Methods, apparatus, media, and program products for lithography modeling

Abstract

A method, device, medium and program product for lithography modeling are provided. The method comprises: determining graphic feature information corresponding to at least one region in a design pattern, the graphic feature information indicating a graphic width and a graphic pitch in the corresponding region; determining mask bias information corresponding to the at least one region based on the graphic feature information; generating a mask image corresponding to the design pattern based on the mask bias information corresponding to the at least one region; and performing lithography modeling based on the mask image.

Description

Technical Field

[0001] The examples in this article generally relate to the field of semiconductor technology, and in particular to methods, apparatus, media, and program products for photolithography modeling. Background Technology

[0002] With the continuous development of semiconductor manufacturing technology, high-precision photolithography has become a crucial step in integrated circuit manufacturing. To achieve accurate imaging of high-density microstructures, the design pattern needs to be projected onto the wafer through a mask during the photolithography process to form an actual mask pattern. Photolithography modeling technology can simulate the imaging effect of the design pattern on the wafer during the design phase, thus providing a basis for subsequent process optimization. Through photolithography modeling, it is possible to predict the dimensional deviations and shape changes that may occur in the design pattern during actual manufacturing, thereby improving manufacturing accuracy and product consistency. Summary of the Invention

[0003] In a first aspect, a method for photolithographic modeling is provided. The method includes: determining graphic feature information corresponding to at least one region in a design pattern, the graphic feature information indicating the graphic width and graphic spacing in the corresponding region; determining mask deviation information corresponding to the at least one region based on the graphic feature information; generating a mask image corresponding to the design pattern based on the mask deviation information corresponding to the at least one region; and performing photolithographic modeling based on the mask image.

[0004] In a second aspect, an electronic device is provided. The device includes at least one processor; and at least one memory coupled to the at least one processor and storing instructions for execution by the at least one processor. When executed by the at least one processor, the instructions cause the device to perform the method of the first aspect.

[0005] In a third aspect, a computer-readable storage medium is provided. The computer-readable storage medium stores computer-executable instructions that can be executed by a processor to implement the method of the first aspect.

[0006] In a fourth aspect, a computer program product is provided, which is tangibly stored in a computer storage medium and includes computer-executable instructions that, when executed by a device, cause the device to perform the method of the first aspect.

[0007] In this way, the compensation for mask deviation can be dynamically changed with the pattern width and pattern spacing, thereby improving the accuracy of mask deviation prediction and the simulation accuracy of lithography modeling.

[0008] It should be understood that the content described in this section is not intended to limit the key or important features of the examples in this article, nor is it intended to restrict the scope of the solution. Other features will become readily apparent from the following description. Attached Figure Description

[0009] The above and other features, advantages, and aspects of the various examples herein will become more apparent when taken in conjunction with the accompanying drawings and the following detailed description. In the accompanying drawings, the same or similar reference numerals denote the same or similar elements, wherein: Figure 1 A schematic diagram of the example environment is shown; Figure 2 A schematic diagram of mask deviation information for some embodiments is shown; Figure 3 Flowcharts of example processes for photolithography modeling in some embodiments are shown; Figure 4A A schematic diagram illustrating the process of generating a mask image in some embodiments is shown; Figure 4B A schematic diagram illustrating the process of generating a 3D mask image in some embodiments is shown; Figure 4C A schematic diagram illustrating the process of generating light intensity distribution images in some embodiments is shown; Figure 4D A schematic diagram of at least one photoresist image from some embodiments is shown; Figure 4E Schematic diagrams illustrating the process of determining a photolithographic model in some embodiments are shown; and Figure 5 A block diagram of an electronic device capable of implementing multiple illustrative scenarios is shown. Detailed Implementation

[0010] The examples in this document will now be described in more detail with reference to the accompanying drawings. While some examples are shown in the drawings, it should be understood that solutions can be implemented in various forms and should not be construed as limited to the examples presented herein. Rather, these examples are provided to provide a more thorough and complete understanding of the solutions. It should be understood that the drawings and examples in this document are for illustrative purposes only and are not intended to limit the scope of protection of the solutions.

[0011] It should be noted that the headings of any section / subsection provided herein are not restrictive. Various examples are described throughout this document, and examples of any type may be included under any section / subsection. Furthermore, examples described in any section / subsection may be combined in any way with any other examples described in the same section / subsection and / or different sections / subsections.

[0012] In the description of the examples in this document, the term "including" and similar terms should be understood as open inclusion, i.e., "including but not limited to". The term "based on" should be understood as "at least partially based on". The term "an example" or "the example" should be understood as "at least one example". The term "some examples" should be understood as "at least some examples". Other explicit and implicit definitions may also be included below. The terms "first", "second", etc., may refer to different or the same objects. Other explicit and implicit definitions may also be included below.

[0013] The examples in this document may involve user data, data acquisition, and / or use. All of these aspects comply with relevant laws, regulations, and rules. In the examples, all data collection, acquisition, processing, manipulation, forwarding, and use are conducted with the user's knowledge and confirmation. Accordingly, when implementing each example, the type, scope of use, and usage scenarios of any data or information that may be involved should be communicated to the user and their authorization obtained through appropriate means, in accordance with relevant laws and regulations. The specific methods of notification and / or authorization can vary depending on the actual situation and application scenario; the scope of the solution is not limited in this regard.

[0014] In this manual and the sample solutions, any processing of personal information will be conducted only under legal grounds (such as obtaining the consent of the data subject or being necessary for the performance of a contract) and will only be carried out within the scope stipulated or agreed upon. A user's refusal to process personal information beyond what is necessary for basic functions will not affect the user's use of basic functions.

[0015] As used herein, the term "design pattern" refers to a pattern characterized by layout data obtained during the integrated circuit design phase for forming patterns on a mask and / or wafer. This pattern may include, but is not limited to, polygonal graphics, line graphics, and combinations thereof. In some examples, a design pattern may also be referred to as a design graphic or design layout. As used herein, the term "actual mask pattern" refers to a pattern actually formed or intended to be formed on a photomask, corresponding to the design pattern, and may differ in size from the design pattern.

[0016] The term "critical dimension (CD)" as used in this article refers to the feature dimension of the graphic of interest in a pattern, typically including but not limited to the linewidth of the graphic. The term "lithographic modeling" as used in this article refers to the process of simulating the pattern imaging process in a lithography process to determine a lithographic model, such as the process of simulating the pattern formed on a wafer by designing a pattern.

[0017] The term “Optical Proximity Effect (OPE)” as used in this article refers to the effect in the exposure process where the pattern formed on the wafer is offset or deformed relative to the design pattern due to factors such as optical diffraction.

