of the photoresist model training.

[0006] In a first aspect, the embodiments of the present application provide a photoresist model training method, comprising: obtaining a lithography design layout, a mask layout and a set of lithography process parameter information affecting the lithography design layout; constructing an initial photoresist model according to a first parameter in the set; performing a first training of the initial photoresist model according to the mask layout and the lithography design layout for a preset number of iterations to obtain a first photoresist model; screening an index value of a pattern index in the last iteration process from a training log of the first training; in a case where the index value of the pattern index is greater than a preset threshold, updating the parameters in the initial photoresist model to a second parameter according to the set to obtain an updated initial photoresist model, and returning to performing the first training of the initial photoresist model according to the mask layout and the lithography design layout for the preset number of iterations to obtain the first photoresist model until the index value is less than or equal to the preset threshold, obtaining a trained photoresist model; the first parameter and the second parameter are not completely the same.

[0007] In an embodiment, the first training of the initial photoresist model according to the mask layout and the lithography design layout for a preset number of iterations comprises: predicting a lithography pattern of the mask layout according to the initial photoresist model to obtain a predicted layout; determining an index value of a pattern index between the predicted layout and the lithography design layout, and storing the index value in a training log; in a case where the number of iterations is determined to be less than the preset number of iterations, adjusting parameters in the initial photoresist model to obtain an adjusted initial photoresist model, and accumulating the number of iterations, returning to input the aerial image into the initial photoresist model to obtain the predicted layout, until the number of iterations reaches the preset number of iterations, and obtaining the first photoresist model.

[0008] In an embodiment, the predicting of the lithography pattern of the mask layout according to the initial photoresist model comprises: determining a cross transmission matrix according to optical process parameter information in the set of lithography process parameter information; calculating a spatial image corresponding to the mask layout according to the mask layout and the cross transmission matrix; and inputting the spatial image into the initial photoresist model to obtain the predicted layout.

[0009] In an embodiment, the pattern index comprises at least one of: a mean square error between the predicted layout and the lithography design layout; and an overrule amount of the predicted layout compared with the lithography design layout.

[0010] In an embodiment, the preset number of iterations is less than a preset number.

[0011] In a second aspect, an embodiment of the present application provides a training device of a photoresist model, the device comprising: an acquisition module configured to acquire a lithography design layout, a mask layout, and a set of lithography process parameter information affecting the lithography design layout; a construction module configured to construct an initial photoresist model according to a first parameter in the set; a training module configured to perform first training of the initial photoresist model according to the mask layout and the lithography design layout for a preset number of iterations to obtain a first photoresist model; a screening module configured to screen an index value of a pattern index in a last iteration process from a training log of the first training; and a loop module configured to, in a case where the index value of the pattern index is determined to be greater than a preset threshold, update parameters in the initial photoresist model to a second parameter according to the set to obtain an updated initial photoresist model, and return to perform the first training of the initial photoresist model according to the mask layout and the lithography design layout for the preset number of iterations to obtain the first photoresist model, until the index value meets the preset threshold, and obtain a trained photoresist model; and the first parameter and the second parameter are not completely the same.

[0012] In a third aspect, the embodiments of the present application provide a training method and device for a photoresist model. The device comprises a processor and a memory storing computer program instructions; and the processor implements the training method for the photoresist model in the first aspect or any one of the embodiments of the first aspect when executing the computer program instructions.

[0013] In a fourth aspect, a computer-readable storage medium stores computer program instructions. The computer program instructions are executed by a processor to implement the training method for the photoresist model in the first aspect or any one of the embodiments of the first aspect.

[0014] In a fifth aspect, the embodiments of the present application provide a computer program product. Instructions in the computer program product are executed by a processor of an electronic device to cause the electronic device to perform the training method for the photoresist model in the first aspect or any one of the embodiments of the first aspect.

