Simulation method of programmable lighting source system and collaborative optimization method of light source and mask

Abstract

The present application belongs to the field of computational lithography technology, and specifically discloses a simulation method for a programmable illumination light source system and a light source mask collaborative optimization method. The simulation method includes: calibrating the parameters of the SMTM in a first simulation model based on a designed light source shape OSM sample and actual processing results; the actual processing results are obtained by inputting the OSM sample into a lithography physical machine and monitoring the processing process of the lithography physical machine, and the first simulation model is used to output a simulation processing result corresponding to the actual processing result, and the first simulation model at least includes the SMTM; the calibrated SMTM is used as a PIS model. The present application calibrates the parameters of the SMTM in the first simulation model using reference data to obtain a PIS model. When obtaining the PIS characteristics, it can effectively avoid the influence of PIS hardware distortion on the exposure effect on the lithography physical machine, thereby ensuring the yield of IC manufacturing.

Description

Technical Field

[0001] The present application belongs to the field of computational lithography technology, and more specifically, relates to a simulation method for a programmable illumination light source system and a light source mask collaborative optimization method. Background Art

[0002] With the gradual reduction in feature size in integrated circuit (IC) manufacturing, source mask optimization (SMO) has become a key technology for improving resolution. In SMO, the light source and mask are collaboratively optimized. The light source is typically implemented using a programmable illumination system (PIS) on a physical lithography machine. However, due to limitations in processing technology and clamping techniques, the PIS hardware can produce some distortion when implementing the designed light source shape (Ordered Source Map (OSM)). The resulting actual light source shape (Realized Source Map (RSM)) often differs from the OSM. For users other than PIS and lithography machine manufacturers, including IC manufacturers, fab engineers, and developers and users of optical proximity correction (OPC) tools, the PIS light source implementation process is largely unknown. Without access to PIS characteristics, using SMO to optimize the light source can lead to unsatisfactory exposure results on the physical lithography machine due to PIS hardware distortion, thus impacting the overall IC manufacturing yield. Summary of the Invention

[0003] In response to the defects of the existing technology, the purpose of this application is to establish and calibrate the Source Map Transfer Model (SMTM). While obtaining the PIS characteristics, the SMO is used to optimize the light source, aiming to solve the problem in the existing technology that the PIS hardware distortion will cause the final exposure effect on the lithography physical machine to be unpredictable.

[0004] To achieve the above objectives, in a first aspect, the present application provides a simulation method for a programmable lighting source system, comprising:

[0005] Calibrate parameters of a light source shape transfer model (SMTM) in a first simulation model based on a designed light source shape OSM sample and actual processing results; the actual processing results are obtained by inputting the OSM sample into a lithography physical machine and monitoring the processing process of the lithography physical machine, wherein the lithography physical machine is equipped with a programmable illumination light source system (PIS) entity; the first simulation model is used to output a simulation processing result corresponding to the actual processing result, and the first simulation model at least includes the SMTM;

[0006] The calibrated SMTM is used as a PIS model; the PIS model is used to simulate a PIS entity.

[0007] In a possible implementation, when the actual light source shape RSM output by the PIS entity can be monitored, the actual processing result is the RSM output by the PIS entity, the first simulation model is the SMTM, and the simulation processing result is the RSM output by the SMTM;

[0008] The calibrating of parameters of the light source shape transfer model SMTM in the first simulation model based on the designed light source shape OSM sample and the actual processing results includes:

[0009] Continuously optimizing the SMTM based on the OSM sample and the RSM output by the PIS entity until an iteration stop condition is met;

[0010] The optimizing of the SMTM comprises:

[0011] Input the OSM sample into the SMTM, and obtain the RSM output by the SMTM;

[0012] comparing a difference between the RSM output by the PIS entity and the RSM output by the SMTM to determine a first difference comparison result;

[0013] When the first difference comparison result indicates that the iteration stop condition is not satisfied, the parameters of the SMTM are adjusted based on the first difference comparison result; when the first difference comparison result indicates that the iteration stop condition is satisfied, the calibrated SMTM is determined.

[0014] In a possible implementation, the SMTM is a neural network model, and comparing the difference between the RSM output by the PIS entity and the RSM output by the SMTM to determine a first difference comparison result includes:

[0015] Determine a first difference between the RSM output by the PIS entity and the RSM output by the SMTM; evaluate the first difference based on a loss function and determine a loss value as the first difference comparison result;

[0016] When the first difference comparison result indicates that an iteration stop condition is not satisfied, adjusting the parameters of the SMTM based on the first difference comparison result includes:

[0017] Based on the loss value, the network neuron weights of the neural network model are adjusted through back propagation.

[0018] In one possible implementation, when the actual light source shape RSM output by the PIS entity cannot be monitored, the first simulation model includes an SMTM, an optical exposure model, and a photoresist model cascaded in sequence, and the simulation processing result is a simulated silicon wafer result output by the photoresist model;

[0019] The calibrating of parameters of the light source shape transfer model SMTM in the first simulation model based on the designed light source shape OSM sample and the actual processing results includes:

[0020] Based on the OSM sample and the actual silicon wafer results output by the lithography physical machine, continuously optimizing the SMTM until an iteration stop condition is met;

[0021] The optimizing of the SMTM comprises:

[0022] Inputting the OSM sample into the first simulation model to obtain the simulated silicon wafer result output by the photoresist model;

[0023] Comparing a difference between an actual silicon wafer result output by the lithography physical machine and a simulated silicon wafer result output by the photoresist model to determine a second difference comparison result;

[0024] When the second difference comparison result indicates that the iteration stop condition is not satisfied, the parameters of the SMTM are adjusted based on the second difference comparison result; when the second difference comparison result indicates that the iteration stop condition is satisfied, the calibrated SMTM is determined.

[0025] In a second aspect, the present application further provides a light source mask collaborative optimization method, comprising:

[0026] Determine target silicon results;

[0027] Based on the target silicon wafer result and the second simulation model, performing light source mask collaborative optimization to obtain an optimized designed light source shape OSM and an optimized mask;

[0028] The second simulation model is constructed based on a PIS model, an optical exposure model and a photoresist model, and the PIS model is obtained by applying any one of the above-mentioned simulation methods for the programmable illumination light source system.

[0029] In a possible implementation, the second simulation model includes a first-stage simulation model and a second-stage simulation model, the first-stage simulation model is constructed based on the optical exposure model and the photoresist model, and the PIS model serves as the second-stage simulation model;

[0030] The light source mask collaborative optimization is performed based on the target silicon wafer result and the second simulation model to obtain the optimized designed light source shape OSM and the optimized mask, including:

[0031] Based on the target silicon wafer result and the first-stage simulation model, perform light source mask collaborative optimization to obtain the target RSM and the optimized mask;

[0032] Based on the target RSM and the second-stage simulation model, an optimized OSM is obtained through iterative optimization.