[0018] The term “Optical Proximity Correction (OPC)” used in this article refers to a technique for pre-compensating design patterns to correct deviations caused by optical proximity effects, and photolithography modeling can be one of the steps in this process.

[0019] As mentioned above, during photomask manufacturing, due to limitations in manufacturing precision, there is often a deviation between the actual produced mask's critical dimensions and the designed critical dimensions—a phenomenon known as mask deviation. Furthermore, due to the optical proximity effect, mask deviation may be further amplified in subsequent exposure processes, leading to a larger offset of the critical dimensions of the pattern on the wafer relative to the design value. If this offset is too large, it may cause problems such as short circuits, open circuits, or electrical parameter drift in the circuitry, thereby affecting device performance and yield.

[0020] To correct the aforementioned deviations, optical proximity correction (OPC) techniques are typically used to pre-compensate the design pattern, a crucial step of which is photolithographic modeling. In some schemes, the photolithographic simulation model uses fixed compensation values ​​to correct the main pattern layer and the auxiliary pattern layer. However, there may be complex nonlinear relationships between mask deviation and pattern width and pattern spacing, making it difficult for fixed compensation values ​​to accurately reflect the actual deviations in different pattern regions, thus limiting the model's prediction accuracy. Furthermore, if the pattern density differs on both sides of the target pattern, the mask deviations on both sides should also differ, and the scheme using the same compensation value is ill-suited to such asymmetrical pattern structures.

[0021] In view of this, a scheme for photolithography modeling is proposed. According to this scheme, graphic feature information corresponding to at least one region in the design pattern is determined, indicating the pattern width and pattern spacing within the corresponding region; based on this graphic feature information, using an association model, mask deviation information corresponding to the at least one region is determined, indicating the dimensional difference between the actual mask pattern and the design pattern; based on the mask deviation information corresponding to the at least one region, a mask image corresponding to the design pattern is generated; and photolithography modeling is performed based on this mask image to simulate the pattern formed by the design pattern on the wafer.

[0022] By employing the above scheme, the correlation between mask deviation and the graphic environment is established using graphic feature information such as graphic width and graphic spacing. This allows for the determination of corresponding mask deviation information for different graphic regions, thereby avoiding the problem of inaccurate deviation representation caused by using a uniform compensation value. Furthermore, by incorporating the determined mask deviation information into the mask image generation process and performing photolithography modeling based on the mask image, the simulation accuracy of the photolithography model for the actual mask pattern and actual imaging results can be improved, thus enhancing the accuracy of the photolithography modeling results.

[0023] The following describes various examples of this scheme in further detail with reference to the accompanying drawings.

[0024] Figure 1 A schematic diagram of example environment 100 is shown. (e.g.) Figure 1 As shown, example environment 100 may include electronic device 110.

[0025] In this example environment 100, electronic device 110 can acquire design layout data 120 as input, process the design layout data 120, and output the calculation result 130. The design layout data 120 can represent the design pattern to be processed, and the calculation result 130 can include, but is not limited to, mask deviation information, mask images, and / or lithography models. Electronic device 110 can run applications that support lithography modeling, and users can trigger or configure the lithography modeling process via electronic device 110 and / or its attached devices.

[0026] Electronic device 110 can be any type of mobile terminal, fixed terminal, or portable terminal, including mobile phones, desktop computers, laptop computers, notebook computers, netbook computers, tablet computers, media computers, multimedia tablets, handheld computers, portable gaming terminals, VR / AR devices, personal communication system (PCS) devices, personal navigation devices, personal digital assistants (PDAs), audio / video players, digital cameras / camcorders, positioning devices, television receivers, radio receivers, e-book devices, gaming devices, or any combination thereof, including accessories and peripherals of these devices or any combination thereof. In some embodiments, electronic device 110 can also support any type of user-facing interface (such as "wearable" circuitry).

[0027] In some embodiments, the electronic device 110 can also be implemented as an independent physical server, or a server cluster or distributed system composed of multiple physical servers. It can also be implemented as a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content delivery networks, and big data and artificial intelligence platforms. That is, the lithography modeling process described herein can be executed locally by the electronic device 110 as a terminal, or by the electronic device 110 as a server, or jointly by the terminal and the server. For example, the terminal determines the graphic feature information and sends it to the server via a communication connection; the server determines the mask deviation information, performs lithography modeling, and returns the calculation result 130 to the terminal. The communication connection can be established via wired or wireless means, and can include, but is not limited to, Bluetooth connections, mobile network connections, Universal Serial Bus (USB) connections, Wireless Fidelity (WiFi) connections, etc., without limitation in this regard.

[0028] It should be understood that the structure and function of the various elements in environment 100 are described for illustrative purposes only and do not imply any limitation on the scope of the scheme.

[0029] The following description of the example will continue with reference to the accompanying drawings.

[0030] Figure 2 A schematic diagram 200 illustrating mask deviation information according to some embodiments is shown. For example... Figure 2 As shown, design pattern 202 represents the target graphic defined in the layout design stage, and actual mask pattern 204 represents the actual graphic on the mask manufactured according to design pattern 202. During the photomask manufacturing process, due to factors such as process errors, manufacturing precision, and the graphic environment, the size of the actual mask pattern 204 may differ from the size of the design pattern 202. For example... Figure 2 As shown, this difference can be represented as mask deviation information 203, which can indicate the dimensional offset of the actual mask pattern 204 relative to the design pattern 202.

[0031] In some embodiments, the mask deviation information 203 may be the same or different for different sides of the same pattern. For example, if the pattern density is different on both sides of the pattern, the mask deviation information 203 on both sides may be different from each other, such as... Figure 2The two mask deviation information 203 located on either side of the pattern are shown. In this paper, the mask deviation information 203 can be associated with local regions in the design pattern, thereby allowing the corresponding mask deviation information to be determined separately for different regions. Subsequently, based on the graphic feature information corresponding to each region, the mask deviation information corresponding to each region can be determined separately, and a mask image for photolithography modeling can be generated based on the determined mask deviation information.

[0032] The term "mask bias" as used in this article refers to the dimensional difference between the actual mask pattern and the design pattern, such as the offset of the critical dimension of the actual pattern formed on the mask relative to the design critical dimension. Mask bias information can refer to this dimensional difference, and the mask bias can be the same or different for different sides of the same pattern.

[0033] Figure 3 A flowchart of an example process 300 for photolithographic modeling according to some embodiments is shown. Process 300 can be implemented at electronic device 110. Reference is made below. Figure 1 To describe process 300.

[0034] like Figure 3 As shown in box 310, electronic device 110 can determine graphic feature information corresponding to at least one region in the design pattern, the graphic feature information indicating the graphic width and graphic spacing in the corresponding region.

