Optical proximity effect correction method and system, electronic equipment and storage medium

Abstract

The invention provides an optical proximity effect correction method and system, electronic equipment and a storage medium. The optical proximity effect correction method comprises the steps of providing a reference structure, performing optical proximity effect correction processing on the reference structure, and obtaining rounded collection data of a plurality of line end graphs with different feature sizes; constructing a rounded corner prediction model according to the plurality of feature sizes and the rounded corner acquisition data; and inputting the feature size of the target layout into the rounded prediction model, and generating rounded prediction data of the target layout by using the rounded prediction model. According to the optical proximity effect correction method, the rounded corner degree of the target layouts with different feature sizes after optical proximity effect correction can be accurately predicted, and a reference is provided for OPC verification.

Description

Background Art

[0002] With the continuous progress of semiconductor manufacturing technology, the transistor pattern size continues to shrink. When the wafer line width is less than the exposure wavelength, it is necessary to perform optical proximity correction (OPC) on the mask pattern. Limited by the optical imaging resolution, during the OPC correction process, the exposure image of the layout line end will show a rounding phenomenon.

[0003] To save computer resources and improve efficiency, the rounding length parameter (Linendbox) is defined in the Recipe (configuration file) for OPC verification. However, the current process of determining the rounding length parameter is very complex, and in actual layout processing, the rounding length parameter after OPC for a specific size layout is usually not a fixed value.

[0004] It should be noted that the information disclosed in the above background art section is only used to enhance the understanding of the background of the present application, and thus may include information that does not constitute the prior art known to those of ordinary skill in the art. Summary of the Invention

[0005] Based on this, the embodiments of the present application provide an optical proximity correction method, system, electronic device, and storage medium, which can accurately predict the rounding degree of the target layout with different feature sizes after optical proximity correction, providing a reference for OPC verification.

[0006] According to some embodiments, on the one hand, the present application provides an optical proximity correction method, including:

[0007] Providing a reference structure, performing optical proximity correction processing on the reference structure, and obtaining rounding acquisition data of multiple line end patterns with different feature sizes;

[0008] Constructing a rounding prediction model according to the multiple feature sizes and the rounding acquisition data;

[0009] Inputting the feature size of the target layout into the rounding prediction model, and generating rounding prediction data of the target layout by using the rounding prediction model.

[0010] In some embodiments, constructing a rounding prediction model according to the multiple feature sizes and the rounding acquisition data includes:

[0011] Selecting an initial model according to the distribution of the multiple feature sizes and the rounding acquisition data;

[0012] Inputting the multiple feature sizes and the rounding acquisition data into the initial model for model training;

[0013] During the model training process, evaluate the prediction effect of the initial model, and use the initial model when the prediction effect reaches the target level as the rounding prediction model.

[0014] In some embodiments, according to multiple feature sizes and the rounding, collect data and construct a rounding prediction model, including:

[0015] Select a linear regression model as the initial model;

[0016] According to multiple feature sizes and the rounding, collect data, and obtain the regression parameters of the linear regression model for model training;

[0017] During the model training process, perform an analysis of variance test and / or a significance test on the linear regression model to evaluate the prediction effect of the linear regression model, and use the linear regression model when the prediction effect reaches the target level as the rounding prediction model.

[0018] In some embodiments, when performing an analysis of variance test on the linear regression model, if the measured significance probability value of the linear regression model is less than the preset significance level, it is determined that the prediction effect reaches the target level; when performing a significance test on the linear regression model, if the measured coefficient of determination of the linear regression model is greater than or equal to the preset goodness-of-fit level, it is determined that the prediction effect reaches the target level.

[0019] In some embodiments, when performing an analysis of variance test on the linear regression model, the preset significance level is set to 0.05; when performing a significance test on the linear regression model, the preset goodness-of-fit level is set to 0.9.

[0020] In some embodiments, during the model training process, draw a regression graph to evaluate the prediction effect of the initial model.

[0021] In some embodiments, the reference structure includes a polysilicon layer.

[0022] According to some embodiments, on the other hand, the present application further provides an optical proximity effect correction system for implementing the optical proximity effect correction method provided in the foregoing embodiments. The optical proximity effect correction system includes:

[0023] A data acquisition module. After performing optical proximity effect correction processing on a reference structure, the data acquisition module acquires rounding acquisition data of multiple line-end patterns with different feature sizes;

[0024] A model construction module configured to construct a rounding prediction model according to multiple feature sizes and the rounding acquisition data;

[0025] A prediction module, configured to obtain the feature size of a target layout and generate the rounded-corner prediction data of the target layout by using the rounded-corner prediction model.