[0033] In a possible implementation, obtaining an optimized OSM through iterative optimization based on the target RSM and the second-stage simulation model includes:

[0034] Input the target OSM into the PIS model to obtain the RSM output by the PIS model, where the initial target OSM is the target RSM;

[0035] Comparing the difference between the RSM output by the PIS model and the target RSM to determine the second-stage difference comparison result;

[0036] When the second-stage difference comparison result indicates that the iteration stopping condition is not satisfied, the target OSM is adjusted based on the second-stage difference comparison result; or, when the second-stage difference comparison result indicates that the iteration stopping condition is satisfied, the target OSM is determined as the optimized OSM.

[0037] In one possible implementation, the second simulation model is composed of the PIS model, the optical exposure model, and the photoresist model, which are cascaded in sequence. The light source and mask collaborative optimization is performed based on the target silicon wafer result and the second simulation model to obtain an optimized designed light source shape OSM and an optimized mask, including:

[0038] Inputting the target OSM into the PIS model, inputting the output of the PIS model and the target mask into the optical exposure model, inputting the output of the optical exposure model into the photoresist model, and obtaining the simulated silicon wafer result output by the photoresist model;

[0039] comparing the difference between the simulated silicon wafer result and the target silicon wafer result to determine a third difference comparison result;

[0040] When the third difference comparison result indicates that the iteration stop condition is not satisfied, the target OSM and the target mask are adjusted based on the third difference comparison result; or, when the third difference comparison result indicates that the iteration stop condition is satisfied, the target OSM is determined as the optimized OSM and the target mask is determined as the optimized mask.

[0041] In a third aspect, the present application provides an electronic device comprising: at least one memory for storing programs; and at least one processor for executing the programs stored in the memory. When the programs stored in the memory are executed, the processor is used to execute the method described in the first aspect or any possible implementation of the first aspect, or the processor is used to execute the method described in the second aspect or any possible implementation of the second aspect.

[0042] In a fourth aspect, the present application provides a computer-readable storage medium, which stores a computer program. When the computer program runs on a processor, it enables the processor to execute the method described in the first aspect or any possible implementation of the first aspect, or enables the processor to execute the method described in the second aspect or any possible implementation of the second aspect.

[0043] In general, the above technical solutions conceived by this application have the following beneficial effects compared with the existing technologies:

[0044] Before calibrating the parameters of the light source shape transfer model SMTM in the first simulation model, the output of a monitored link can be determined as the actual processing result and the corresponding first simulation model can be determined based on the monitorable conditions of each link in the processing process of the lithography physical machine. For example, if the RSM output by the PIS entity of the lithography physical machine can be monitored (or observed), the RSM output by the PIS entity can be used as the actual processing result. Accordingly, the first simulation model constructed uses the OSM sample as the simulation input and the RSM as the simulation output (simulation processing result). For example, if the RSM can be monitored (or observed) and the actual silicon wafer result output by the lithography physical machine can be monitored, the actual silicon wafer result can be used as the actual processing result. Accordingly, the first simulation model constructed uses the OSM sample as the simulation input and the silicon wafer result as the simulation output. After constructing the first simulation model and determining the reference data (including multiple OSM samples and the actual processing results corresponding to each OSM sample), the reference data can be used to calibrate the parameters of the SMTM in the first simulation model to obtain a calibrated SMTM (also known as the PIS model). The PIS model can be used to simulate the PIS entity and characterize PIS characteristics. When the PIS characteristics are obtained, using the SMO to optimize the light source can effectively prevent the impact of PIS hardware distortion on the exposure effect on the lithography physical machine, thereby ensuring the yield of IC manufacturing. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 Schematic diagram comparing the PIS physical machine and the PIS model provided in the embodiment of the present application;

[0046] Figure 2 Schematic diagram of the process of simulating the PIS provided in the embodiment of the present application;

[0047] Figure 3 This is a schematic diagram of SMTM modeling under the condition that RSM is observable provided in an embodiment of the present application;

[0048] Figure 4 This is a schematic diagram of the SMTM modeling based on a neural network provided in an embodiment of the present application;

[0049] Figure 5 This is a schematic diagram of SMTM modeling under the condition that RSM is unobservable, provided in an embodiment of the present application;

[0050] Figure 6 1 is a flow chart of a light source and mask collaborative optimization method provided in an embodiment of the present application;

[0051] Figure 7 This is a schematic diagram of the two-stage SMO process provided in an embodiment of the present application;

[0052] Figure 8Schematic diagram of the process of SMO including PIS model provided in the embodiment of the present application;

[0053] Figure 9 It is a structural diagram of an electronic device provided in an embodiment of the present application.

[0054] Throughout the drawings, the same reference numerals are used to denote the same elements or structures, wherein:

[0055] D1: OSM; D11: OSM image; D2: RSM; D3: measured RSM; D4: simulated RSM; D41: simulated RSM image; D5: mask; D6: actual silicon wafer results (i.e., actual silicon wafer surface exposure and development results); D7: simulated silicon wafer results (i.e., simulated silicon wafer surface results); D8: target silicon wafer results; D9: target RSM; P1: PIS physical machine; P2: PIS model; P3: SMTM; P31: SMTM established using neural network; P4: actual optical exposure system; P5: optical exposure model; P6: actual photoresist reaction; P7: photoresist model; C1: comparison of measured RSM with simulated RSM; C2: comparison of actual silicon wafer results with simulated silicon wafer results. DETAILED DESCRIPTION

[0056] In order to facilitate a clearer understanding of the various embodiments of the present application, some relevant background knowledge is first introduced as follows.

[0057] For users other than PIS manufacturers and lithography machine manufacturers, IC manufacturers, wafer fab engineers, and OPC tool developers and users, the implementation process of PIS for light sources is unknown. However, through the applicant's engineering practice, it was found that the mapping relationship from OSM to RSM implemented by each machine, or even each series of machines, is certain, and this mapping is the light source shape transfer function (SMTF). In terms of mathematical characteristics, this mapping relationship has some good analytical properties, such as it is at least continuous and even differentiable. The continuity of SMTF means that its input and output can be represented by, for example, an image representation or a coefficient vector representation of an orthogonal kernel function. Regardless of which of the above representation forms is used, there is a mathematical mapping relationship between the input and output, namely the SMTF. This ensures that the SMTF implemented by the PIS physical machine is trainable and learnable. Therefore, the SMTF implemented by the PIS can be simulated and modeled to simulate the characteristics of the PIS.