[0035] In some embodiments, the design pattern may be obtained by the electronic device 110 from the design layout data 120. At least one region may include one or more regions in the design pattern to be analyzed, compensated, or modeled. For example, the one or more regions may correspond to a layout graphic in the design pattern, or the boundary position of the graphic, edge segment, line segment region, corner region, or other local region that needs to be photolithographically modeled.

[0036] In some embodiments, at least one region may correspond to at least one graphic edge segment in the design pattern, and the mask deviation information may include at least one mask deviation value corresponding to each of the at least one graphic edge segment. A graphic edge segment may refer to a partial segment constituting the graphic boundary in the design pattern. For example, for a rectangular graphic, a graphic edge segment may correspond to at least a partial segment of each side of the rectangle. For a polygonal graphic, a graphic edge segment may correspond to at least a partial segment of a straight boundary, at least a partial segment of a curved boundary, or at least a partial segment of a boundary segment composed of multiple adjacent boundaries within the polygonal boundary.

[0037] In some embodiments, the graphic width may include the critical dimension (CD) of the graphic corresponding to at least one region, and the graphic spacing may include the space between the graphic corresponding to the region and its neighboring graphics. For example, for a region corresponding to an edge segment, the electronic device 110 may determine the critical dimension (CD) of the graphic to which the edge segment belongs at that location, and determine the distance between the nearest neighboring graphic in the direction in which the edge segment is oriented and the edge segment, as the graphic width and graphic spacing corresponding to the region, respectively.

[0038] In block 320, electronic device 110 can determine mask deviation information corresponding to at least one region based on graphic feature information. For example, electronic device 110 can determine mask deviation information corresponding to at least one region based on graphic feature information using an association model. The mask deviation information indicates the dimensional difference between the actual mask pattern corresponding to the design pattern and the design pattern. In some embodiments, electronic device 110 can substitute graphic width and graphic spacing as independent variables into the association model to output mask deviation information for each region, thereby making the mask deviation information of each region vary with its graphic width and graphic spacing.

[0039] For example, for the first target region and the second target region corresponding to different edge segments of the same graphic in the design pattern, if the corresponding graphic widths, graphic spacings, or both are different, the association model can output different mask deviation information. Thus, the electronic device 110 can determine the corresponding mask deviation information for different regions corresponding to different edge segments of the graphic in the design pattern, without needing to apply a uniform size compensation to the entire design pattern. This improves the accuracy of mask deviation characterization, thereby enhancing the accuracy of subsequent photolithography modeling.

[0040] The term "association model" as used in this paper refers to a model used to establish the correlation between mask deviation information and graphic feature information, such as a model used to establish a nonlinear correlation between mask deviation information and graphic width and graphic spacing. An association model may typically include, but is not limited to, one or more association terms and the parameters of those terms.

[0041] In some embodiments, the correlation model can be used to establish a nonlinear correlation between mask deviation information and graphic width and graphic spacing. Specifically, the correlation model can use graphic width and graphic spacing to jointly represent mask deviation information, so that different size features and different graphic environments correspond to different mask deviation information.

[0042] In the embodiments of this disclosure, by establishing a nonlinear correlation between pattern width, pattern spacing, and mask deviation information, the mask deviation information can be dynamically changed with the changes in pattern structural features, thereby improving the ability to characterize changes in the actual mask pattern size. Furthermore, since different regions can have their corresponding mask deviation information determined separately, local differences in different regions of the design pattern can be characterized, thereby improving the accuracy of subsequent photolithography modeling.

[0043] In some embodiments, the correlation model may include at least one correlation term and parameters for at least one correlation term. The at least one correlation term may include at least one of the following: a first correlation term representing an inverse proportional relationship between graphic width and mask deviation information; a second correlation term representing an inverse proportional relationship between graphic proximity effect related to graphic spacing and mask deviation information; a third correlation term representing a nonlinear relationship between graphic width and mask deviation information; and a fourth correlation term representing a nonlinear relationship between graphic spacing and mask deviation information.

[0044] The first correlation term can be used to characterize the trend of mask deviation change caused by the decrease of graphic width; the second correlation term can be used to characterize the influence of neighboring graphics caused by the decrease of graphic spacing; the third and fourth correlation terms can be used to characterize the nonlinear effects of graphic width and graphic spacing on mask deviation information.

[0045] In some embodiments, the third and fourth correlation terms may be implemented using logarithmic, exponential, polynomial, or other functions that can characterize nonlinear relationships, and this disclosure does not limit them.

[0046] In this way, the first and second correlation terms characterize the basic influence of graphic size and neighboring graphic environment on mask deviation information, and the third and fourth correlation terms further characterize the nonlinear influence of related factors, thereby enabling a more comprehensive description of the correlation between graphic width, graphic spacing, graphic proximity effect and mask deviation information.

[0047] In some embodiments, the electronic device 110 can construct an association model using the following formula: (1), in, Indicates mask deviation information, first correlation term The second correlation term is used to represent the inverse proportional relationship between graphic width and mask deviation information. The third correlation term is used to represent the inverse proportional relationship between the proximity effect of pattern spacing and mask deviation information. The fourth correlation term is used to represent the non-linear relationship between the graphic width and mask deviation information. This is used to represent the nonlinear relationship between the pattern spacing and the mask deviation information; x represents the pattern width, y represents the pattern spacing, a, b, m, and n represent the parameters of the corresponding correlation terms, and k represents the constant term.

[0048] In some embodiments, the first and second correlation terms are used to characterize the fundamental influence trends of graphic size and adjacent graphic environment on mask deviation information. For example, when the graphic width or graphic spacing decreases, the numerical change of the corresponding correlation term increases, thereby reflecting the influence of small-sized graphics and high-density graphic environment on mask deviation information. The third and fourth correlation terms are used to further characterize the nonlinear variation characteristics between graphic width and graphic spacing and mask deviation information, thereby improving the fitting ability of the correlation model to the actual mask deviation variation law.

[0049] As an example, the value of parameter a can be between -100 and 0, the value of parameter b can be between 0 and 100, the values ​​of parameters m and n can be between 0 and 1 respectively, and the value of parameter k can be between -5 and 5. It should be understood that the above value ranges are merely examples, and other suitable values ​​can be used in other examples.

[0050] In some embodiments, the above parameters can be obtained through a parameter fitting process based on actual mask measurement data. For example, parameters a, b, m, n, and k can be solved using sample data containing pattern width, pattern spacing, and corresponding true mask deviations, so that the error between the mask deviation information output by the correlation model and the actual measurement results meets predetermined requirements.

[0051] In this way, by simultaneously introducing inverse proportional and logarithmic terms related to the width of the pattern, as well as inverse proportional and logarithmic terms related to the spacing of the pattern, the influence of pattern size features and pattern environment features on mask deviation information can be characterized from multiple dimensions. This improves the ability of the correlation model to describe the actual mask deviation variation law and enhances the simulation accuracy of the actual imaging results in the subsequent photolithography modeling process.