[0026] According to some embodiments, another aspect of the present application further provides an electronic device, including:

[0027] A processor;

[0028] A memory, in which executable instructions of the processor are stored;

[0029] Wherein, the processor is configured to execute the steps of the optical proximity effect correction method provided in the foregoing embodiments by executing the executable instructions.

[0030] According to some embodiments, yet another aspect of the present application provides a computer-readable storage medium for storing a program, and when the program is executed by a processor, the steps of the optical proximity effect correction method provided in the foregoing embodiments are implemented.

[0031] It should be understood that the above general description and the following detailed description are only exemplary and explanatory, and cannot limit the present application.

[0032] The embodiments of the present application may / at least have the following advantages:

[0033] By providing a reference structure and performing optical proximity effect correction processing on it, the present application obtains the rounded-corner acquisition data of multiple line-end patterns with different feature sizes, providing a data basis for subsequent model construction. Based on multiple feature sizes and rounded-corner acquisition data, a rounded-corner prediction model is constructed. The rounded-corner prediction model can accurately predict the rounded-corner degree of the target layout with different feature sizes after optical proximity effect correction, providing a reference for OPC verification. Compared with the current method of determining the rounded-corner length parameter by testing the standard size pattern of the node plus the engineer's experience, the present application can provide a more accurate and reliable prediction result, effectively solving the defect of the problem that the line-end rounding cannot be accurately measured.

[0034] Other advantages, objectives and features of the present application will be described to some extent in the subsequent specification, and to some extent, will be obvious to those skilled in the art based on the study of the following text, or can be taught from the practice of the present application. The objectives and other advantages of the present application can be realized and obtained through the following specification. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] By reading the detailed description of the non-limiting embodiments with reference to the following drawings, other features, objectives and advantages of the present application will become more obvious.

[0036] Figure 1 is a schematic flowchart of an optical proximity effect correction method in some embodiments of the present application;

[0037] Figure 2 is a schematic flowchart of constructing a rounding prediction model in the optical proximity effect correction method provided by some embodiments of the present application;

[0038] Figure 3 is a schematic flowchart of constructing a rounding prediction model in the optical proximity effect correction method provided by other embodiments of the present application;

[0039] Figure 4 is a scatter plot of the linear regression model obtained in some embodiments of the present application;

[0040] Figure 5 is Figure 4 the statistical analysis result of the linear regression model shown;

[0041] Figure 6 is a schematic flowchart of an optical proximity effect correction method in other embodiments of the present application;

[0042] Figure 7 is a schematic structural diagram of an optical proximity effect correction system in some embodiments of the present application;

[0043] Figure 8 is a schematic structural diagram of an electronic device in some embodiments of the present application;

[0044] Figure 9 is a schematic structural diagram of a computer-readable storage medium in some embodiments of the present application. Detailed implementation manners

[0045] Example embodiments will now be described more fully with reference to the accompanying drawings. However, the example embodiments can be implemented in various forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this application will be more complete and comprehensive, and will fully convey the concept of the example embodiments to those skilled in the art. The described features, structures, or characteristics can be combined in any suitable manner in one or more embodiments.

[0046] In addition, the accompanying drawings are only schematic illustrations of the present application and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and thus repeated descriptions thereof will be omitted. Some of the block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities can be implemented in software, or in one or more hardware modules or integrated circuits, or in different networks and / or processor devices and / or microcontroller devices.

[0047] In the process of Optical Proximity Correction (OPC) in semiconductor manufacturing, the phenomenon of layout line-end rounding will affect the subsequent verification process. To save computer resources and improve efficiency, when performing OPC verification, the rounding length parameter (Linendbox) is usually set in the Recipe (configuration file) to reflect the degree of layout rounding. During the OPC verification process, the line segments will be filtered by Linendbox, that is, within a specific length range, some inspection items such as EPE (Edge Placement Error) and CDV (Critical Dimension Variation) will not detect the OPC correction situation within this length range.

[0048] However, the current process of determining the rounding length parameter is very complex, and in actual layout processing, the rounding length parameter after OPC for a specific size layout is usually not a fixed value.