[0015] The training method, device, equipment and computer storage medium for the photoresist model provided by the embodiments of the present application can obtain a set of lithography process parameter information, and construct an initial photoresist model based on a first parameter in the set of lithography process parameter information. Further, the initial photoresist model is trained for a preset number of iterations by using a mask layout and a lithography design layout to obtain a first photoresist model. The preset number of iterations can be a small number of iterations, and the training of the initial photoresist model for the preset number of iterations can make the initial photoresist model reflect the influence of the first parameter on the lithography pattern. The influence of the first parameter in the first photoresist model on the lithography pattern can be determined by obtaining the index value of the pattern index in the training log. In the case where it is determined that there is no optimization effect on the lithography pattern by adjusting the first parameter, i.e., in the case where the index value of the pattern index is greater than a preset threshold, the first parameter in the initial photoresist model is adjusted to a second parameter, and the initial photoresist model after the adjustment of the parameter is retrained to determine the influence of the second parameter on the photoresist pattern. It can be understood that, in the embodiments of the present application, the photoresist model is quickly trained by a small number of iterations, and the influence of the parameter in the model on the lithography pattern is determined according to the index value of the pattern index in the training log, so as to quickly exclude the model parameters that have no optimization effect on the lithography pattern, and thus the efficiency of the training of the photoresist model is improved. BRIEF DESCRIPTION OF DRAWINGS

[0016] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following will briefly introduce the drawings needed to be used in the embodiments of the present application. For those skilled in the art, other drawings can also be obtained without creative labor on the basis of these drawings.

[0017] Figure 1A flowchart of a training method of a photoresist model is shown;

[0018] Figure 2 A flowchart of S130 is shown;

[0019] Figure 3 A flowchart of S131 is shown;

[0020] Figure 4 A structural diagram of a training device of a photoresist model is shown;

[0021] Figure 5 A hardware structural diagram of a training method of a photoresist model is shown. DETAILED DESCRIPTION

[0022] The features and exemplary embodiments of various aspects of the present application will be described below in detail, in order to make the purposes, technical solutions and advantages of the present application more clear and apparent, the present application will be further described in detail below in combination with the drawings and specific embodiments. It should be understood that the specific embodiments described herein are only intended to explain the present application, but not to limit the present application. The present application can be implemented without some of these specific details by those skilled in the art. The following description of the embodiments is only to provide a better understanding of the present application by showing examples of the present application.

[0023] It should be noted that in this paper, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply that there is any such actual relationship or order between these entities or operations. Moreover, the terms "include", "contain" or any other variants thereof are intended to cover non-exclusive inclusion, so that the process, method, article or device including a series of elements not only includes those elements, but also includes other elements not explicitly listed or inherent to such process, method, article or device. Without more limitations, the elements defined by the statement "include" do not exclude the presence of other identical elements in the process, method, article or device including the elements.

[0024] Computational lithography is an advanced technology that simulates the optical, chemical and physical phenomena in the lithography process by establishing an accurate mathematical model based on the theoretical basis of optical imaging theory, photochemical theory and thermal diffusion theory.

[0025] The calculation of lithography can include an optical model and a chemical model. Through training of the optical model and the chemical model, simulation of the process of diffraction of light irradiated on a mask and finally imaging and the process of chemical reaction of light projected on photoresist is realized.

[0026] However, when training the model, the number of iterations is not easy to grasp, and too many or too few iterations can lead to an unsatisfactory model. Moreover, if the trained model is unsatisfactory, it is necessary to backtrack to find the problem and make corresponding modifications. The adjusted model still needs to be trained according to the set number of iterations. For example, if it is determined that the reason for the unsatisfactory training model is that the parameters in the model are not reasonably selected, for example, a series of parameter configurations in the photoresist kernel function in the photoresist model are not reasonable, and when adjusting the parameters, multiple adjustments may be needed, thus, multiple repeated training is needed in the training process, and each training is training with a preset number of iterations, which leads to a waste of a large amount of time and resources in the model training process.

[0027] To solve the problems in the prior art, the embodiments of the present application provide a photoresist model training method, device, equipment and computer storage medium. First, the photoresist model training method provided by the embodiments of the present application is introduced.

[0028] Figure 1 A flowchart of the photoresist model training method provided by an embodiment of the present application is shown. As shown in Figure 1 The photoresist model training method includes the following steps:

[0029] S110, obtaining a lithography design layout, a mask layout and a set of lithography process parameter information affecting the lithography design layout.

[0030] Illustratively, the lithography design layout describes the logical functions and electrical connection relationships of various electronic components in the chip, thereby realizing various performances of the chip.