[0052] In some embodiments, the electronic device 110 can construct an association model using the following formula: (2), Wherein, Mask Bias represents mask bias information; x represents the pattern width; y represents the pattern spacing; a, b, m, and n represent the parameters of the corresponding correlation terms; and k represents a constant term. For explanations of each parameter and each correlation term, please refer to the relevant description in formula (1). The base of the logarithmic function term can be any positive number, such as 2, 3, 5, or 10.

[0053] In some embodiments, different logarithmic bases can be selected based on different process nodes, different graphic structures, or different sample data distributions, and fitted in combination with corresponding parameters to obtain an association model that meets the accuracy requirements. In this way, by extending the natural logarithm function to a logarithm function with an adjustable base, the flexibility of the association model can be improved, enabling the association model to adapt to the correlation rules between mask deviation and graphic width and graphic spacing under different scenarios, thereby improving the fitting ability of the association model to actual measurement data.

[0054] In some embodiments, the electronic device 110 can construct an association model using the following formula: (3), Mask Bias represents mask bias information. Indicates the width of the graphic. Indicates the spacing between graphics. , These represent the parameters of the association model. Represents a constant term. and These represent the first and second correlation terms, respectively. Compared with the correlation model shown in formula (1), the correlation model shown in formula (3) omits the logarithmic function term used to characterize the nonlinear relationship, thus enabling the determination of mask bias information with less computational resources. In some application scenarios where the computational efficiency of the model is high or the fitting accuracy is relatively low, the correlation model shown in formula (3) can be used.

[0055] In this way, by preserving the basic relationships related to graphic width and graphic spacing, while reducing the number of relationship terms in the relationship model, the complexity of the model can be reduced while ensuring a certain modeling capability, thereby reducing the consumption of computational resources in the parameter fitting and model inference process.

[0056] In some embodiments, the correlation model may further include a fifth correlation term, which represents a linear correlation between the pattern width and mask deviation information in a pattern region smaller than a first threshold size. As an example, the first threshold size may correspond to a pattern region with a mask critical size of less than about 25 nanometers or a wafer critical size of less than about 100 nanometers.

[0057] In some embodiments, the electronic device 110 can construct an association model using the following formula: (4), in, This indicates mask deviation information. Represents the fifth function term. The second association term is represented by x, the width of the graphic is represented by y, the spacing between the graphic is represented by a, the parameter of the fifth association term is represented by b, the parameter of the second association term is represented by k, and the constant term is represented by k.

[0058] In some embodiments, when the graphic size is reduced to a predetermined size range, the relationship between the graphic width and the mask deviation information can be approximately characterized as a linear relationship. Therefore, the fifth correlation term can be used to describe the correlation between the graphic width and the mask deviation information, and the influence of the second correlation term on the adjacent graphic environment can be characterized.

[0059] In this way, in small-sized graphic regions, the relationship between graphic width and mask deviation information can be approximated using linear correlation terms. While retaining the influence of graphic proximity effect, the number of function terms in the correlation model is further reduced, thereby reducing model complexity and computational resource consumption, and improving the applicability of the correlation model within the corresponding size range.

[0060] In some embodiments, the correlation model may further include a sixth correlation term, which comprises a quadratic function term centered at a predetermined width threshold. The sixth correlation term represents the degree of influence of the graphic width on mask deviation information in graphic regions smaller than a second threshold size. The predetermined width threshold corresponds to the graphic width at which mask edge coupling effects begin to significantly affect mask deviation information.

[0061] In some embodiments, as the pattern size decreases to a predetermined range, the influence of the mask 3D effect on the light propagation process gradually increases. Specifically, the light field distribution near the mask edge is affected not only by the current edge but also by neighboring edges, resulting in an edge coupling effect. Under this effect, the relationship between the pattern width and mask deviation information is no longer suitable for description using a simple linear relationship and may exhibit a nonlinear trend.

[0062] In some embodiments, by analyzing and fitting actual measurement data, it was found that when the graphic width is close to a predetermined width threshold, the influence of the graphic width on the mask deviation information exhibits an approximately quadratic variation law. Therefore, this influence relationship can be described using a quadratic function term centered on the predetermined width threshold.

[0063] In some embodiments, the electronic device 110 can construct an association model using the following formula: (5), in, This indicates mask deviation information. This indicates the sixth related item, with a predetermined width threshold of 100. denoted as the second association term, x represents the graphic width, y represents the graphic spacing, a represents the parameter of the sixth association term, b represents the parameter of the second association term, and k represents the constant term.

[0064] In some embodiments, the predetermined width threshold 100 may correspond to a pattern width of 100 nanometers. For patterns smaller than this size range, the influence of the pattern width on mask deviation information changes significantly due to the increasingly pronounced mask edge coupling effect; therefore, the aforementioned quadratic function term can be used for characterization. In other embodiments, the predetermined width threshold may also be adjusted based on specific process conditions, mask structure, or fitting results of measurement data; this disclosure does not limit this.

[0065] In this way, by introducing a quadratic function term centered on a predetermined width threshold, the nonlinear variation trend caused by the three-dimensional effect of the mask and the edge coupling effect in small-sized graphic regions can be characterized, thereby improving the ability of the correlation model to describe the mask deviation variation law within the relevant size range and improving the consistency between the subsequent lithography modeling results and the actual exposure results.

[0066] In some embodiments, the association model can be constructed by: acquiring multiple sets of sample data, the multiple sets of sample data including multiple ground truth mask biases and graphic widths and graphic spacings corresponding to the multiple ground truth mask biases; determining at least one association term included in the association model; and determining at least one parameter corresponding to at least one association term based on the multiple sets of sample data.

[0067] In some embodiments, multiple sets of sample data can be derived from actual measurement data after the photomask manufacturing is completed. For example, actual measurement results under different pattern positions, pattern types, pattern densities, and pattern sizes can be obtained, and the corresponding true mask deviation can be determined. The true mask deviation can represent the dimensional offset of the actual mask pattern relative to the design pattern. The term "true mask deviation" as used herein refers to the mask deviation obtained based on measurement or other methods and used as a reference true value, which can be used to construct an association model.

[0068] In some embodiments, after obtaining multiple sets of sample data, statistical analysis can be performed on the sample data to determine the target factors affecting mask bias. For example, the correlation between pattern width, pattern spacing, and pattern proximity effect and mask bias can be analyzed, and the correlation terms included in the correlation model can be determined accordingly.

[0069] In some embodiments, different combinations of association terms can be selected according to the actual application scenario. For example, an association model including a first association term, a second association term, a third association term, and a fourth association term can be selected; alternatively, other combinations of association terms can be selected to construct the association model based on the target size range, computational resource requirements, or fitting accuracy requirements.