[0049] Based on this, this application hopes to provide a solution that can solve the above technical problems, accurately predict the rounding degree of the target layout with different feature sizes after optical proximity effect correction, and provide a reference for OPC verification. Its detailed content will be elaborated in the subsequent embodiments.

[0050] According to some embodiments, on the one hand, this application provides an optical proximity effect correction method. Please refer to Figure 1 and the optical proximity effect correction method may specifically include the following steps S100 to S300:

[0051] S100: Provide a reference structure, perform optical proximity effect correction processing on the reference structure, and obtain the rounding acquisition data of multiple line-end patterns with different feature sizes.

[0052] S200: Construct a rounding prediction model according to multiple feature sizes and rounding acquisition data.

[0053] S300: Input the feature size of the target layout into the rounding prediction model, and use the rounding prediction model to generate the rounding prediction data of the target layout.

[0054] This application provides a reference structure and performs optical proximity effect correction processing on it to obtain the rounded corner acquisition data of multiple line-end patterns with different feature sizes, providing a data basis for subsequent model construction. Based on multiple feature sizes and the rounded corner acquisition data, a rounded corner prediction model is constructed. The rounded corner prediction model can accurately predict the rounded corner degree of the target layout with different feature sizes after optical proximity effect correction. Compared with the current method of determining the rounded corner length parameter by testing the standard size pattern of the node plus the engineer's experience, this application can provide more accurate and reliable prediction results, effectively solving the defect of the inability to accurately measure the line-end rounding problem.

[0055] Input the feature size of the target layout into the rounded corner prediction model to generate the rounded corner prediction data of the target layout, which can provide a basis for setting the rounded corner length parameter in OPC verification. During the OPC verification process, it is possible to more reasonably filter out some inspection items within a certain length range, which is conducive to improving the accuracy and reliability of the OPC verification results.

[0056] The following combines Figures 2 to 6 to describe the optical proximity effect correction method provided by the embodiments of this application in more detail.

[0057] In step S100, by way of example, the reference structure may include a polysilicon (Poly) layer, but is not limited thereto. Exemplarily, the reference structure may also be other layers in integrated circuit design, such as an epitaxial layer, an interlayer dielectric layer, and so on. The embodiments of this application have wide applicability and good expandability, and can meet the rounded corner prediction requirements of different-level layouts during optical proximity effect correction, providing strong support for OPC verification.

[0058] In one of the embodiments, first provide a polysilicon layer as the reference structure and perform OPC processing on the provided polysilicon layer. After the processing is completed, analyze the layout of the polysilicon layer after OPC and classify the one-dimensional (1D) line-end patterns therein. After the classification is completed, for the aforementioned 1D line-end patterns, collect the rounded corner acquisition data at different feature sizes. Specifically, the Linedbox length of the line-end rounding after OPC correction with feature sizes of 64nm, 68nm, 72nm, 83nm, 95nm, 128nm, and 173nm can be collected, a total of 7 groups of data. Each group can include multiple (for example, 3) samples, but is not limited thereto. In other embodiments, fewer or more groups of data can also be collected. Exemplarily, the calculation starting point of the Linedbox length can start where the EPE is greater than 2nm.

[0059] Through the above steps, the rounded corner acquisition data of multiple line-end patterns with different feature sizes is obtained, providing basic data support for subsequent construction of the rounded corner prediction model.

[0060] It should be noted that the above classification and statistical steps for the rounding of 1D line ends can also be extended to the rounding classification and statistics of 2D and corner line ends, providing basic data support for the rounding prediction of 2D and corner line ends, thereby predicting the rounding degree of more complex layout structures.

[0061] For step S200, please refer to Figure 2 , in some embodiments, step S200 may specifically include the following steps S211 to S213.

[0062] S211: Select an initial model according to the distribution of multiple feature sizes and the collected data of rounding.

[0063] In step S211, observe and analyze the mathematical principles of multiple feature sizes and the corresponding collected data of rounding. According to the distribution characteristics, estimate the type of mathematical model that can be built, so as to select a suitable initial model for subsequent prediction. For example, if the data shows a linear relationship, a linear regression model can be initially selected as the initial model.

[0064] S212: Input multiple feature sizes and the collected data of rounding into the initial model for model training.

[0065] S213: During the model training process, evaluate the prediction effect of the initial model, and use the initial model when the prediction effect reaches the target level as the rounding prediction model.

