A modeling method, system and computer equipment for optical proximity correction model

Abstract

The present invention discloses a modeling method, system and computer equipment for an optical proximity correction model, which belongs to the field of integrated circuit manufacturing. The modeling method of the optical proximity correction model includes the following steps: setting a cost function including the standard deviation of an asymmetric pattern; inputting target pattern data into the optical proximity correction model under each parameter, obtaining simulated pattern data of the target pattern, and obtaining the cost function under each parameter based on the simulated pattern data and wafer pattern data; outputting the optical proximity correction model with the minimum cost function within the parameter range; and when the minimum cost function is less than a preset cost, using the optical proximity correction model corresponding to the minimum cost function as the target optical proximity correction model. The modeling method of the optical proximity correction model disclosed by the present invention can improve the accuracy of optical proximity correction of asymmetric mask patterns.

Description

Technical Field

[0001] The present invention belongs to the field of integrated circuit manufacturing, and in particular relates to a modeling method, system and computer equipment for an optical proximity correction model. Background Art

[0002] In semiconductor manufacturing, optical proximity correction (OPC) is a key step in the photolithography process. It fine-tunes the mask pattern to compensate for image distortion caused by optical effects during the photolithography process.

[0003] Optical proximity correction (OPC) models typically focus on symmetrically designed reticle patterns. Because these patterns allow for simplified mathematical models and algorithms to estimate and correct optical distortion, OPC models work well for symmetrical reticle patterns. However, many important reticle patterns, such as those used for static random-access memory (SRAM), are asymmetric. Due to their unique layout and optical properties, these asymmetric reticle patterns are ineffective for traditional OPC models to effectively predict and correct their optical distortion. This requires engineers to manually adjust process steps and parameters to adjust these asymmetric reticle patterns, a time-consuming and labor-intensive process. Summary of the Invention

[0004] The object of the present invention is to provide a modeling method, system and computer equipment for an optical proximity correction model, which can solve the problem that optical proximity correction cannot be performed on asymmetric mask patterns.

[0005] To solve the above technical problems, the present invention is achieved through the following technical solutions:

[0006] The present invention provides a modeling method for an optical proximity correction model, comprising the following steps:

[0007] Acquire a plurality of target pattern data and wafer pattern data corresponding to each of the target pattern data;

[0008] Distinguish target figures into symmetrical figures and asymmetrical figures;

[0009] Setting an initial kernel and parameter range of an optical proximity correction model, and setting a cost function representing the degree of fit, wherein the cost function includes the standard deviation of all target patterns and the standard deviation of the asymmetric pattern;

[0010] Under each parameter within the parameter range, inputting the target pattern data into the optical proximity correction model to obtain simulated pattern data of the target pattern, and obtaining a cost function under each parameter based on the simulated pattern data and the wafer pattern data;

[0011] Outputting the optical proximity correction model when the cost function is minimized within the parameter range; and

[0012] Determine whether the minimum cost function is less than a preset cost; if the minimum cost function is less than the preset cost, use the output optical proximity correction model as the target optical proximity correction model; if the minimum cost function is greater than or equal to the preset cost, adjust the kernel and parameter range of the optical proximity correction model until the minimum cost function is less than the preset cost.

[0013] In one embodiment of the present invention, obtaining the cost function under each parameter includes the following steps:

[0014] Select a parameter within the parameter range;

[0015] Inputting all target graphic data into the optical proximity correction model to obtain a simulation data graphic corresponding to each target graphic data;

[0016] Obtaining a cost function under the parameters according to the simulation graphic data and the wafer graphic data corresponding to each target graphic data; and

[0017] Determine whether the parameters within the parameter range have been traversed. If the parameters within the parameter range have been traversed, end the process. If the parameters within the parameter range have not been traversed, select another parameter within the parameter range and repeat the process of obtaining the cost function.

[0018] In one embodiment of the present invention, the simulated pattern data is position data of two intersections of an optical signal curve and an exposure threshold value of a wafer pattern formed on a wafer by simulating a target pattern using the optical proximity correction model.

[0019] In one embodiment of the present invention, the cost function is obtained by the following formula:

[0020] Cost function=A×(RMS-all / RMS-all-spec)+B×(RMS-part / RMS-part-spec);

[0021] Among them, Cost function is the cost function, RMS-all is the standard deviation of the target graphics of all inputs, RMS-all-spec is the specification value of the standard deviation of the target graphics of all inputs, A is the weight coefficient of the target graphics of all inputs, RMS-part is the standard deviation of all the asymmetric graphics, RMS-part-spec is the specification value of the standard deviation of all the asymmetric graphics, and B is the weight coefficient of all the asymmetric graphics.