[0070] In some embodiments, to further improve the accuracy of mask deviation prediction while maintaining computational efficiency, the electronic device can differentiate the associated model according to the characteristics of different regions or different graphic types in the design drawing. For example, the electronic device 110 can classify multiple sets of sample data based on at least one graphic category corresponding to multiple sets of sample data to obtain at least one sample dataset corresponding to at least one graphic category.

[0071] As used herein, the term "graphic category" refers to a category classified according to the structural characteristics, graphic density, functional attributes, or layout purpose of a design pattern. For example, a graphic category may include memory cell areas, logic areas, analog areas, radio frequency areas, interconnect areas, filled graphic areas, and boundary areas. Other suitable classification methods may be used in other embodiments, and this disclosure does not limit such methods.

[0072] Furthermore, for each of the at least one graphic category, the electronic device 110 can determine, based on at least one sample dataset, at least one association term and / or at least one parameter included in the association model corresponding to that graphic category. In other words, different graphic categories can use the same form of association model but correspond to different parameter sets, or they can use an association model that includes different association terms, or the association terms and corresponding parameter sets are both different.

[0073] In some embodiments, the structural features of graphics differ across different graphic categories, thus the correlation between mask bias and graphic width and spacing may also differ. For example, the mask bias variation patterns for high-density periodic graphics and low-density random graphics may differ; similarly, the mask bias variation patterns for large-size graphics and small-size graphics may also differ. Therefore, corresponding correlation models can be constructed for different graphic categories to improve the accuracy of mask bias prediction.

[0074] As an example, for the memory cell region, since it typically includes highly periodic small-sized patterns with high and relatively uniform pattern density, a simplified correlation model can be used, such as the correlation model shown in formula (3). For the logic region, since it typically includes various pattern structures such as standard cells and random wiring, and the pattern density varies greatly, the correlation model shown in formula (1) or formula (2) can be used to improve the representation capability of different pattern environments. For the analog region or radio frequency region, since it typically includes large-sized patterns and has high requirements for pattern symmetry, a globally fixed compensation value can be used as mask deviation information to reduce the amount of computation.

[0075] For interconnect regions, since they typically include long line structures, structures with varying widths, and branch structures, the correlation model shown in formula (1) or formula (2) can be used to improve the characterization of pattern density variations and proximity effects. For filled pattern regions, since they typically include filled patterns to meet process density requirements and have high pattern density, the correlation model shown in formula (1) or formula (2) can also be used. For boundary regions or dicing slot regions, since they typically include test structures, alignment marks, and other auxiliary patterns, and the patterns are relatively sparse, a globally fixed compensation value can be used, or mask deviation compensation processing can be omitted.

[0076] It should be understood that the above correspondence between graphic categories and association models is merely an example. In other embodiments, other association items and combinations thereof may be used for different graphic categories, or the same association items may be used but with different parameters configured; this disclosure does not limit this.

[0077] After determining the structure of the correlation model, the parameters corresponding to each correlation term can be determined based on multiple sets of sample data. For example, the parameters in the correlation model can be solved using the least squares method, nonlinear regression method, numerical optimization method, or other parameter fitting method, so that the error between the mask deviation information output by the correlation model and the true mask deviation in the sample data meets predetermined requirements. In some embodiments, when new measurement data is obtained or the mask corresponding to different process conditions is changed, at least one parameter can be re-determined using the new sample data to update the correlation model, so that the correlation model adapts to the mask deviation characteristics under the corresponding process conditions.

[0078] In this way, by utilizing actual measurement data to determine the correlation terms and their corresponding parameters in the correlation model, the correlation model can reflect the correlation between actual mask deviation and pattern width and pattern spacing. Furthermore, by uniformly determining model parameters based on sample data, the iterative process required for manually adjusting compensation values ​​can be reduced, improving the efficiency of correlation model construction and simultaneously enhancing the accuracy of simulating actual exposure results in subsequent lithography modeling processes.

[0079] In some embodiments, determining mask deviation information may include: inputting the graphic width and graphic spacing into an association model for each region in at least one region; and determining the mask deviation information for that region based on the output of the association model. For example, electronic device 110 may determine the corresponding graphic width and graphic spacing for each region to be corrected in a design pattern, and input the corresponding graphic width and graphic spacing into the association model. The association model may output the corresponding mask deviation information based on the input graphic width and graphic spacing, thereby determining the dimensional deviation corresponding to each region.

[0080] In some embodiments, the graphic width, graphic spacing, or neighboring graphic environment may differ for different regions, thus the mask deviation information output by the association model may also differ. For example, for different boundary regions of the same graphic, different mask deviation information can be obtained because their corresponding neighboring graphic distributions are different.

[0081] In some embodiments, the electronic device 110 may determine a graphic category corresponding to at least one region; determine at least one association item included in the association model and / or at least one parameter corresponding to the at least one association item based on the graphic category; and determine mask deviation information corresponding to at least one region based on the determined at least one association item and at least one parameter.

[0082] In some embodiments, different graphic categories may correspond to different association model configurations. As an example, different graphic categories may correspond to different combinations of association terms; or, different graphic categories may use the same combination of association terms but correspond to different parameter groups; or, different graphic categories may use both different combinations of association terms and different parameter groups.

[0083] In some embodiments, the electronic device 110 can select a target association model from a plurality of pre-configured association models, or select a target parameter group corresponding to the graphic category from a plurality of parameter groups, based on the determined graphic category. Subsequently, the electronic device 110 can input the graphic width and graphic spacing of the corresponding region into the determined association model to output the corresponding mask deviation information.

[0084] In this way, by introducing pattern category information into the mask deviation determination process, the correlation model can adopt a more suitable modeling strategy based on the structural features, pattern environment, and process characteristics of different types of patterns, thereby improving the accuracy of mask deviation prediction. Furthermore, correlation items and parameter groups can be flexibly configured for different pattern categories, reducing unnecessary computational overhead and improving lithography modeling efficiency while ensuring modeling accuracy.

[0085] In some embodiments, the mask deviation information described above can be determined, statistically analyzed, or applied to graphic objects at different levels. For example, at a single graphic level, the electronic device 110 can determine the corresponding mask deviation information for one or more graphic edge segments in a single graphic, and perform photolithography modeling based on the determined mask deviation information. Taking transistor gate patterns, interconnect patterns, etc., as examples, the determined mask deviation information can be used to analyze the dimensional changes of the corresponding patterns.