[0066] It should be noted that the embodiments of the present application do not specifically limit the implementation manner of performing model fitting operations. As an example, it can be efficiently implemented with the help of Python. In the Python environment, professional libraries such as numpy, statmodels, matplotlib, pandas, patsy, and / or scipy can be used to execute step S200. The specific content and operations of each professional library in Python are not the focus of the present application, and the specific content and operations of each professional library can refer to related technologies and will not be elaborated further here.

[0067] The initial models that can be selected in the embodiments of the present application may include linear regression models (such as univariate or multivariate linear regression models), probability distribution models, differential equation models, Fourier transform models, etc., but are not limited thereto.

[0068] As an example, the Pearson correlation coefficient (Pearson coefficient) between the feature size and the collected data of rounding can be calculated to quantify the linear correlation between the two. If the Pearson coefficient is close to +1 or -1, it indicates that there is a strong linear relationship between the two, and it is appropriate to select a linear regression model.

[0069] The following further illustrates by taking the selection of a linear regression model as the initial model as an example. Please refer to Figure 3 , in some embodiments, step S200 may specifically include the following steps S221 to S223.

[0070] S221: Select a linear regression model as the initial model.

[0071] In step S221, first, a linear regression model is established. For example, a unary regression model y = ax + b, where y is the dependent variable, representing the chamfering acquisition data; x is the independent variable, representing the feature size; a is the regression coefficient, and b is the regression constant. Both a and b are regression parameters to be determined.

[0072] S222: According to multiple feature sizes and chamfering acquisition data, obtain the regression parameters of the linear regression model to perform model training.

[0073] Exemplarily, in step S222, the smf.ols function can be used for linear regression analysis to solve the regression coefficient a and the regression constant b and construct the linear regression model.

[0074] In step S222, as an example, the values of a and b can be solved through the least squares principle, thereby completing the preliminary construction and parameter determination of the linear regression model. The expression of the least squares principle here is as follows:

[0075]

[0076] S223: During the model training process, perform an analysis of variance test and / or a significance test on the linear regression model to evaluate the prediction effect of the linear regression model, and use the linear regression model when the prediction effect reaches the target level as the chamfering prediction model.

[0077] In step S223, when performing an analysis of variance test on the linear regression model, it can be configured that if the measured significance probability value of the linear regression model is less than the preset significance level, it is determined that the prediction effect reaches the target level.

[0078] The analysis of variance test, that is, the F test, is used to evaluate whether the relationship between the dependent variable and the independent variable in the linear regression model is significant. Exemplarily, anova_lm can be used for the analysis of variance test.

[0079] Specifically, when performing an analysis of variance (ANOVA) test on a linear regression model, the F-statistic is calculated, and the significance of the current linear regression model is determined through the significance probability value (p-value). When the p-value is less than the preset significance level, it indicates that the independent variables in the current linear regression model have a significant impact on the dependent variable, and the error is not caused solely by random error, that is, the current linear regression model is significant.

[0080] Exemplarily, the F-statistic can be calculated using the following formula:

[0081]

[0082] In the above formula, MS between groups represents the mean square between groups, and MS within groups represents the mean square within groups.

[0083] In some embodiments, when performing an ANOVA test on a linear regression model, the preset significance level can be set to 0.05. That is, if the p-value is less than the preset significance level of 0.05, it indicates that the current linear regression model is significant.

[0084] In step S223, when performing a significance test on a linear regression model, it can be configured that if the coefficient of determination of the measured linear regression model is greater than or equal to the preset goodness-of-fit level, it is determined that the prediction effect reaches the target level.

[0085] The significance test is to evaluate the explanatory power of the current linear regression model for the dependent variable by calculating the coefficient of determination R 2 ,. The closer R 2 is to 1, the higher the goodness-of-fit of the linear regression model, that is, the linear regression model can better explain the change of the dependent variable. Exemplarily, the coefficient of determination R 2 can be calculated using scipy.stats.pearsonr.

[0086] Among them, the coefficient of determination R 2 can be obtained using the correlation coefficient R. Exemplarily, the correlation coefficient R can be calculated using the following formula:

[0087]

[0088] In some embodiments, when performing a significance test on a linear regression model, the preset goodness-of-fit level can be set to 0.9. That is, if the coefficient of determination R 2 is greater than or equal to the preset goodness-of-fit level of 0.9, it indicates that the explanatory power of the current linear regression model for the dependent variable (i.e., the rounded corner acquisition data) is strong enough to accurately reflect the relationship between the feature size and the rounded corner.