[0058] To this end, this application provides a PIS simulation method, aiming to establish a separate PIS model to simulate the PIS's light source implementation process. This application also provides a light source and mask collaborative optimization method, which further applies the calibrated model to SMO, providing a solution for users other than PIS manufacturers and lithography machine manufacturers, including IC manufacturers, wafer fab engineers, and OPC tool developers and users, to avoid the problems of reduced exposure quality and reduced process window caused by PIS distortion.

[0059] In order to make the purpose, technical solutions and advantages of this application more clear, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0060] In the specification and claims herein, the terms "first" and "second" are used to distinguish different objects, rather than to describe a specific order of objects. For example, a first difference comparison result and a second difference comparison result are used to distinguish different difference comparison results, rather than to describe a specific order of difference comparison results.

[0061] In the embodiments of this application, words such as "exemplary" or "for example" are used to indicate examples, illustrations, or descriptions. Any embodiment or design described as "exemplary" or "for example" in the embodiments of this application should not be interpreted as being preferred or advantageous over other embodiments or designs. Rather, the use of words such as "exemplary" or "for example" is intended to present the relevant concepts in a concrete manner.

[0062] In the description of the embodiments of the present application, unless otherwise specified, "multiple" means two or more, for example, multiple processing units means two or more processing units, etc.; multiple elements means two or more elements, etc.

[0063] For ease of understanding, the English abbreviations and related technical terms involved in the embodiments of this application are explained and described below.

[0064] (1)OPC: optical proximity correction;

[0065] (2) SMO: Source Mask Optimization, source mask collaborative optimization;

[0066] (3)PIS: Programmable Illumination System, programmable lighting source system;

[0067] (4) OSM: ordered source map, which indicates the designed light source shape before entering PIS;

[0068] (5)RSM: realized source map, which represents the actual light source shape generated by PIS;

[0069] (6) In this application, physical refers to an entity, an object, or a physical machine, and virtual refers to something on a computer or simulated;

[0070] (7) SMTF: source map transfer function, light source shape transfer function;

[0071] (8) SMTM: source map transfer model, a model established by SMTF to describe PIS. The calibrated SMTM can be used as the PIS model.

[0072] The embodiments of the present application are described below in conjunction with the drawings in the embodiments of the present application.

[0073] Figure 1 Schematic diagram of the comparison between the PIS physical machine and the PIS model provided in the embodiment of the present application, as shown in FIG. Figure 1 As shown in the figure, in the physical environment, OSM is the input of the PIS physical machine (the programmable illumination light source system entity in the lithography physical machine) and the output is RSM. In the simulation environment, the PIS model is used to describe the mapping characteristics of the PIS physical machine, namely SMTF. The same OSM is input and the output is the same RSM as the physical environment.

[0074] The modeling process of the PIS model can be considered separately according to whether the RSM output by the PIS physical machine can be observed (which will be explained in detail later).

[0075] Figure 2 Schematic diagram of the process of simulating the PIS provided in the embodiment of the present application. Figure 2 As shown, the execution subject of the PIS simulation method may be an electronic device, such as a server, etc. The method includes the following steps S101 and S102.

[0076] Step S101: Calibrate the parameters of a light source shape transfer model (SMTM) in a first simulation model based on a design light source shape OSM sample and actual processing results. The actual processing results are obtained by inputting the OSM sample into a lithography physical machine and monitoring the processing of the lithography physical machine. The lithography physical machine is configured with a programmable illumination light source system (PIS) entity (or PIS physical machine). The first simulation model is used to output simulation processing results corresponding to the actual processing results. The first simulation model at least includes the SMTM.

[0077] Step S102: The calibrated SMTM is used as a PIS model; the PIS model is used to simulate a PIS entity.

[0078] Specifically, the actual processing result can be the processing result of a certain processing link in the middle of the lithography physical machine, or the processing result of the last processing link. The specific determination needs to be based on whether the various processing links of the lithography physical machine can be monitored (observed). If the processing result of a certain processing link in the middle can be monitored (observed), the processing result of the processing link can be used as the above-mentioned actual processing result; if the processing result of the intermediate link cannot be monitored (observed), the processing result of the last processing link can be used as the above-mentioned actual processing result.

[0079] For example, the actual processing result can be the actual light source shape RSM or the actual silicon wafer result. If the RSM output by the PIS entity of the lithography physical machine can be monitored (or observed), the RSM output by the PIS entity can be used as the actual processing result. If the actual silicon wafer result output by the lithography physical machine can be monitored, the actual silicon wafer result can be used as the actual processing result.

[0080] The coverage of light source shapes in the data set (including OSM samples and corresponding actual processing results) has a direct impact on the accuracy of the PIS model. To ensure the accuracy of the PIS model, multiple OSM samples and the actual processing results corresponding to each OSM sample can be obtained. These multiple OSM samples should cover as many light source shapes as possible that can be realized by the PIS physical machine. Using these multiple OSM samples and the actual processing results corresponding to each OSM sample, the parameters of the light source shape transfer model (SMTM) in the first simulation model are calibrated to ensure the accuracy of the PIS model.

[0081] The working principle of the PIS simulation method is analyzed below: Before calibrating the parameters of the light source shape transfer model SMTM in the first simulation model, the output of a monitored link can be determined as the actual processing result and the corresponding first simulation model can be determined based on the monitorable conditions of each link in the processing process of the lithography physical machine. For example, if the RSM output by the PIS entity of the lithography physical machine can be monitored (or observed), the RSM output by the PIS entity can be used as the actual processing result. Accordingly, the first simulation model constructed uses the OSM sample as the simulation input and the RSM as the simulation output (simulation processing result). For example, if the actual silicon wafer result output by the lithography physical machine can be monitored, the actual silicon wafer result can be used as the actual processing result. Accordingly, the first simulation model constructed uses the OSM sample as the simulation input and the silicon wafer result as the simulation output. After constructing the first simulation model and determining the reference data (including multiple OSM samples and the actual processing results corresponding to each OSM sample), the reference data can be used to calibrate the parameters of the SMTM in the first simulation model to obtain a calibrated SMTM (also known as the PIS model). The PIS model can be used to simulate the PIS entity and characterize PIS characteristics. When the PIS characteristics are obtained, using the SMO to optimize the light source can effectively prevent the impact of PIS hardware distortion on the exposure effect on the lithography physical machine, thereby ensuring the yield of IC manufacturing.