[0086] For example, at the local location level, the electronic device 110 can determine mask deviation information for specific locations of a graphic. These specific locations may include graphic corners, line ends, the boundary between dense and sparse graphics, locations where the spacing between adjacent graphics changes, or other locations with significant changes in local structure. Since the graphic width, spacing, or adjacent graphic environment may differ at different locations, the determined mask deviation information may also differ.

[0087] For example, at the regional statistical level, the electronic device 110 can perform statistical analysis on the target area, target layout area, or multiple graphic areas in the design pattern based on the mask deviation information corresponding to multiple graphic edge segments, in order to obtain the mask deviation distribution of the corresponding areas. Furthermore, the obtained statistical results can be used to analyze the dimensional consistency or process stability between different areas.

[0088] Return to reference Figure 3 In box 330, electronic device 110 can adjust the graphic boundary corresponding to at least one region in the design pattern based on mask deviation information corresponding to at least one region, to generate a mask image corresponding to the design pattern. The term "mask image (MI)" as used herein refers to an image corresponding to the design pattern used for photolithographic modeling, such as an image obtained by discretizing the pixels of a modified design pattern.

[0089] Figure 4A A schematic diagram illustrating a process for generating a mask image according to some embodiments is shown. In some embodiments, determining the mask image may include: adjusting the graphic boundary corresponding to at least one region in a design pattern based on mask deviation information; and generating the mask image based on the modified design pattern.

[0090] like Figure 4A As shown, the electronic device 110 can determine the graphic width and graphic spacing corresponding to multiple regions to be compensated in the design pattern 411, and input them into the association model to obtain the mask deviation information corresponding to each region. Subsequently, the electronic device 110 can adjust the position of the graphic boundary corresponding to the design pattern according to the mask deviation information, thereby obtaining the corrected design pattern 412. After obtaining the corrected design pattern 412, the electronic device 110 can perform discrete meshing processing on the corrected design pattern 412, converting the continuous graphic into a discrete pixel representation, thereby obtaining the mask image 413.

[0091] In this way, by correcting the graphic boundaries in the design pattern based on the mask deviation information corresponding to each region, the mask image can represent the size change characteristics corresponding to different regions, thereby improving the fitting degree of the mask image to the actual mask pattern and improving the accuracy of subsequent photolithography modeling.

[0092] Figure 4B A schematic diagram illustrating a process for generating a three-dimensional mask image according to some embodiments is shown. In some embodiments, determining the mask image for a three-dimensional lithography model may include: generating a two-dimensional mask image based on mask deviation information; generating an edge image for characterizing the three-dimensional effect of the mask edges based on the mask deviation information; and superimposing the two-dimensional mask image and the edge image to obtain a three-dimensional mask image.

[0093] like Figure 4B As shown, the electronic device 110 first generates a two-dimensional mask image 413 based on mask deviation information. Subsequently, the electronic device 110 generates an edge image 414 based on corrected boundary information. The edge image 414 is used to characterize the mask thickness, edge contour, and the effects of optical effects near the edge.

[0094] In some embodiments, the edge image 414 can characterize edge coupling effects, diffraction effects, or other edge-related effects caused by the three-dimensional structure of the mask. Subsequently, the electronic device 110 superimposes the two-dimensional mask image 413 and the edge image 414 to obtain a three-dimensional mask image 415. The two-dimensional mask image is mainly used to characterize the planar geometry of the mask pattern, while the edge image is mainly used to characterize the three-dimensional structural features corresponding to the mask edges. By combining the two, a three-dimensional mask image that simultaneously contains planar structural information and edge three-dimensional effect information can be obtained.

[0095] In this way, for three-dimensional lithography models, in addition to considering the two-dimensional size changes of the mask pattern, the influence of the three-dimensional structure of the mask edge on the light propagation process can be further considered, thereby improving the ability of the lithography modeling process to simulate the actual exposure process and improving the consistency between the modeling results and the actual imaging results.

[0096] In box 340, electronic device 110 can perform photolithography modeling based on a mask image. Electronic device 110 can simulate the imaging process of light propagating from the mask to the wafer surface based on the mask image, and further simulate the response of the photoresist to the light signal, thereby determining the photolithography model corresponding to the actual exposure process. Since the mask image already incorporates information related to the actual mask deviation, the subsequent photolithography modeling process can reflect the impact of mask deviation on the wafer imaging results.

[0097] Figure 4C , Figure 4D and Figure 4E A schematic diagram illustrating the process of performing photolithographic modeling according to some embodiments is shown. The following description uses a two-dimensional photolithographic model as an example; a similar process can also be used for three-dimensional photolithographic models, and this disclosure does not limit this process.

[0098] In some embodiments, performing photolithographic modeling based on a mask image may include: determining an aerial image corresponding to the mask image; determining at least one photoresist image by simulating the reaction process of the photoresist based on the mask image and the aerial image; and determining a photolithographic model based on the aerial image and the at least one photoresist image. As used herein, the term "aerial image (AI)" refers to the optical intensity distribution image formed on the imaging plane after light is modulated by the mask image. The aerial image can be used to characterize the imaging result of an optical system on a mask pattern.

[0099] like Figure 4C As shown, the electronic device 110 can generate a light intensity distribution image 421 corresponding to the mask image 413 based on the mask image 413. Specifically, the electronic device 110 can simulate the propagation process of light after passing through the mask image 413 based on a preset optical model, thereby obtaining the corresponding light intensity distribution image 421.

[0100] like Figure 4D As shown, the electronic device 110 determines at least one photoresist image based on a mask image 413 and a light intensity distribution image 421 by simulating the reaction process of the photoresist. For example, multiple photoresist images 430-1, 430-2, etc., can be generated according to different photoresist parameters, exposure conditions, or development conditions. The term "photoresist image (RI)" as used herein refers to an image obtained by simulating the reaction behavior of photoresist materials during exposure and development. Photoresist images can be used to characterize the response of photoresist to light signals under different process conditions. In some embodiments, different photoresist images can respectively characterize the photoresist response characteristics under different process conditions, thereby enabling the lithography model to cover a wider range of process variations.

[0101] like Figure 4E As shown, the electronic device 110 determines the photolithography model 440 by superimposing a light intensity distribution image and at least one photoresist image. In some embodiments, the electronic device 110 can determine the signal corresponding to the photolithography model using the following formula:

[0102] Where S represents the signal corresponding to the photolithography model; AI represents the signal corresponding to the light intensity distribution image; This represents the signal corresponding to the i-th photoresist image; n represents the number of photoresist images. It should be understood that the above signal combination method is merely an example. In other embodiments, weighted combination, nonlinear combination, or other suitable signal fusion methods can also be used to determine the lithography model, and this disclosure does not limit this.