[0031] Illustratively, the lithography process parameter information affecting the lithography design layout includes corresponding parameters of the chemical behavior of light exposure in photoresist, for example, the chemical behavior of light exposure in photoresist can include the chemical reaction, diffusion and dissolution of photoresist, and the corresponding parameters in this process can include the acidity, alkalinity and mechanical stress of photoresist.

[0032] It can be understood that different lithography process parameter information causes different chemical reactions of light in photoresist, thereby affecting the shape of the pattern in the patterned layer on the wafer surface.

[0033] Exemplarily, the mask layout can be used to control the projection area of the light, so as to control the area where the light is irradiated on the photoresist.

[0034] Exemplarily, the set of lithography process parameter information can further include optical process parameter information. For example, the optical process parameter information can be the light intensity, wavelength, etc. of the light source.

[0035] S120, constructing an initial photoresist model according to the first parameter in the set.

[0036] Exemplarily, the construction of the initial photoresist model can be realized by the first parameter in the set of lithography process parameter information. The first parameter can be obtained by any combination of multiple parameters in the set of lithography process parameter information.

[0037] Exemplarily, any parameter in the set of lithography process parameter information can be represented by constructing a corresponding mathematical function. Therefore, in the photoresist model, the chemical behavior of the light in the actual exposure in the photoresist can be simulated by constructing a mathematical function corresponding to each parameter in the first parameter. For example, the acidity in the photoresist model can be represented by a Gaussian function.

[0038] Exemplarily, the architecture of the mathematical function corresponding to different parameters can be different, and for different parameter values of the same parameter, the corresponding data in the mathematical function can be adjusted, for example, the weight in the mathematical function can be adjusted to represent different parameter values of the parameter.

[0039] Exemplarily, the mathematical function corresponding to the first parameter can be collectively referred to as a photoresist kernel function. It can be understood that different first parameters correspond to different photoresist kernel functions, and similarly, different first parameters also describe different chemical processes of light exposure in the photoresist.

[0040] S130, according to the mask layout and the lithography design layout, the initial photoresist model is trained for a preset number of iterations to obtain a first photoresist model.

[0041] Exemplarily, the mask layout and the lithography design layout can be input into the initial photoresist model to train the initial photoresist model until the number of iterations meets the preset iteration condition to obtain the first photoresist model. The first photoresist model can reflect the influence of the first parameter on the lithography pattern.

[0042] In some optional embodiments, the preset number of iterations is less than the preset number. The preset number can be pre-set by the relevant technical personnel according to experience. And the preset number is generally a small value. For example, the relevant technical personnel can set the preset number to 5 times, or can set the preset number to 10 times.

[0043] S140, filtering the index value of the pattern index in the last iteration process from the training log of the first training.

[0044] Exemplarily, it can be understood that the first training process includes a preset number of iterations of training processes. And the data in each training process is stored in the training log. That is, the training log of the first training can store important data in each iteration training and training process in the first training process. And the last training process can be determined by filtering the training log of the first training, and the important data in the last training process is filtered to filter out the index value of the pattern index.

[0045] Exemplarily, the pattern index can represent the influence of the first parameter in the initial photoresist model on the lithography pattern.

[0046] In some optional embodiments, the pattern index can include root mean square (RMS) and / or out of spec number (OOS). The influence of the first parameter in the initial photoresist model on the lithography pattern is represented by the root mean square and / or the out of spec number. It can be understood that if the root mean square value and the out of spec number are small, it can be determined that a better lithography pattern can be obtained by adjusting the parameter value of the first parameter in the initial photoresist model, that is, the first parameter has a beneficial effect on the lithography pattern.

[0047] S150, determining whether the index value of the pattern index is greater than a preset threshold value, if yes, performing step S160; if no, the training is ended, and a trained photoresist model is obtained.

[0048] S160, updating the parameter in the initial photoresist model to a second parameter according to the set to obtain an updated initial photoresist model, wherein the first parameter and the second parameter are not completely the same.