[0089] It can be understood that in step S223, it can be configured such that when either the significance probability value is less than the preset significance level or the coefficient of determination is greater than or equal to the preset goodness-of-fit level, it can be determined that the prediction effect reaches the target level; it can also be configured such that only when both the significance probability value is less than the preset significance level and the coefficient of determination is greater than or equal to the preset goodness-of-fit level are satisfied, it is determined that the prediction effect reaches the target level, both of which are allowed in the embodiments of the present application.

[0090] Figure 4 is a scatter plot of the linear regression model y = 0.225x + 20.65 obtained in some embodiments. Figure 5 shows Figure 4 the statistical analysis results of the shown linear regression model. Figure 4 where data represents the original data points, that is, the actually observed sample data, and OLS represents the regression line fitted by the ordinary least squares method.

[0091] As Figure 5 shown at point A in, the value of R-squared at this time, that is, R 2 is 0.940, which is greater than the preset goodness-of-fit level of 0.9, indicating that there is a 94% probability that the change in the feature size will cause a change in the length of the Linedbox, that is, the goodness-of-fit of the current linear regression model is good. As Figure 5 shown at point B in, the value of Prob(F-statistic) at this time, that is, the p-value, is 0.000302, which is less than the preset significance level of 0.05, indicating that the experimental error is not caused by random error, that is, the current linear regression model is significant.

[0092] The above evaluation results show that Figure 4 the shown linear regression model y = 0.225x + 20.65 reaches the target level and can be used as a rounding prediction model to generate rounding prediction data for the target layout, so as to accurately predict the rounding degree of the line-end patterns with different feature sizes after optical proximity effect correction.

[0093] In some embodiments, during the model training process, the prediction effect of the initial model can also be evaluated by plotting the regression graph.

[0094] As an example, matplotlib can be used to plot the regression graph, visually display the actual rounding data points and the predicted values of the initial model, and intuitively observe the prediction effect of the model to judge the goodness-of-fit and accuracy of the current initial model.

[0095] Exemplarily, as Figure 6As shown, if during the evaluation of the prediction effect of the initial model, the determination result is that the prediction effect of the current initial model does not reach the target level, for example, the p-value is greater than the preset significance level or the coefficient of determination R 2 is less than the preset goodness-of-fit level, then the initial model is adjusted and re-evaluated. Thus, it is ensured that the finally obtained rounded prediction model has high accuracy and reliability and can meet the actual application requirements.

[0096] In step S300, the rounded prediction data of the target layout is generated using the rounded prediction model. As an example, after the above-mentioned prediction effect test is completed, the trained rounded prediction model can be used for single-value prediction or interval mean prediction.

[0097] According to some embodiments, on the other hand, the present application also provides an optical proximity effect correction system for implementing the optical proximity effect correction method provided in the foregoing embodiments. The technical effects that can be achieved by the foregoing optical proximity effect correction method can also be achieved by this optical proximity effect correction system, which will not be elaborated here.

[0098] Please refer to Figure 7 , this optical proximity effect correction system may specifically include a data acquisition module 100, a model construction module 200, and a prediction module 300.

[0099] After performing optical proximity effect correction processing on the reference structure, the data acquisition module 100 acquires the rounded acquisition data of a plurality of line-end patterns with different feature sizes; the model construction module 200 is configured to construct a rounded prediction model according to the plurality of feature sizes and the rounded acquisition data; the prediction module 300 is configured to acquire the feature size of the target layout and generate the rounded prediction data of the target layout using the rounded prediction model.

[0100] The embodiments of the present application also provide an electronic device, including a processor; a memory storing executable instructions of the processor; wherein, the processor is configured to execute the steps of the optical proximity effect correction method described in the foregoing embodiments by executing the executable instructions.

[0101] Those skilled in the art can understand that various aspects of the present application can be implemented as a system, a method, or a program product. Therefore, various aspects of the present application can be specifically implemented in the following forms, namely: a complete hardware implementation, a complete software implementation (including firmware, microcode, etc.), or an implementation combining hardware and software aspects, which can be collectively referred to as "circuit", "module", or "platform" here.

[0102] Next, refer to Figure 8 to describe the electronic device 600 according to this embodiment of the present application. Figure 8The displayed electronic device 600 is merely an example and shall not impose any limitation on the functions and usage scope of the embodiments of the present application.