[0103] In this way, by further introducing the photoresist response process on the basis of the optical imaging process, the influence of the optical system and the photoresist process on the final imaging result can be considered simultaneously, thereby improving the ability of the lithography model to represent the actual exposure process; and by introducing mask deviation information into the mask image generation process, the influence of the actual mask size deviation on the final imaging result can be further reflected, thereby improving the consistency between the lithography model and the actual wafer pattern.

[0104] In some examples, the above-mentioned correlation model can be applied not only to lithography modeling in optical proximity correction, but also to mask process correction (MPC) models, etching models, and other process simulation models involving mask size deviation modeling, which this disclosure does not limit.

[0105] Using the above method, based on the graphic width and spacing of each region in the design pattern, the mask deviation information corresponding to each region can be determined using an association model. Based on the determined mask deviation information, a mask image for photolithography modeling is generated, thus incorporating the mask deviation into the photolithography modeling process. Furthermore, by establishing the correlation between the mask deviation information and the graphic width and spacing, the influence of different graphic sizes and their adjacent environments on the mask deviation can be reflected, making the determined mask deviation information more consistent with the dimensional variation patterns in the actual mask manufacturing process. Especially in scenarios with small graphic sizes or complex graphic distributions, the impact of mask deviation on subsequent exposure processes can be more accurately characterized.

[0106] Since mask deviation information for different regions can be determined separately based on the graphic features of the corresponding regions, it is possible to distinguish the differentiated dimensional deviations corresponding to different boundary regions of the same graphic. For example, when the distribution of adjacent graphics on both sides of a graphic is different or the graphic density is different, different mask deviation information can be determined, thereby avoiding the errors caused by using a uniform compensation value and improving the model's adaptability to asymmetric graphic structures.

[0107] Furthermore, by generating a mask image based on the determined mask deviation information and using this mask image to perform lithography modeling, the lithography model can simultaneously consider the influence of design pattern features and actual mask size deviations. This approach improves the lithography model's ability to predict actual exposure results and enhances the consistency between simulation results and actual wafer patterns, thus providing a more accurate model basis for optical proximity correction, mask process correction, and other semiconductor manufacturing process simulations.

[0108] Figure 5 A block diagram of an electronic device 500 in which one or more examples may be implemented is shown. It should be understood that... Figure 5 The electronic device 500 shown is merely exemplary and should not be construed as limiting the functionality and scope of the examples described herein. Figure 5 The electronic device 500 shown can be used to implement the electronic device 110 discussed above.

[0109] like Figure 5As shown, electronic device 500 is in the form of a general-purpose electronic device. Components of electronic device 500 may include, but are not limited to, one or more processing units or processors 510, memory 520, storage devices 530, one or more communication units 540, one or more input devices 550, and one or more output devices 560. Processor 510 may be a physical or virtual processor and is capable of performing various processes according to programs stored in memory 520. In a multiprocessor system, multiple processors execute computer-executable instructions in parallel to improve the parallel processing capability of electronic device 500.

[0110] Electronic device 500 typically includes multiple computer storage media. Such media can be any accessible media that is accessible to electronic device 500, including but not limited to volatile and non-volatile media, removable and non-removable media. Memory 520 can be volatile memory (e.g., registers, cache, random access memory (RAM)), non-volatile memory (e.g., read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory), or some combination thereof). Storage device 530 can be removable or non-removable media and may include machine-readable media, such as flash drives, disks, or any other media that can be used to store information and / or data and can be accessed within electronic device 500.

[0111] Electronic device 500 may further include additional removable / non-removable, volatile / non-volatile storage media. Although not explicitly stated... Figure 5 As shown, disk drives for reading from or writing to removable, non-volatile disks (e.g., "floppy disks") and optical disk drives for reading from or writing to removable, non-volatile optical disks can be provided. In these cases, each drive can be connected to a bus (not shown) via one or more data media interfaces. Memory 520 may include computer program product 525 having one or more program modules configured to perform various methods or actions of various examples.

[0112] The communication unit 540 enables communication with other electronic devices via a communication medium. Additionally, the functionality of the components of the electronic device 500 can be implemented using a single computing cluster or multiple computing machines capable of communicating via communication connections. Therefore, the electronic device 500 can operate in a networked environment using logical connections to one or more other servers, networked personal computers, or another network node.

[0113] Input device 550 can be one or more input devices, such as a mouse, keyboard, trackball, etc. Output device 560 can be one or more output devices, such as a monitor, speaker, printer, etc. Electronic device 500 can also communicate with one or more external devices (not shown) via communication unit 540 as needed. These external devices include storage devices, display devices, etc., and can communicate with one or more devices that enable user interaction with electronic device 500, or with any device that enables electronic device 500 to communicate with one or more other electronic devices (e.g., network card, modem, etc.). Such communication can be performed via an input / output (I / O) interface (not shown).

[0114] A computer-readable storage medium is provided that stores computer-executable instructions thereon, wherein the computer-executable instructions are executed by a processor to implement the methods described above. A computer program product is also provided, which is tangibly stored on a non-transitory computer-readable medium and includes computer-executable instructions, which are executed by a processor to implement the methods described above.

[0115] The flowcharts and / or block diagrams of the methods, apparatus, devices, and computer program products referred to herein describe various aspects. It should be understood that each block of the flowcharts and / or block diagrams, as well as combinations of blocks in the flowcharts and / or block diagrams, can be implemented by computer-readable program instructions.

[0116] These computer-readable program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus to produce a machine such that, when executed by the processor of the computer or other programmable data processing apparatus, they create means for implementing the functions / actions specified in one or more blocks of the flowchart and / or block diagram. These computer-readable program instructions can also be stored in a computer-readable storage medium that causes a computer, programmable data processing apparatus, and / or other device to operate in a particular manner; thus, the computer-readable medium storing the instructions comprises an article of manufacture that includes instructions for implementing aspects of the functions / actions specified in one or more blocks of the flowchart and / or block diagram.

[0117] Computer-readable program instructions can be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable data processing apparatus, or other device to produce a computer-implemented process, thereby causing the instructions that execute on the computer, other programmable data processing apparatus, or other device to perform the functions / actions specified in one or more boxes of a flowchart and / or block diagram.

[0118] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to some embodiments. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of an instruction, which contains one or more executable instructions for implementing a specified logical function. In some alternative implementations, the functions marked in the blocks may occur in a different order than those shown in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0119] Various examples have been described above. The foregoing descriptions are exemplary and not exhaustive, nor are they limited to the disclosed implementations. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described implementations. The terminology used herein is chosen to best explain the principles, practical applications, or improvements to technology in the market, or to enable others skilled in the art to understand the various implementations disclosed herein.