[0049] Exemplarily, it can be determined whether the index value of the pattern index is greater than a preset threshold value. If it is determined that the index value of the pattern index is greater than the preset threshold value, the parameter in the initial photoresist model is updated to a second parameter according to the set to obtain an updated initial photoresist model, and the first training of the initial photoresist model according to the mask layout and the lithography design layout is returned. A preset number of iterations is performed to obtain a first photoresist model, that is, the photoresist model after adjusting the parameter is trained again until the index value of the pattern index is less than or equal to the preset threshold value. If it is determined that the index value of the pattern index is less than or equal to the preset threshold value, it is determined that the training is ended, and a trained photoresist model is obtained.

[0050] For example, updating the parameters in the initial photoresist model to the second parameters according to the set can be selecting multiple parameters different from the first parameters in the set as the second parameters of the updated initial photoresist model. Alternatively, the second parameters can be determined by analyzing the data in the training log of the first training, thereby determining the parameters in the first parameters that have no effect or adverse effect on the photoresist pattern, and removing these parameters from the first parameters. In an example, the data in the training log of the first training can be analyzed by the point-symmetry analysis method, thereby determining the parameters in the first parameters that have no effect or adverse effect on the photoresist pattern.

[0051] In an example, the first parameters in the initial photoresist model include the acidity, alkalinity and mechanical stress of the photoresist, and after the first training, the first photoresist model is obtained, at this time, the parameter value corresponding to the acidity in the first photoresist model can be m, the parameter value corresponding to the alkalinity can be n, and the parameter value corresponding to the mechanical stress can be p. Moreover, the index value of the pattern index of the first photoresist model is greater than the preset threshold, and it is determined by analysis that setting the alkalinity in the photoresist has an adverse effect on the formation of the photoresist pattern. Therefore, the alkalinity is removed from the model parameters, and the updated second parameters are the acidity and the mechanical stress of the photoresist. Further, the photoresist model after the parameter modification is retrained.

[0052] In the embodiments of the present application, the set of photoresist process parameter information is obtained, and the initial photoresist model is constructed based on the first parameters in the set of photoresist process parameter information. Further, the initial photoresist model is trained for a preset number of iterations using the mask layout and the photoresist design layout to obtain the first photoresist model. The preset number of iterations can be a small number of iterations, and the training of the preset number of iterations enables the initial photoresist model to reflect the effect of the first parameters on the photoresist pattern. The effect of the first parameters in the first photoresist model on the photoresist pattern can be determined by obtaining the index value of the pattern index in the training log. In the case where it is determined that there is no optimization effect on the photoresist pattern by adjusting the first parameters, i.e., in the case where the index value of the pattern index is greater than the preset threshold, the first parameters in the initial photoresist model are adjusted to the second parameters, and the initial photoresist model after the parameter adjustment is retrained to determine the effect of the second parameters on the photoresist pattern. It can be understood that in the embodiments of the present application, the photoresist model is quickly trained by a small number of iterations, and the effect of the parameters in the model on the photoresist pattern is determined according to the index value of the pattern index in the training log, thereby quickly excluding the model parameters that have no optimization effect on the photoresist pattern, and further improving the efficiency of the photoresist model training.

[0053] To achieve the training of the initial photoresist model, as another implementation manner of the present application, the present application further provides another implementation manner of S130, which is specifically described in the following embodiment.

[0054] Figure 2 A flowchart of S130 provided by one embodiment of the present application is shown. As shown in the figure, Figure 2 S130 includes the following steps:

[0055] S131, predicting the photoetching pattern of the mask layout according to the initial photoresist model to obtain a predicted layout.

[0056] Illustratively, the predicted layout can represent the layout that can be obtained under the photoetching process condition corresponding to the first parameter. The mask layout can include multiple photoetching patterns. Further, the prediction of the photoetching pattern morphology of the mask layout is realized by the initial photoresist model, thereby obtaining the predicted layout.

[0057] S132, determining the index value of the pattern index between the predicted layout and the photoetching design layout, and storing the index value into the training log.

[0058] Illustratively, the index value of the pattern index between the predicted layout and the photoetching design layout can be determined, and the index value can be stored into the training log.

[0059] Illustratively, the pattern index between the predicted layout and the photoetching design layout can include the mean square error between the predicted layout and the photoetching design layout, and / or the over-specification amount of the predicted layout compared with the photoetching design layout. The mean square error is used to measure the difference degree of the predicted layout and the photoetching design layout at the critical dimension, and the over-specification amount focuses on whether these differences exceed the preset specification range.