[0022] In one embodiment of the present invention, RMS-all is the root mean square of the fitting errors of all the target graphics, which is used to characterize the errors between the simulation graphics and the wafer graphics of all the target graphics, and RMS-part is the root mean square of the fitting errors of all the asymmetric graphics, which is used to characterize the errors between the simulation graphics and the wafer graphics of all the asymmetric graphics.

[0023] In one embodiment of the present invention, the standard deviation RMS-all of all input target graphics is obtained by the following formula:

[0024] RMS-all=SQRT(SUMSQ E(1,N) / COUNTA E(1,N));

[0025] The standard deviation RMS-part of all asymmetric patterns is obtained by the following formula:

[0026] RMS-part=SQRT(SUMSQ E(b+1,N) / COUNTA E(b+1,N));

[0027] Among them, SQRT represents the square root function, SUMSQ represents the sum of squares function, COUNTA represents the function of counting non-empty units within a given range, E(1, N) is the set of errors between the simulation graphic data and the wafer graphic data corresponding to all the target graphics, and E(b+1, N) is the set of errors between the simulation graphic data and the wafer graphic data corresponding to the asymmetric graphics.

[0028] In one embodiment of the present invention, the value of the weight coefficient A of all the input target graphics is 0.8, and the value of the weight coefficient B of all the asymmetric graphics is 0.2.

[0029] In one embodiment of the present invention, differentiating a target graphic into a symmetrical graphic and an asymmetrical graphic comprises the following steps:

[0030] inputting the target pattern data and the wafer pattern data into the optical proximity correction model;

[0031] Using the optical proximity correction model to simulate an optical signal curve of a wafer pattern formed by a target pattern on a wafer;

[0032] Determining the exposure threshold according to the distance between two intersection points of the light signal curve and the exposure threshold being equal to the wafer pattern data;

[0033] Obtaining distances from two intersection points of the light signal curve and the exposure threshold to the target graphic data, and determining two intersection points of the light signal curve and the exposure threshold; and

[0034] Determine whether the distances from the two intersection points of the light signal curve and the exposure threshold to the target graphic data are equal. If the distances from the two intersection points of the light signal curve and the exposure threshold to the target graphic data are equal, determine that the target graphic is the symmetrical graphic. If the distances from the two intersection points of the light signal curve and the exposure threshold to the target graphic data are not equal, determine that the target graphic is the asymmetrical graphic.

[0035] The present invention also provides a modeling system for an optical proximity correction model, comprising:

[0036] A data acquisition module, which acquires a plurality of target graphic data and wafer graphic data corresponding to each of the target graphic data;

[0037] A graphic distinction module, which distinguishes target graphics into symmetrical graphics and asymmetrical graphics;

[0038] Kernel and parameter range adjustment module, which sets the initial kernel and parameter range of the optical proximity correction model;

[0039] A cost function setting module is configured to set a cost function representing the degree of fitting, wherein the cost function includes the standard deviation of all target graphics and the standard deviation of the asymmetric graphics;

[0040] an optical proximity correction and cost function output module, which inputs the target pattern data into the optical proximity correction model at each parameter within the parameter range, obtains simulated pattern data of the target pattern, and obtains a cost function at each parameter based on the simulated pattern data and the wafer pattern data;

[0041] An optical proximity correction model output module, configured to output the optical proximity correction model when the cost function is minimized within the parameter range; and

[0042] A judgment module, judging whether the minimum cost function is less than a preset cost; and

[0043] a target optical proximity correction model output module, which uses the output optical proximity correction model as the target optical proximity correction model when the minimum cost function is less than the preset cost;

[0044] If the minimum cost function is greater than or equal to the preset cost, the kernel and parameter range adjustment module adjusts the kernel and parameter range of the optical proximity correction model until the minimum cost function is less than the preset cost.

[0045] The present invention also provides a computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the method for modeling an optical proximity correction model as described in any one of the above items when executing the computer program.

[0046] In summary, the present invention provides a method, system, and computer device for modeling an optical proximity correction model, which have the unexpected effect of significantly improving the optical proximity correction model's ability to process asymmetric design graphics, especially the correction accuracy when processing asymmetric graphics of key components such as static random access memory. The modeled optical proximity correction model can more accurately predict and correct the optical distortion of asymmetric graphics, thereby directly improving the results of optical proximity correction and the yield of the product. By enabling the modeled optical proximity correction model to adapt to asymmetric graphics in the initial stage, the tedious adjustment and compensation work that engineers need to perform in the subsequent process is greatly reduced, thereby accelerating the R&D progress and improving production efficiency. In addition, the application of this technical solution reduces the reliance on subsequent manual adjustment process steps and parameter compensation, reduces the risk of compensation failure, and further ensures the accuracy of electrical performance and the reliability of the product.