[0103] As Figure 8 shown, the electronic device 600 is presented in the form of a general-purpose computing device. The components of the electronic device 600 may include, but are not limited to: at least one processing unit 610, at least one storage unit 620, a bus 630 connecting different system components (including the storage unit 620 and the processing unit 610), a display unit 640, etc.

[0104] Among them, the storage unit stores program codes, and the program codes can be executed by the processing unit 610, so that the processing unit 610 executes the steps according to various exemplary embodiments of the present application described in the above optical proximity effect correction method part of this specification. For example, the processing unit 610 can execute the steps as Figures 1 to 3 shown in.

[0105] The storage unit 620 may include a readable medium in the form of a volatile storage unit, such as a random access storage unit (RAM) 6201 and / or a cache storage unit 6202, and may further include a read-only storage unit (ROM) 6203.

[0106] The storage unit 620 may further include a program / utility 6204 having a set (at least one) of program modules 6205. Such program modules 6205 include, but are not limited to: an operating system, one or more application programs, other program modules, and program data. The implementation of a network environment may be included in each or some combination of these examples.

[0107] The bus 630 may represent one or more of several types of bus structures, including a storage unit bus or a storage unit controller, a peripheral bus, a graphics acceleration port, a processing unit, or a local bus using any bus structure in a variety of bus structures.

[0108] The electronic device 600 can also communicate with one or more external devices 700 (such as a keyboard, a pointing device, a Bluetooth device, etc.), and can also communicate with one or more devices that enable a user to interact with the electronic device 600, and / or communicate with any device (such as a router, a modem, etc.) that enables the electronic device 600 to communicate with one or more other computing devices. Such communication can be carried out through the input / output (I / O) interface 650. Moreover, the electronic device 600 can also communicate with one or more networks (such as a local area network (LAN), a wide area network (WAN), and / or a public network, such as the Internet) through the network adapter 660. The network adapter 660 can communicate with other modules of the electronic device 600 through the bus 630. It should be understood that, although not shown in the figure, other hardware and / or software modules can be used in combination with the electronic device 600, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, etc.

[0109] In the electronic device, when the program in the memory is executed by the processor, the steps of the optical proximity effect correction method described in the foregoing embodiments are implemented. Therefore, the electronic device can also obtain the technical effects of the foregoing optical proximity effect correction method.

[0110] An embodiment of the present application also provides a computer-readable storage medium for storing a program, and when the program is executed by the processor, the steps of the optical proximity effect correction method described in the foregoing embodiments are implemented. In some possible implementation manners, various aspects of the present application can also be implemented in the form of a program product, which includes program code. When the program product is executed on a terminal device, the program code is used to cause the terminal device to execute the steps according to various exemplary embodiments described in the optical proximity effect correction method part of the present specification above.

[0111] Reference Figure 9 As shown, a program product 800 for implementing the above method according to an embodiment of the present application is described. It can be a portable compact disc read-only memory (CD-ROM) and includes program code, and can be executed on a terminal device, such as a personal computer. However, the program product of the present application is not limited thereto. In this document, the readable storage medium can be any tangible medium that contains or stores a program, and this program can be used by or in combination with an instruction execution system, apparatus, or device.

[0112] The program product may adopt any combination of one or more readable media. The readable media may be a readable signal medium or a readable storage medium. The readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the above. More specific examples (a non-exhaustive list) of the readable storage medium include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0113] The computer-readable storage medium may include a data signal propagated in a baseband or as part of a carrier wave, in which the readable program code is carried. Such a propagated data signal may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. The readable storage medium may also be any readable medium other than the readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the readable storage medium may be transmitted using any appropriate medium, including but not limited to wireless, wired, optical fiber cable, RF, etc., or any suitable combination of the above.

[0114] The program code for performing the operations of this application may be written in any combination of one or more programming languages, including object-oriented programming languages such as Java, C++, etc., and also including conventional procedural programming languages such as the "C" language or similar programming languages. The program code may be executed entirely on the user's computing device, partially on the user's device, executed as a stand-alone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server. In the case of a remote computing device, the remote computing device may be connected to the user's computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computing device (e.g., by using an Internet service provider to connect through the Internet).

[0115] When the program in the computer storage medium is executed by a processor, the steps of the optical proximity effect correction method are implemented. Therefore, the computer storage medium can also obtain the technical effects of the above optical proximity effect correction method.