[0060] Illustratively, the mean square error and / or the over-specification amount corresponding to each iteration training can be stored into the training log.

[0061] S133, judging whether the iteration number reaches the preset iteration number, if yes, ending the training to obtain the first photoresist model; if no, executing step S134.

[0062] S134, adjusting the parameters in the initial photoresist model to obtain the adjusted initial photoresist model, and accumulating the iteration number.

[0063] Exemplarily, for the initial photoresist model, the number of iterations can be taken as a constraint condition thereof. When it is determined that the number of training times of the initial photoresist model reaches the preset number of iterations, it is determined that the training of the initial photoresist model is completed, and the first photoresist model is obtained. If it is determined that the number of training times of the initial photoresist model does not reach the preset number of iterations, the parameters in the initial photoresist model need to be adjusted to obtain an adjusted initial photoresist model, and the number of iterations is accumulated, and then the initial photoresist model is retrained.

[0064] Exemplarily, the adjustment of the parameter values of the first parameters can be achieved by adjusting the parameters in the initial photoresist model, so that the initial photoresist model can simulate the photoetching process conditions under different parameter values of the first parameters.

[0065] Exemplarily, the first parameters can include a plurality of photoetching process parameters. Adjusting the parameters in the initial photoresist model can be used to simultaneously adjust the parameter values corresponding to a plurality of parameters. Alternatively, adjusting the parameters in the initial photoresist model can be used to individually adjust the parameter values corresponding to one parameter.

[0066] In the embodiments of the present application, the initial model is trained according to the preset number of iterations, so that the training of the photoresist model can be completed, and then it can be quickly determined whether the parameters of the initial photoresist model will affect the photoetching pattern, thereby avoiding the waste of time and computing resources caused by the continuous training of the initial photoresist model which has no effect or has a bad effect on the photoetching pattern.

[0067] In order to achieve the acquisition of the predicted layout, another implementation manner of the training of the photoresist model is provided for another implementation manner of the present application, which is described in the following embodiments.

[0068] Figure 3 A flowchart of S131 provided by an embodiment of the present application is shown. As shown in Figure 3 S131 includes the following steps:

[0069] S1311, determining a cross transmission matrix according to the optical process parameter information in the set of photoetching process parameter information.

[0070] Exemplarily, the cross transmission matrix (Transmission Cross Coefficient, TCC) can be used to represent the transmission efficiency of light from a mask to each point above the photoresist. The Hopkins model and Abbe imaging theory can be used to describe the light path system from the mask to the photoresist, and then the cross transmission matrix can be determined.

[0071] Exemplarily, under the same photoetching process conditions, the cross transmission matrix is fixed.

[0072] S1312, calculate the aerial image corresponding to the mask layout according to the mask layout and the cross-over transmission matrix.

[0073] For example, the aerial image corresponding to the mask layout can be obtained by convoluting the mask image (MI) corresponding to the mask layout with the cross-over transmission matrix. The mask image can be obtained by sampling the mask.

[0074] For example, the aerial image can be used to represent the image formed above the photoresist after the light passes through the mask.

[0075] S1313, input the aerial image into the initial photoresist model to obtain the predicted layout.

[0076] For example, the calculated aerial image can be input into the photoresist model, and the aerial image and the photoresist kernel function can be convoluted to obtain the photoresist image (RI).

[0077] Further, the predicted layout can be obtained by performing subsequent process steps such as etching on the photoresist image.

[0078] In the embodiments of the present application, the cross-over transmission matrix is obtained, and the aerial image corresponding to the mask layout is calculated using the cross-over transmission matrix and the mask layout, and then the predicted layout is determined using the aerial image, thereby realizing the acquisition of the predicted layout.

[0079] Based on the training method of the photoresist model provided in the above embodiments, the present application also provides a specific implementation of the training device of the photoresist model. Please refer to the following embodiments.

[0080] First, refer to Figure 4 , Figure 4 is a structural schematic diagram of the training device of the photoresist model provided in another embodiment of the present application. The training device of the photoresist model provided in the embodiments of the present application comprises the following units:

[0081] The acquisition module 401 is configured to acquire the photolithography design layout, the mask layout, and the set of photolithography process parameter information affecting the photolithography design layout.