[0082] It is understandable that, compared to the SMO in the prior art, the embodiment of the present application takes into account the distortion of the light source shape achieved by PIS, distinguishes the designed light source shape (OSM) from the actual light source shape (RSM) achieved by PIS, models and calibrates the PIS physical machine, and constructs a model conversion relationship from OSM to RSM to obtain the calibrated SMTM (i.e., PIS model). The PIS model can be used in SMO to optimize the designed light source shape of the SMO optimization algorithm, so that the final SMO optimization result OSM can be directly used as the input of the PIS physical machine, solving the problem that when IC manufacturers actually use the light source result of the existing SMO as the light source setting of the PIS physical machine, the distortion caused by the processing and manufacturing and clamping constraints of the PIS physical machine further leads to unsatisfactory lithography exposure results, thereby effectively improving the process window.

[0083] In addition, during the PIS model establishment process, the embodiment of the present application takes into account the detectability of the RSM of the light source shape output by the PIS physical machine of the actual machine, and considers the situations where the RSM can be directly detected and the RSM cannot be directly detected. Based on the monitorable conditions of each link in the processing process of the lithography physical machine, the output of a monitorable link is determined as the actual processing result and the corresponding first simulation model is determined. Then, the parameters of the SMTM in the first simulation model are calibrated using reference data to obtain the calibrated SMTM (i.e., the PIS model). The PIS model can be used to simulate the PIS entity. For IC manufacturers, the proposed method is practical and applicable.

[0084] In one possible implementation, when the actual light source shape RSM output by the PIS entity can be monitored, the actual processing result is the RSM output by the PIS entity (i.e., the RSM obtained through monitoring, which can be referred to as the "measured RSM"); the first simulation model is the SMTM, and the simulation processing result is the RSM output by the SMTM (i.e., the RSM obtained through simulation, which can be referred to as the "simulated RSM");

[0085] Based on the designed light source shape OSM sample and actual processing results, the parameters of the light source shape transfer model SMTM in the first simulation model are calibrated, including:

[0086] Based on the OSM samples and the RSM output by the PIS entity, the SMTM is continuously optimized until the iteration stop condition is met;

[0087] Optimize SMTM, including:

[0088] Input OSM samples into SMTM and obtain RSM output by SMTM;

[0089] Comparing the difference between the RSM output by the PIS entity and the RSM output by the SMTM, and determining a first difference comparison result;

[0090] When the first difference comparison result indicates that the iteration stop condition is not satisfied, the parameters of the SMTM are adjusted based on the first difference comparison result; when the first difference comparison result indicates that the iteration stop condition is satisfied, the calibrated SMTM is determined.

[0091] Example 1: Figure 3 This is a schematic diagram of SMTM modeling under the condition that RSM is observable provided by the embodiment of the present application. When the RSM output by the PIS physical machine can be directly observed, the following can be used: Figure 3The process shown is used for modeling, wherein the OSM sample is used as input to the PIS physical machine and the light source shape transfer model (SMTM), respectively. The RSM output by the PIS physical machine is compared with the RSM output by the SMTM. The parameters in the SMTM are adjusted according to the comparison results. The parameters are iteratively adjusted until the difference between the RSM output by the two is small enough, that is, the SMTF implemented by the PIS physical machine is consistent with the SMTF implemented by the established model. Then, the established SMTM can be used as the PIS model. Specifically, when the RSM output by the PIS physical machine can be directly observed, the simulation method of the PIS provided in the embodiment of the present application includes the following steps S11 to S15.

[0092] Step S11: preliminarily establish an SMTM model, which may adopt, but is not limited to, a combination of various eigenvalues ​​and eigenfunctions, including adjustable parameters.

[0093] Step S12: Collect a data set of input OSM samples and output RSM (measured RSM) through the PIS physical machine as reference data; the data should be OSM samples of multiple different types (the type here can be used to characterize the light source shape), different configurations (the configuration here can be used to indicate the configuration method of the light source shape), and different parameters (the parameters here can be parameters related to the light source shape), and try to cover all kinds of light source shapes that the PIS physical machine can achieve. After inputting each OSM sample into the PIS, the corresponding actual RSM is obtained. The coverage of the light source shape of this data set has a direct impact on the model accuracy of the PIS model; the OSM samples and RSM, as the input and output of the SMTM, can be represented by, but not limited to, images, or by using approximate complete polynomials as basis functions to decompose and characterize them (OSM and RSM), such as, but not limited to, Zernike polynomials, Legendre polynomials, etc.

[0094] Step S13: Use the OSM samples in step S12 as input data and input them into the SMTM in step S11, output the simulated RSM, and make a difference between each group of simulated RSM and the actual RSM (measured RSM) for comparison to determine whether the difference (i.e., the first difference comparison result) is less than a preset threshold. If it is not less than the threshold, jump to step S14; if it is less than the threshold, jump to step S15.

[0095] Step S14: Adjust the adjustable parameters in the SMTM, update the SMTM, and jump to step S13.

[0096] Step S15: Stop iteration and output SMTM as the PIS model.

[0097] In a possible implementation, the SMTM is a neural network model, and comparing the difference between the RSM output by the PIS entity and the RSM output by the SMTM to determine a first difference comparison result includes:

[0098] Determine a first difference between the RSM output by the PIS entity and the RSM output by the SMTM; evaluate the first difference based on a loss function and determine a loss value as a first difference comparison result;

[0099] If the first difference comparison result indicates that the iteration stop condition is not satisfied, adjusting the parameters of the SMTM based on the first difference comparison result includes:

[0100] Based on the loss value, the network neuron weights of the neural network model are adjusted through back propagation.

[0101] Example 2: Figure 4 This is a schematic diagram of SMTM modeling based on neural network provided by the embodiment of the present application. Taking the input OSM sample and the output RSM as an example, when the RSM output by the PIS physical machine can be directly observed, the following can be used: Figure 4 Specifically, when the RSM output by the PIS physical machine can be directly observed, the PIS simulation method provided by the embodiment of the present application includes the following steps S21 to S28.

[0102] Step S21: Establish an SMTM neural network structure, set the number and structure of hidden layers according to the PIS features and light source shape representation method, the input layer is the OSM image, the output layer is the RSM image, and the hidden layer performs image processing.

[0103] Step S22: Initialize the network neuron weights using a random initialization method.

[0104] Step S23: Collect a data set of input OSM images and actual output RSM images through the PIS physical machine; the data should be multiple OSMs of different types, configurations, and parameters, and try to cover all types of light source shapes that the PIS physical machine can achieve. After inputting each OSM sample into the PIS, the corresponding actual RSM is obtained. The coverage of the light source shape of this data set has a direct impact on the model accuracy of the PIS model.

[0105] Step S24: define the loss function as the difference between the actual RSM image and the simulated RSM image. The difference image (ie, the first difference) between the two may be evaluated using, but not limited to, the 2-norm.