Claims

1. A method for photolithographic modeling, characterized in that, include: Determine the graphic feature information corresponding to at least one region in the design pattern, wherein the graphic feature information indicates the graphic width and graphic spacing in the corresponding region; Based on the graphic feature information, mask deviation information corresponding to the at least one region is determined, wherein the mask deviation information indicates the size difference between the actual mask pattern corresponding to the design pattern and the design pattern. Based on the mask deviation information corresponding to the at least one region, the graphic boundary corresponding to the at least one region in the design pattern is adjusted to generate a mask image corresponding to the design pattern; as well as Photolithographic modeling is performed based on the mask image.

2. The method for photolithographic modeling according to claim 1, characterized in that, The at least one region corresponds to at least one graphic edge segment in the design pattern, and the mask deviation information includes at least one mask deviation value corresponding to each of the at least one graphic edge segments.

3. The method for photolithographic modeling according to claim 2, characterized in that, The graphic width includes the line width of the at least one graphic edge segment, and the graphic spacing includes the distance between the at least one graphic edge segment and its neighboring graphic edge segments.

4. The method for photolithographic modeling according to claim 1, characterized in that, The determination of the mask offset information corresponding to the at least one region includes: The mask deviation information corresponding to the at least one region is determined based on the correlation model, and the correlation model is used to establish a nonlinear correlation between the mask deviation information and the graphic width and the graphic spacing.

5. The method for photolithographic modeling according to claim 4, characterized in that, Also includes: Determine the graphic category corresponding to the at least one region; Based on the graphic category, determine at least one association item included in the association model and at least one parameter corresponding to the at least one association item; as well as Based on the determined at least one correlation item and the at least one parameter, mask deviation information corresponding to the at least one region is determined.

6. The method for photolithographic modeling according to claim 4, characterized in that, The association model includes at least one association term and parameters for the at least one association term, wherein the at least one association term includes at least one of the following: The first correlation term is used to represent the inverse proportional correlation between the graphic width and the mask deviation information; The second correlation term is used to represent the inverse proportional correlation between the graphic proximity effect related to the graphic spacing and the mask deviation information; The third correlation term is used to represent the nonlinear relationship between the graphic width and the mask deviation information; The fourth correlation term is used to represent the nonlinear relationship between the graphic spacing and the mask deviation information.

7. The method for photolithographic modeling according to claim 6, characterized in that, The association model is constructed according to the following formula: in, This indicates the mask deviation information. The first association term, the second association term, the third association term, and the fourth association term are respectively represented by x, y, and a, b, m, and n, respectively, and k represents a constant term.

8. The method for photolithographic modeling according to claim 6, characterized in that, The association model is constructed according to the following formula: in, This indicates the mask deviation information. The terms represent the first association term, the second association term, the third association term, and the fourth association term, respectively. x represents the width of the graphic, y represents the spacing between the graphic terms, a, b, m, and n represent the corresponding terms of the first association term, the second association term, the third association term, and the fourth association term, respectively. θ represents the base of the third association term, and k represents a constant term.

9. The method for photolithographic modeling according to claim 6, characterized in that, The association model is constructed according to the following formula: in, This indicates the mask deviation information. Let x represent the first association term and the second association term respectively, y represent the graphic width, y represent the graphic spacing, a and b represent the corresponding parameters of the first association term and the second association term respectively, and k represent a constant term.

10. The method for photolithographic modeling according to claim 6, characterized in that, The association model also includes a fifth association term, which represents the linear association between the graphic width and the mask deviation information in a graphic region smaller than the first threshold size.

11. The method for photolithographic modeling according to claim 10, characterized in that, The association model is constructed according to the following formula: in, This indicates the mask deviation information. This refers to the fifth association item. The term represents the second association term, x represents the width of the graphic, y represents the spacing between the graphics, a represents the parameter of the fifth association term, b represents the parameter of the second association term, and k represents a constant term.

12. The method for photolithographic modeling according to claim 6, characterized in that, The association model also includes a sixth association term, which includes a quadratic function term centered on a predetermined width threshold. The sixth association term is used to represent the degree of influence of the graphic width on the mask deviation information in graphic regions smaller than a second threshold size. The predetermined width threshold corresponds to the graphic width at which the mask edge coupling effect begins to significantly affect the mask deviation information.

13. The method for photolithographic modeling according to claim 12, characterized in that, The association model is constructed according to the following formula: in, This indicates the mask deviation information. This refers to the sixth association item, where the predetermined width threshold is 100. The second association term is represented by x, the width of the graphic is represented by y, the spacing between the graphics is represented by a, the parameter of the sixth association term is represented by b, the parameter of the second association term is represented by k, and the constant term is represented by k.

14. The method for photolithographic modeling according to claim 4, characterized in that, Determining the mask deviation information includes: For each of the at least one region, the graphic width and the graphic spacing are input into the association model; and The mask deviation information for that region is determined based on the output of the correlation model.

15. The method for photolithographic modeling according to claim 1, characterized in that, For a three-dimensional photolithography model, generating the mask image further includes: A two-dimensional mask image is generated by adjusting the graphic boundary corresponding to the at least one region based on the mask deviation information. Based on the mask deviation information, an edge image is generated to characterize the three-dimensional effect of the mask edges; and The two-dimensional mask image and the edge image are superimposed to obtain a three-dimensional mask image.

16. The method for photolithographic modeling according to claim 1, characterized in that, Performing photolithographic modeling based on the mask image includes: Determine the light intensity distribution image corresponding to the mask image; Based on the mask image and the light intensity distribution map, at least one photoresist image is determined by simulating the photoresist reaction process; and Based on the light intensity distribution image and the at least one photoresist image, a photolithography model is determined.

17. The method for photolithographic modeling according to claim 4, characterized in that, The association model is constructed in the following way: Acquire multiple sets of sample data, including multiple ground truth mask deviations and the corresponding graphic width and graphic spacing; as well as Based on multiple sets of sample data, at least one association term included in the association model and at least one parameter corresponding to the at least one association term are determined.

18. The method for photolithographic modeling according to claim 17, characterized in that, Determining the at least one association item and the at least one parameter includes: Based on at least one graphic category corresponding to the multiple sets of sample data, the multiple sets of sample data are classified to obtain at least one sample dataset corresponding to the at least one graphic category; and For each of the at least one graphic category, based on the at least one sample dataset, determine the at least one association item and the at least one parameter included in the association model corresponding to that graphic category.

19. An electronic device comprising: At least one processor; as well as At least one memory is coupled to at least one processor and stores instructions for execution by the at least one processor, which, when executed by the at least one processor, cause the electronic device to perform the method according to any one of claims 1 to 18.

20. A computer-readable storage medium having stored thereon computer-executable instructions that can be executed by a processor to implement the method according to any one of claims 1 to 18.

21. A computer program product tangibly stored in a computer storage medium and comprising computer-executable instructions that, when executed by a device, cause the device to perform the method according to any one of claims 1 to 18.

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