[0082] The construction module 402 is configured to construct an initial photoresist model according to the first parameter in the set.

[0083] The training module 403 is configured to perform a first training on the initial photoresist model according to the mask layout and the photolithography design layout for a preset number of iterations to obtain a first photoresist model.

[0084] The screening module 404 is configured to screen the index value of the pattern index in the last iteration process from the training log of the first training.

[0085] The cycle module 405 is configured to update the parameters in the initial photoresist model to second parameters according to the set when it is determined that the index value of the pattern index is greater than the preset threshold, to obtain an updated initial photoresist model, and return to perform the first training on the initial photoresist model according to the mask layout and the lithography design layout for a preset number of iterations to obtain the first photoresist model until the index value meets the preset threshold to obtain the trained photoresist model; the first parameters and the second parameters are not completely same.

[0086] As an implementation manner of the present application, the training module 403 performs the first training on the initial photoresist model according to the mask layout and the lithography design layout for a preset number of iterations to obtain the first photoresist model in the following manner:

[0087] As an implementation manner of the present application, the training module 403 performs the first training on the initial photoresist model according to the mask layout and the lithography design layout for a preset number of iterations to obtain the first photoresist model in the following manner: predicting the lithography pattern of the mask layout according to the initial photoresist model to obtain a predicted layout; determining the index value of the pattern index between the predicted layout and the lithography design layout, and storing the index value into the training log; when it is determined that the number of iterations does not reach the preset number of iterations, adjusting the parameters in the initial photoresist model to obtain an adjusted initial photoresist model, and accumulating the number of iterations, returning to input the aerial image into the initial photoresist model to obtain the predicted layout until the number of iterations reaches the preset number of iterations to obtain the first photoresist model.

[0088] As an implementation manner of the present application, the training module 403 predicts the lithography pattern of the mask layout according to the initial photoresist model to obtain the predicted layout in the following manner: predicting the lithography pattern of the mask layout according to the initial photoresist model to obtain the predicted layout, including: determining the cross transmission matrix according to the optical process parameter information in the set of lithography process parameter information; calculating the aerial image corresponding to the mask layout according to the mask layout and the cross transmission matrix; inputting the aerial image into the initial photoresist model to obtain the predicted layout.

[0089] As an implementation manner of the present application, the pattern index includes at least one of the following: mean square error between the predicted layout and the lithography design layout; overrule amount of the predicted layout compared with the lithography design layout.

[0090] As an implementation manner of the present application, the preset number of iterations is less than the preset number.

[0091] Figure 5A hardware structure schematic diagram of a training method of a photoresist model provided by an embodiment of the present application is shown.

[0092] The training device of the photoresist model can include a processor 501 and a memory 502 storing computer program instructions.

[0093] Specifically, the processor 501 can include a central processing unit (CPU), or an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present application.

[0094] The memory 502 can include a mass storage for data or instructions. By way of example and not limitation, the memory 502 can include a hard disk drive (HDD), a floppy disk drive, a flash memory, an optical disk, a magneto-optical disk, a magnetic tape, or a universal serial bus (USB) drive or a combination of two or more of these. Where appropriate, the memory 502 can include removable or non-removable (or fixed) media. Where appropriate, the memory 502 can be internal or external to the integrated gateway disaster recovery device. In certain embodiments, the memory 502 is non-volatile solid-state memory.

[0095] The memory can include read-only memory (ROM), random-access memory (RAM), magnetic disk storage mediums, optical storage mediums, flash memory devices, electrical, optical, or other physical / tangible memory storage devices. Thus, in general, the memory includes one or more tangible (non-transitory) computer-readable storage media (e.g., a memory device) encoded with software that, when executed (by one or more processors), is operable to perform operations described with reference to the methods according to an aspect of the present disclosure.

[0096] The processor 501 implements any one of the training methods of the photoresist model in the above embodiments by reading and executing the computer program instructions stored in the memory 502.