[0106] Step S25: Use each OSM image as the network input and perform forward propagation to obtain each simulated RSM image as the network output.

[0107] Step S26, calculate the loss function, and determine whether the loss value of the loss function (that is, the first difference comparison result) meets the iteration stop condition. If not, jump to step S27; if so, jump to step S28.

[0108] Step S27: Based on the network error (i.e., loss value) calculated in step S26, perform back propagation, update the model by adjusting the network neuron weights, and jump to step S25; back propagation can use but is not limited to the gradient descent method to update the network neuron weights.

[0109] Step S28: stop iteration and output the network structure as the calibrated SMTM, that is, the PIS model.

[0110] In one possible implementation, when the actual light source shape RSM output by the PIS entity cannot be monitored, the first simulation model includes a cascaded SMTM, an optical exposure model, and a photoresist model, and the simulation processing result is a simulated silicon wafer result (i.e., a simulated silicon wafer surface result) output by the photoresist model.

[0111] Based on the designed light source shape OSM sample and actual processing results, the parameters of the light source shape transfer model SMTM in the first simulation model are calibrated, including:

[0112] Based on the OSM samples and the actual silicon wafer results output by the lithography physical machine (i.e., the actual silicon wafer surface exposure and development results), the SMTM is continuously optimized until the iteration stop condition is met;

[0113] Optimize SMTM, including:

[0114] Input the OSM sample into the first simulation model to obtain the simulated silicon wafer result output by the photoresist model;

[0115] Comparing the difference between the actual silicon wafer result output by the lithography physical machine and the simulated silicon wafer result output by the photoresist model to determine a second difference comparison result;

[0116] When the second difference comparison result indicates that the iteration stop condition is not satisfied, the parameters of the SMTM are adjusted based on the second difference comparison result; when the second difference comparison result indicates that the iteration stop condition is satisfied, the calibrated SMTM is determined.

[0117] Example three, Figure 5 This is a schematic diagram of SMTM modeling in the case where RSM is unobservable provided in the embodiment of the present application. When the RSM output of the PIS physical machine cannot be directly observed, the following can be used: Figure 5The process shown is used to perform PIS modeling, in which the OSM sample is used as input, and the actual photolithography exposure and development and the complete photolithography exposure and development model including SMTM are input respectively. The actual exposed silicon wafer results are compared with the silicon wafer results obtained by simulation calculation, and the parameters in SMTM are adjusted according to the comparison results. The parameters are iteratively adjusted until the difference between the silicon wafer results output by the two is small enough, that is, the SMTF implemented by the PIS physical machine is consistent with the SMTF implemented by the established model, and the established SMTM can be used as the PIS model. When the RSM output by the PIS physical machine cannot be directly observed, the simulation method of the PIS provided in the embodiment of the present application includes the following steps S31 to S36.

[0118] Step S31: preliminarily establish an SMTM model, which may adopt, but is not limited to, a combination of various eigenvalues ​​and eigenfunctions, including adjustable parameters.

[0119] Step S32: Through the actual photolithography exposure and development process, collect the data group of input OSM samples and output actual silicon wafer results as reference data; the input OSM data should be multiple OSM samples of different types, different configurations, and different parameters, and try to cover various light source shapes that the PIS physical machine can achieve. Each OSM sample is input into the PIS physical machine, and after optical exposure and photoresist reaction, the actual exposure silicon wafer result is obtained; the OSM characterization method can be selected from but not limited to image representation, or use approximate complete polynomials as basis functions to decompose and characterize it, such as but not limited to Zernike polynomials, Legendre polynomials, etc.; the silicon wafer result can be characterized by but not limited to the photoresist profile, the feature size of each part of the silicon wafer, etc.; when performing optical exposure, a mask is required, and one or a group of specially designed masks can be used for cooperation, and the mask characterization method is not restricted.

[0120] Step S33: Use each OSM sample in step S32 as input data, input the SMTM in step S31, use its output simulated RSM as input of the optical exposure model, and use the output of the optical exposure model as input of the photoresist model to output the simulated silicon wafer result; the simulated silicon wafer result adopts the same characterization method as the actual silicon wafer result in step S32.

[0121] It is worth noting that the optical exposure model and the photoresist model are established and calibrated models, which have the ability to describe the optical exposure process and the photoresist reaction process in the actual exposure and development process. The model establishment and calibration methods can adopt the existing relevant model establishment and calibration methods. This application is not restricted by the modeling and calibration methods of the two.

[0122] Step S34: Subtract the simulated silicon wafer results from the actual silicon wafer results for comparison to determine whether the difference (i.e., the second difference comparison result) is less than a preset threshold. If not, jump to step S35; if less than the threshold, jump to step S36.

[0123] Step S35: Adjust the adjustable parameters in SMTM, update SMTM, and jump to step S33.

[0124] Step S36: Stop iteration and output SMTM as the PIS model.

[0125] Figure 6 FIG. 1 is a flow chart of a light source mask collaborative optimization method provided in an embodiment of the present application. Figure 6 As shown, the embodiment of the present application further provides a light source mask collaborative optimization method, and the execution subject of the method can be an electronic device, such as a server, etc. The method includes the following steps S201 and S202.

[0126] Step S201 : determining a target silicon wafer result (ie, a desired silicon wafer surface result).

[0127] Step S202 : performing light source mask collaborative optimization based on the target silicon wafer result and the second simulation model to obtain an optimized designed light source shape OSM and an optimized mask.

[0128] The second simulation model is constructed based on the PIS model, the optical exposure model and the photoresist model. The PIS model is obtained by applying any one of the above-mentioned simulation methods of the programmable illumination light source system.

[0129] It can be understood that after obtaining the calibrated SMTM (also known as the PIS model), the PIS model can be used in the SMO to optimize the design light source shape of the SMO optimization algorithm, so that the final SMO optimization result OSM can be directly used as the input of the PIS physical machine. This solves the problem that when IC manufacturers actually use the light source results of the existing SMO as the light source settings of the PIS physical machine, the distortion caused by the processing and clamping constraints of the PIS physical machine further leads to unsatisfactory lithography exposure results, thereby effectively improving the process window.

[0130] In a possible implementation, the second simulation model includes a first-stage simulation model and a second-stage simulation model, the first-stage simulation model is constructed based on the optical exposure model and the photoresist model, and the PIS model serves as the second-stage simulation model;

[0131] Based on the target silicon wafer results and the second simulation model, the light source mask collaborative optimization is performed to obtain the optimized design light source shape OSM and optimized mask, including:

[0132] Based on the target silicon wafer results and the first-stage simulation model, perform light source and mask collaborative optimization to obtain the target RSM and optimized mask;

[0133] Based on the target RSM and the second-stage simulation model, the optimized OSM is obtained through iterative optimization.