[0116] It will be understood that various modifications and variations can be made to the present application without departing from the spirit or scope of the present application, which will be apparent to those skilled in the art. Therefore, the present application is intended to cover modifications and variations of the present application that fall within the scope of the corresponding claims (the claimed technical solutions) and their equivalents. It should be noted that the embodiments provided in the embodiments of the present application can be combined with each other without conflict.

[0117] In the description of this specification, the descriptions referring to terms such as "some embodiments", "as an example", "exemplarily", etc. mean that the specific features, structures, materials or features described in connection with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic descriptions of the above terms do not necessarily refer to the same embodiment or example.

[0118] The technical features of the above-described embodiments can be combined arbitrarily. For the sake of brevity of description, not all possible combinations of the technical features of the above-described embodiments are described. However, as long as there is no conflict in the combination of these technical features, it should be considered as the scope described in this specification.

[0119] The above-described embodiments merely represent several implementation manners of the present application, and the descriptions thereof are relatively specific and detailed, but should not be construed as a limitation on the scope of the patent application. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present application, several deformations and improvements can still be made, and these all belong to the protection scope of the present application. Therefore, the protection scope of the patent of the present application shall be subject to the appended claims.

Claims

1. An optical proximity effect correction method, characterized in that: include: providing a reference structure, performing an optical proximity effect correction process on the reference structure, Acquire the rounded corner collection data of multiple line end graphics with different feature sizes; Constructing a rounding prediction model according to the plurality of characteristic dimensions and the rounding acquisition data; The characteristic size of the target layout is input into the corner rounding prediction model, and the corner rounding prediction data of the target layout is generated by using the corner rounding prediction model.

2. The optical proximity effect correction method according to claim 1, characterized in that: According to the plurality of feature sizes and the corner rounding acquisition data, a corner rounding prediction model is constructed, including: Selecting an initial model according to the distribution of the plurality of characteristic sizes and the rounded corner collection data; Inputting a plurality of the feature sizes and the rounded corner acquisition data into the initial model to perform model training; During the model training process, the prediction effect of the initial model is evaluated, and the initial model when the prediction effect reaches the target level is used as the rounding prediction model.

3. The optical proximity effect correction method according to claim 1, characterized in that: According to the plurality of feature sizes and the corner rounding acquisition data, a corner rounding prediction model is constructed, including: The linear regression model was selected as the initial model; According to the plurality of feature sizes and the rounded corner collection data, regression parameters of the linear regression model are obtained to perform model training; During the model training process, the linear regression model is subjected to variance analysis test and / or significance test to evaluate the prediction effect of the linear regression model, and the linear regression model when the prediction effect reaches the target level is used as the rounding prediction model.

4. The optical proximity effect correction method according to claim 3, characterized in that: When performing variance analysis on the linear regression model, if the significance probability value of the linear regression model is measured to be less than a preset significance level, it is determined that the prediction effect reaches the target level; When performing a significance test on the linear regression model, if the determination coefficient of the linear regression model is measured to be greater than or equal to a preset fit level, it is determined that the prediction effect reaches the target level.

5. The optical proximity effect correction method according to claim 4, characterized in that: When performing variance analysis on the linear regression model, the preset significance level is set to 0.05; When performing a significance test on the linear regression model, the preset fit level is set to 0.

9.

6. The optical proximity effect correction method according to claim 2, characterized in that: During the model training process, a regression graph is drawn to evaluate the prediction effect of the initial model.

7. The optical proximity effect correction method according to claim 1, characterized in that: The reference structure includes a polysilicon layer.

8. An optical proximity effect correction system, characterized in that: For implementing the optical proximity effect correction method according to any one of claims 1 to 7, the optical proximity effect correction system comprises: A data acquisition module, which acquires rounded corner acquisition data of a plurality of line end graphics with different characteristic sizes after performing optical proximity effect correction processing on the reference structure; A model building module is configured to build a rounding prediction model according to the plurality of feature sizes and the rounding acquisition data; The prediction module is configured to obtain the characteristic size of the target layout and generate the rounding prediction data of the target layout using the rounding prediction model.

9. An electronic device, characterized in that: include: processor; a memory storing executable instructions of the processor; The processor is configured to perform the steps of the optical proximity effect correction method according to any one of claims 1 to 7 by executing the executable instructions.

10. A computer-readable storage medium for storing a program, characterized in that: When the program is executed by a processor, the steps of the optical proximity effect correction method according to any one of claims 1 to 7 are implemented.

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