[0097] In one example, the training device of the photoresist model can further include a communication interface 503 and a bus 510. As shown, the processor 501, the memory 502, and the communication interface 503 are connected through the bus 510 and complete communication with each other. Figure 5

[0098] The communication interface 503 is mainly used to realize the communication between the modules, devices, units and / or equipment in the embodiments of the present application.

[0099] ​Bus 510 includes hardware, software, or both, that couples components of an online data traffic metering device together. For example, and not limitingly, the bus may include an Accelerated Graphics Port (AGP) or other graphics bus, an Enhanced Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), HyperTransport (HT) interconnect, an Industry Standard Architecture (ISA) bus, an Infinite Bandwidth Interconnect, a Low Pin Count (LPC) bus, a memory bus, a Microchannel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-X) bus, a Serial Advanced Technology Attachment (SATA) bus, a Video Electronics Standards Association Local (VLB) bus, or other suitable buses, or combinations of two or more of these. Where appropriate, bus 510 may include one or more buses. Although specific buses are described and illustrated in embodiments of this application, any suitable bus or interconnect is contemplated herein.

[0100] The training device for the photoresist model can execute the training method of the photoresist model in the embodiments of this application by preset number of iterations, thereby achieving the combination of Figure 1 and Figure 4 The training method and apparatus for the photoresist model are described.

[0101] Furthermore, in conjunction with the photoresist model training methods in the above embodiments, this application embodiment can provide a computer storage medium for implementation. This computer storage medium stores computer program instructions; when these computer program instructions are executed by a processor, they implement any of the photoresist model training methods in the above embodiments.

[0102] This embodiment also provides a computer program product, including a computer program, which, when executed, implements a method for training any of the photoresist models described in the above embodiments.

[0103] It should be clarified that this application is not limited to the specific configurations and processes described above and shown in the figures. For the sake of brevity, detailed descriptions of known methods are omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the method process of this application is not limited to the specific steps described and shown. Those skilled in the art can make various changes, modifications, and additions, or change the order of steps, after understanding the spirit of this application.

[0104] The functions noted in the description of the structural block diagrams above can be implemented as hardware, software, firmware, or a combination thereof. When implemented in hardware, they can be, for example, electronic circuits, application specific integrated circuits (ASICs), appropriate firmware, plug-ins, functional cards, and the like. When implemented in software, the elements of the present application are program or code segments that are used to perform the required tasks. The program or code segments can be stored in a machine-readable medium or transmitted through a data signal carried in a carrier wave over a transmission medium or communication link. A "machine-readable medium" includes any medium that can store or transport information. Examples of machine-readable media include electronic circuits, semiconductor memory devices, ROM, flash memory, erasable ROM (EROM), floppy disks, CD-ROMs, optical disks, hard disks, fiber optic media, radio frequency (RF) links, and the like. The code segments can be downloaded via computer networks such as the Internet, intranets, and the like.

[0105] It is also important to note that the examples mentioned in the present application describe some methods or systems based on a series of steps or devices. However, the present application is not limited to the order of the steps mentioned above, that is, the steps can be performed in the order mentioned in the examples, or in an order different from the examples, or several steps can be performed simultaneously.

[0106] The computer program instructions can also be loaded onto a computer, other programmable data processing apparatus, or other processing device to cause a series of operational steps to be performed on the computer, other programmable apparatus or other processing device to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide processes for implementing the functions / acts specified in the flowchart and / or block diagram block or blocks. These computer program instructions can also be stored in a computer readable medium that can direct a computer, other programmable data processing apparatus, or other processing device to operate in a particular manner, such that the instructions stored in the computer readable medium produce an article of manufacture including instructions which implement the function / act specified in the flowchart and / or block diagram block or blocks. The computer program instructions can also be loaded onto a computer, other programmable data processing apparatus, or other processing device to cause a series of operational steps to be performed on the computer, other programmable apparatus or other processing device to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide processes for implementing the functions / acts specified in the flowchart and / or block diagram block or blocks. These computer program instructions can also be stored in a computer readable medium that can direct a computer, other programmable data processing apparatus, or other processing device to operate in a particular manner, such that the instructions stored in the computer readable medium produce an article of manufacture including instructions which implement the function / act specified in the flowchart and / or block diagram block or blocks.