[0134] Optionally, based on the target RSM and the second-stage simulation model, an optimized OSM is obtained through iterative optimization, specifically including:

[0135] Input the target OSM into the PIS model to obtain the RSM output by the PIS model. The initial target OSM is the target RSM.

[0136] Compare the difference between the RSM output by the PIS model and the target RSM (the difference can be compared by a pre-configured second-stage objective function), and determine the second-stage difference comparison result;

[0137] When the second-stage difference comparison result indicates that the iteration stop condition is not satisfied, the target OSM is adjusted based on the second-stage difference comparison result; or, when the second-stage difference comparison result indicates that the iteration stop condition is satisfied, the target OSM is determined as the optimized OSM.

[0138] Example 4: Figure 7 This is a schematic diagram of the two-stage SMO process provided in the embodiment of the present application. Figure 7 As shown, the two-stage SMO method divides the entire SMO process into two iterative stages. In the first iterative stage, the existing SMO optimization strategy is used to iteratively optimize the light source shape and mask. The light source optimization result of the first iterative stage is used as the target RSM in the second iterative process. In the second iterative stage, the OSM that can actually be input into the PIS is optimized. Specifically, the SMO method provided in the embodiment of the present application includes the following steps S41 to S43.

[0139] Step S41: Use any of the above PIS simulation methods to obtain SMTM as a PIS model.

[0140] Step S42: perform the first stage iteration, using the existing SMO technology to obtain the optimal light source shape and the optimal mask.

[0141] The light source shape representation method may be selected from but not limited to image representation, or decomposed using approximately complete polynomials as basis functions, such as but not limited to Zernike polynomials, Legendre polynomials, etc.; the mask may be represented by but not limited to pixelation, vertex representation, etc.; the implementation of this application is not limited by the light source shape representation and mask representation methods.

[0142] Step S42 specifically includes the following steps S42.1 to S42.6.

[0143] Step S42.1: Set the initial light source shape and the initial mask shape as the initial target RSM and mask.

[0144] Step S42.2, define the first-stage objective function, which can be defined as the difference between the simulated silicon wafer results and the target silicon wafer results, or the objective function can be defined by increasing the process window as the optimization target; the specific expression of the objective function defined by the difference between the simulated silicon wafer results and the target silicon wafer results, using edge placement error (EPE) or edge profile error (Contour Error) as the evaluation function to measure the quality of graphic correction, EPE is defined as the difference between the designed exposure profile and the simulation at the evaluation point of the target silicon wafer surface result, the smaller the EPE, the closer the graphic after simulated exposure and development is to the designed target graphic; the above-mentioned increase in the process window is used as the optimization target to define the objective function, calculate the process window corresponding to the light source and the mask, that is, to ensure that the mask graphic can be correctly copied to the range of exposure dose and defocus on the silicon wafer, the larger the process window, the better the optimization effect.

[0145] Step S42.3, input the target RSM and mask into the optical exposure model, and use the output of the optical exposure model as the input of the photoresist model, and output the simulated silicon wafer result (that is, the first-stage simulation model is constructed based on the optical exposure model and the photoresist model); the silicon wafer result can be characterized by, but not limited to, the photoresist profile, the characteristic dimensions of each part of the silicon wafer, etc. Correspondingly, different characterization methods correspond to different specific expressions of the objective function; it is worth noting that the optical exposure model and the photoresist model are established and calibrated models, and have the ability to describe the optical exposure process and the photoresist reaction process in the actual exposure and development process. The model establishment and calibration methods can adopt the existing relevant model establishment and calibration methods, and the present application is not restricted by the modeling and calibration methods of the two.

[0146] Step S42.4: Calculate the first-segment objective function according to the definition of the first-stage objective function in step S42.2; determine whether the iteration stop condition is met: if "no", jump to step S42.5; if "yes", jump to step S42.6; the iteration stop condition may be one or more of the following but is not limited to: the objective function value is less than the preset threshold; the number of iterations reaches the preset maximum number of iterations for the first segment.

[0147] Step S42.5. According to the objective function calculated in step S42.4, the existing SMO optimization strategy is used to optimize and adjust the target RSM and mask, and jump to step S42.3; the existing SMO optimization strategy includes but is not limited to: separate optimization of the light source and then separate optimization of the mask, separate optimization of the mask and then separate optimization of the light source, collaborative optimization of the light source and mask, and alternating optimization of the light source and mask. The implementation of this application is not limited by the existing SMO optimization strategy adopted; the specific optimization method in the existing optimization strategy may adopt but is not limited to the least squares method, regularization, convex optimization and other methods. The implementation of this application is not limited by the optimization method adopted.

[0148] Step S42.6: Stop the first iteration and output the target RSM and mask (as the optimized mask) as the optimal light source shape and optimal mask of the first iteration of step S42.

[0149] Step S43, perform the second stage iteration, use the optimal light source shape obtained in step S42 as the target RSM, use the PIS model established in step S41 to solve the OSM, which is the input OSM of the final lithography physical machine. Step S43 specifically includes the following steps S43.1 to S43.6.

[0150] Step S43.1: Use the target RSM obtained in step S42.6 as the initial OSM.

[0151] Step S43.2: Define the second stage objective function. The objective function can be defined as the difference between the simulated RSM and the target RSM. The difference between the two can be evaluated using, but not limited to, the 2-norm.

[0152] Step S43.3: Input the OSM into the SMTM established in step S41 (ie, the PIS model as the second-stage simulation model), and calculate and obtain the output as the simulated RSM.

[0153] Step S43.4: Compare the difference between the simulated RSM obtained in step S43.3 and the target RSM, and calculate the second-segment objective function according to the definition in step S43.2; determine whether the iteration stop condition is met: if "no", jump to step S43.5; if "yes", jump to step S43.6; the iteration stop condition may be one or more of the following but is not limited to: the objective function value is less than the preset threshold; the number of iterations reaches the preset maximum number of iterations for the second segment.

[0154] Step S43.5: Adjust and update the OSM based on the difference between the RSM simulated in step S43.4 and the target RSM, and jump to step S43.3; the OSM can be adjusted and updated by using, but not limited to, optimization methods such as the neural network back propagation method, the least squares method, regularization, and convex optimization.

[0155] Step S43.6: Stop the second iteration and output the OSM as the optimized OSM, which is the light source optimization result of SMO.