[0107] The above merely describes a specific implementation of the present application. Those skilled in the art can clearly understand the specific working processes of the system, modules and units described above for the convenience and brevity of description, and can refer to the corresponding processes in the foregoing method embodiments, which will not be described herein again. It should be understood that the protection scope of the present application is not limited to this, and any person skilled in the art can easily think of various equivalent modifications or replacements within the technical range disclosed by the present application, and these modifications or replacements should be covered within the protection scope of the present application.

Claims

1. A method for training a photoresist model, characterized in that, The method comprises the following steps: obtaining a photolithography design layout, a mask layout and a set of photolithography process parameter information affecting the photolithography design layout; constructing an initial photoresist model according to a first parameter in the set; performing first training on the initial photoresist model according to the mask layout and the photolithography design layout for a preset number of iterations to obtain a first photoresist model; screening an index value of a pattern index in a last iteration process from a training log of the first training; in a case where it is determined that the index value of the pattern index is greater than a preset threshold, updating parameters in the initial photoresist model to second parameters according to the set to obtain an updated initial photoresist model, and returning to performing first training on the initial photoresist model according to the mask layout and the photolithography design layout for a preset number of iterations to obtain a first photoresist model until the index value is less than or equal to the preset threshold to obtain a trained photoresist model; the first parameter and the second parameter are not completely same. 2.The training method of a photoresist model according to claim 1, wherein, The performing first training on the initial photoresist model according to the mask layout and the photolithography design layout for a preset number of iterations to obtain a first photoresist model comprises: predicting a lithography pattern of the mask layout according to the initial photoresist model to obtain a predicted layout; determining an index value of a pattern index between the predicted layout and the photolithography design layout, and storing the index value in the training log; in a case where it is determined that the number of iterations does not reach the preset number of iterations, adjusting parameters in the initial photoresist model to obtain an adjusted initial photoresist model, and accumulating the number of iterations, and returning to predicting a lithography pattern of the mask layout according to the initial photoresist model to obtain a predicted layout until the number of iterations reaches the preset number of iterations to obtain a first photoresist model.

3. The method of claim 2, wherein, The predicting a lithography pattern of the mask layout according to the initial photoresist model to obtain a predicted layout comprises: determining a cross transmission matrix according to optical process parameter information in the set of photolithography process parameter information; calculating a spatial image corresponding to the mask layout according to the mask layout and the cross transmission matrix; inputting the spatial image into the initial photoresist model to obtain the predicted layout.

4. The method according to any one of claims 1 to 3, characterized in that, The pattern index comprises at least one of the following: a mean square error between the predicted layout and the photolithography design layout; an overrule amount of the predicted layout compared with the photolithography design layout.

5. The method according to any one of claims 1 to 3, characterized in that, The preset number of iterations is less than a preset number.

6. A training device of a photoresist model, characterized in that, The device comprises: an obtaining module configured to obtain a photolithography design layout, a mask layout and a set of photolithography process parameter information affecting the photolithography design layout; a constructing module configured to construct an initial photoresist model according to a first parameter in the set; a training module configured to perform first training on the initial photoresist model according to the mask layout and the photolithography design layout for a preset number of iterations to obtain a first photoresist model; a screening module configured to screen an index value of a pattern index in a last iteration process from a training log of the first training; The circulation module is configured to update parameters in the initial photoresist model to second parameters according to the set in a case where a value of the graphic index is greater than a preset threshold, to obtain an updated initial photoresist model, and to return to performing the first training on the initial photoresist model according to the mask layout and the lithography design layout for a preset number of iterations to obtain a first photoresist model until the value of the index meets the preset threshold to obtain a trained photoresist model; the first parameters and the second parameters are not completely identical. 7.A training device of a photoresist model, characterized in that, The device comprises a processor and a memory storing computer program instructions; The processor executes the computer program instructions to implement the photoresist model training method in any one of claims 1-5.

8. A computer-readable storage medium, characterized in that, The computer program instructions are stored on the computer readable storage medium and are executed by the processor to implement the photoresist model training method in any one of claims 1-5.

9. A computer program product, characterised in that, The instructions in the computer program product are executed by the processor of the electronic device to enable the electronic device to perform the photoresist model training method in any one of claims 1-5.

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