[0156] In one possible implementation, the second simulation model consists of a sequentially cascaded PIS model, an optical exposure model, and a photoresist model. Based on the target silicon wafer results and the second simulation model, light source and mask collaborative optimization is performed to obtain an optimized design light source shape OSM and an optimized mask, including:

[0157] Input the target OSM into the PIS model, input the output of the PIS model and the target mask into the optical exposure model, input the output of the optical exposure model into the photoresist model, and obtain the simulated silicon wafer result output by the photoresist model;

[0158] comparing the difference between the simulated silicon wafer result and the target silicon wafer result (the difference may be compared using a pre-configured objective function), and determining a third difference comparison result;

[0159] When the third difference comparison result indicates that the iteration stop condition is not met, the target OSM and the target mask are adjusted based on the third difference comparison result; or, when the third difference comparison result indicates that the iteration stop condition is met, the target OSM is determined as the optimized OSM and the target mask is determined as the optimized mask.

[0160] Example 5: Figure 8 Schematic diagram of the process of SMO including PIS model provided in the embodiment of the present application, such as Figure 8 As shown, the SMO method provided in the embodiment of the present application includes the following steps S51 to S53.

[0161] Step S51: adopt any of the above PIS simulation methods to obtain SMTM as a PIS model.

[0162] Step S52: Establish an SMO framework including the PIS model of step S51, with OSM and mask as optimization targets. The entire framework includes a forward process and a reverse process.

[0163] The forward process of the SMO framework includes: using OSM as the input of SMTM, SMTM output RSM and mask together as the input of the optical exposure model, its output spatial image as the input of the photoresist model, and the output is the simulated silicon wafer result; the OSM characterization method can be selected but not limited to image representation, or the decomposition representation can be performed using approximately complete polynomials as basis functions, and the mask can be represented by pixelation, vertex representation, etc.; it is worth noting that the optical exposure model and the photoresist model are established and calibrated models, which have the ability to describe the optical exposure process and the photoresist reaction process in the actual exposure and development process. The model establishment and calibration methods can adopt the existing relevant model establishment and calibration methods, and this application is not restricted by the modeling and calibration methods of the two.

[0164] The inverse optimization process of the SMO is to adjust the OSM and the mask according to the objective function using optimization strategies and algorithms; the optimization strategy may include but is not limited to optimizing the OSM separately and then optimizing the mask separately, optimizing the mask separately and then optimizing the OSM separately, co-optimizing the OSM and the mask, and alternately optimizing the OSM and the mask; the optimization algorithm may include but is not limited to the least squares method, regularization, convex optimization and other methods.

[0165] Step S53: Perform SMO using an iterative optimization method to obtain the optimal OSM and the optimal mask; specifically, the following steps S53.1 to S53.6 are included.

[0166] Step S53.1, setting an initial light source shape (as an initial target OSM) and an initial mask shape (as an initial target mask); optionally, the initial mask shape can be set to a pattern of the target silicon wafer surface result.

[0167] Step S53.2, define the SMO objective function, which can be defined as the difference between the simulated silicon wafer results and the target silicon wafer results, or the objective function can be defined by increasing the process window as the optimization target; the specific expression of the objective function defined by the difference between the simulated silicon wafer results and the target silicon wafer results, using edge placement error (EPE) or edge profile error (Contour Error) as the evaluation function to measure the quality of graphic correction, EPE is defined as the difference between the designed exposure profile and the simulation at the evaluation point of the target silicon wafer surface result, the smaller the EPE, the closer the graphic after simulated exposure and development is to the designed target graphic; the above-mentioned increase in the process window is used as the optimization target to define the objective function, calculate the process window corresponding to the light source and the mask, that is, to ensure that the mask graphic can be correctly copied to the range of exposure dose and defocus on the silicon wafer, the larger the process window, the better the optimization effect.

[0168] Step S53.3: Input OSM into SMTM, input the output of SMTM and the mask into the optical exposure model, and use the output of the optical exposure model as the input of the photoresist model to output the simulated silicon wafer result.

[0169] Step S53.4, according to the objective function definition in step S53.2 and the simulated silicon wafer results obtained in step S53.3, calculate the objective function (the objective function value is used as the third difference comparison result); determine whether the iteration stop condition is met: if "no", jump to step S53.5; if "yes", jump to step S53.6; the iteration stop condition may be one or more of the following but is not limited to: the objective function value is less than the preset threshold; the number of iterations reaches the preset maximum number of iterations of the first segment.

[0170] Step S53.5: Use the SMO optimization strategy and optimization algorithm to optimize and adjust the OSM and mask (that is, adjust the target OSM and target mask), and jump to step S53.3.

[0171] Step S53.6: Stop iteration, output the OSM (as the optimized OSM) and the mask (as the optimized mask), and the optimized OSM and the optimized mask constitute the SMO optimization result.

[0172] Based on the method in the above embodiment, an embodiment of the present application provides an electronic device, Figure 9 is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application, such as Figure 9 As shown, the electronic device may include: a processor (Processor) 810, a communication interface (Communications Interface) 820, a memory (Memory) 830 and a communication bus 840, wherein the processor 810, the communication interface 820, and the memory 830 communicate with each other via the communication bus 840. The processor 810 can call the logic instructions in the memory 830 to execute the method in the above embodiment.

[0173] In addition, the logic instructions in the above-mentioned memory 830 can be implemented in the form of a software functional unit and can be stored in a computer-readable storage medium when sold or used as an independent product. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or the part of the technical solution can be embodied in the form of a software product, which is stored in a storage medium and includes a number of instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application.

[0174] Based on the method in the above embodiment, an embodiment of the present application provides a computer-readable storage medium, which stores a computer program. When the computer program runs on a processor, the processor executes the method in the above embodiment.

[0175] Based on the method in the above embodiment, an embodiment of the present application provides a computer program product. When the computer program product runs on a processor, the processor executes the method in the above embodiment.

[0176] It is understood that the processor in the embodiments of the present application may be a central processing unit (CPU), or may be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field programmable gate arrays (FPGA), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. The general-purpose processor may be a microprocessor or any conventional processor.

[0177] The method steps in the embodiments of the present application can be implemented by hardware or by a processor executing software instructions. The software instructions can be composed of corresponding software modules, and the software modules can be stored in random access memory (RAM), flash memory, read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), registers, hard disks, mobile hard disks, CD-ROMs or any other form of storage medium well known in the art. An exemplary storage medium is coupled to the processor so that the processor can read information from the storage medium and write information to the storage medium. Of course, the storage medium can also be a component of the processor. The processor and the storage medium can be located in an ASIC.

[0178] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted via the computer-readable storage medium. The computer instructions can be transmitted from one website, computer, server or data center to another website, computer, server or data center via a wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) method. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more available media integrations. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid-state drive (SSD)).

[0179] It will be understood that the various numerical numbers involved in the embodiments of the present application are merely distinctions for the convenience of description and are not intended to limit the scope of the embodiments of the present application.

[0180] It is easy for those skilled in the art to understand that the above is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present application should be included in the scope of protection of the present application.

Claims

1. A method for simulating a programmable lighting source system, characterized in that: include: Based on the designed light source shape OSM sample and actual processing results, the parameters of the light source shape transfer model SMTM in the first simulation model are calibrated; The actual processing result is obtained by inputting an OSM sample into a lithography physical machine and monitoring a processing process of the lithography physical machine, wherein a programmable illumination light source system (PIS) entity is configured in the lithography physical machine, and the first simulation model is used to output a simulation processing result corresponding to the actual processing result, and the first simulation model at least includes the SMTM; The calibrated SMTM is used as a PIS model; the PIS model is used to simulate a PIS entity.

2. The simulation method of a programmable lighting source system according to claim 1, characterized in that: In the case where the actual light source shape RSM output by the PIS entity can be monitored, the actual processing result is the RSM output by the PIS entity, the first simulation model is the SMTM, and the simulation processing result is the RSM output by the SMTM; The calibrating of parameters of the light source shape transfer model SMTM in the first simulation model based on the designed light source shape OSM sample and the actual processing results includes: Continuously optimizing the SMTM based on the OSM sample and the RSM output by the PIS entity until an iteration stop condition is met; The optimizing of the SMTM comprises: Input the OSM sample into the SMTM, and obtain the RSM output by the SMTM; comparing a difference between the RSM output by the PIS entity and the RSM output by the SMTM to determine a first difference comparison result; When the first difference comparison result indicates that the iteration stop condition is not satisfied, the parameters of the SMTM are adjusted based on the first difference comparison result; when the first difference comparison result indicates that the iteration stop condition is satisfied, the calibrated SMTM is determined.

3. The method for simulating a programmable lighting source system according to claim 2, wherein: The SMTM is a neural network model, and comparing the difference between the RSM output by the PIS entity and the RSM output by the SMTM to determine a first difference comparison result includes: Determine a first difference between the RSM output by the PIS entity and the RSM output by the SMTM; evaluate the first difference based on a loss function and determine a loss value as the first difference comparison result; When the first difference comparison result indicates that an iteration stop condition is not satisfied, adjusting the parameters of the SMTM based on the first difference comparison result includes: Based on the loss value, the network neuron weights of the neural network model are adjusted through back propagation.

4. The method for simulating a programmable lighting source system according to claim 1, wherein: In the case where the actual light source shape RSM output by the PIS entity cannot be monitored, the first simulation model includes an SMTM, an optical exposure model, and a photoresist model cascaded in sequence, and the simulation processing result is a simulated silicon wafer result output by the photoresist model; The calibrating of parameters of the light source shape transfer model SMTM in the first simulation model based on the designed light source shape OSM sample and the actual processing results includes: Based on the OSM sample and the actual silicon wafer results output by the lithography physical machine, continuously optimizing the SMTM until an iteration stop condition is met; The optimizing of the SMTM comprises: Inputting the OSM sample into the first simulation model to obtain the simulated silicon wafer result output by the photoresist model; Comparing a difference between an actual silicon wafer result output by the lithography physical machine and a simulated silicon wafer result output by the photoresist model to determine a second difference comparison result; When the second difference comparison result indicates that the iteration stop condition is not satisfied, the parameters of the SMTM are adjusted based on the second difference comparison result; when the second difference comparison result indicates that the iteration stop condition is satisfied, the calibrated SMTM is determined.

5. A light source mask collaborative optimization method, characterized in that: include: Determine target silicon results; Based on the target silicon wafer result and the second simulation model, performing light source mask collaborative optimization to obtain an optimized designed light source shape OSM and an optimized mask; The second simulation model is constructed based on a PIS model, an optical exposure model and a photoresist model, and the PIS model is obtained by applying the simulation method of the programmable illumination light source system according to any one of claims 1 to 4.

6. The light source mask collaborative optimization method according to claim 5, characterized in that: The second simulation model includes a first-stage simulation model and a second-stage simulation model, the first-stage simulation model is constructed based on the optical exposure model and the photoresist model, and the PIS model serves as the second-stage simulation model; The light source mask collaborative optimization is performed based on the target silicon wafer result and the second simulation model to obtain the optimized designed light source shape OSM and the optimized mask, including: Based on the target silicon wafer result and the first-stage simulation model, perform light source mask collaborative optimization to obtain the target RSM and the optimized mask; Based on the target RSM and the second-stage simulation model, an optimized OSM is obtained through iterative optimization.

7. The light source mask collaborative optimization method according to claim 6, characterized in that: The step of obtaining an optimized OSM based on the target RSM and the second-stage simulation model through iterative optimization includes: Input the target OSM into the PIS model to obtain the RSM output by the PIS model, where the initial target OSM is the target RSM; Comparing the difference between the RSM output by the PIS model and the target RSM to determine the second-stage difference comparison result; When the second-stage difference comparison result indicates that the iteration stopping condition is not satisfied, the target OSM is adjusted based on the second-stage difference comparison result; or, when the second-stage difference comparison result indicates that the iteration stopping condition is satisfied, the target OSM is determined as the optimized OSM.

8. The light source mask collaborative optimization method according to claim 5, characterized in that: The second simulation model is composed of the PIS model, the optical exposure model, and the photoresist model, which are cascaded in sequence. The light source and mask collaborative optimization is performed based on the target silicon wafer result and the second simulation model to obtain the optimized designed light source shape OSM and the optimized mask, including: Inputting the target OSM into the PIS model, inputting the output of the PIS model and the target mask into the optical exposure model, inputting the output of the optical exposure model into the photoresist model, and obtaining the simulated silicon wafer result output by the photoresist model; comparing a difference between the simulated silicon wafer result and the target silicon wafer result to determine a third difference comparison result; When the third difference comparison result indicates that the iteration stop condition is not satisfied, the target OSM and the target mask are adjusted based on the third difference comparison result; or, when the third difference comparison result indicates that the iteration stop condition is satisfied, the target OSM is determined as the optimized OSM and the target mask is determined as the optimized mask.

9. An electronic device, characterized in that: include: at least one memory for storing a computer program; At least one processor is used to execute the program stored in the memory. When the program stored in the memory is executed, the processor is used to execute the method according to any one of claims 1 to 4 or the method according to any one of claims 5 to 8.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program runs on a processor, the processor is enabled to execute the method according to any one of claims 1 to 4 or the method according to any one of claims 5 to 8.

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