[0047] Of course, any product implementing the present invention does not necessarily need to achieve all of the advantages described above at the same time. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0049] Figure 1 FIG. 4 is a flowchart of a method for modeling an optical proximity correction model in one embodiment.

[0050] Figure 2 FIG. 1 is a flow chart of classifying a target pattern into a symmetrical pattern and an asymmetrical pattern in one embodiment.

[0051] Figure 3 FIG. 1 is a schematic diagram showing the principle of differentiating target patterns into symmetrical patterns and asymmetrical patterns in one embodiment.

[0052] Figure 4 FIG. 4 is a flowchart of obtaining a cost function under each parameter in one embodiment.

[0053] Figure 5 Schematic diagram of the symmetrical pattern correction principle in one embodiment.

[0054] Figure 6 Schematic diagram of asymmetric pattern correction principle in one embodiment.

[0055] Figure 7 FIG. 4 is a structural block diagram of a modeling system for an optical proximity correction model in one embodiment.

[0056] Figure 8 The figure is a structural block diagram of a computer device in one embodiment.

[0057] Description of labels:

[0058] 201. Data acquisition module; 202. Graphic differentiation module; 203. Kernel and parameter range adjustment module; 204. Cost function setting module; 205. Optical proximity correction and cost function output module; 206. Optical proximity correction model output module; 207. Judgment module; 208. Target optical proximity correction model output module; 210. Light signal curve; 211. Exposure threshold. DETAILED DESCRIPTION

[0059] The following describes the embodiments of the present invention through specific examples. Those skilled in the art will readily understand the other advantages and benefits of the present invention from the disclosure herein. The present invention may also be implemented or applied through various other specific embodiments, and the details in this specification may be modified or altered based on different viewpoints and applications without departing from the spirit of the present invention.

[0060] It should be noted that the illustrations provided in this embodiment are only used to schematically illustrate the basic concept of the present invention. Therefore, the illustrations only show components related to the present invention and are not drawn according to the number, shape and size of components in actual implementation. In actual implementation, the type, quantity and proportion of each component can be changed at will, and the component layout type may also be more complicated.

[0061] In the present invention, it should be noted that the terms "center," "upper," "lower," "left," "right," "vertical," "horizontal," "inner," and "outer" and the like indicate positions or locations based on those shown in the accompanying drawings. These terms are intended solely to facilitate the description of the present application and simplify the description, and are not intended to indicate or imply that the devices or components referred to must have a specific orientation, be constructed, or operate in a specific orientation. Therefore, they should not be construed as limiting the present application. Furthermore, the terms "first" and "second" are used solely for descriptive and distinguishing purposes and should not be construed as indicating or implying relative importance.

[0062] In the semiconductor integrated circuit manufacturing process, photolithography is used to transfer the mask pattern from a mask to a wafer, forming the wafer pattern. Before photolithography, an optical proximity correction (OPC) model is used to correct the mask pattern, ensuring that the resulting wafer pattern on the wafer closely matches the designed wafer pattern. Specifically, the OPC model first identifies the edges of the mask pattern, allowing each edge to move freely. This correction is then performed to form the target pattern. Next, the target pattern is simulated using an optical model and a photoresist chemical reaction model to form a wafer pattern on the wafer, known as a simulated pattern. The simulated pattern is then compared to the designed wafer pattern. A close error between the simulated and designed wafer patterns indicates that the simulated pattern is close to the designed wafer pattern. During operation, the correction software moves the edge position of the mask pattern, measuring the error between the simulated and designed wafer patterns in real time. This process is repeated until the error reaches an acceptable value.

[0063] An initial optical proximity correction (OPC) model is established within the lithography machine. The kernel and parameter range within this initial OPC model can be set using empirical values. The process of modeling the OPC model involves determining the kernel and parameter range within the OPC model. After inputting the target pattern data and the designed wafer pattern data into the OPC model, the OPC model iteratively calculates the simulated pattern's optical signal curve and the lithography machine's exposure threshold. When the distance between the intersection of the exposure threshold and the optical signal curve is exactly equal to the wafer pattern data, the simulated pattern output by the OPC model fully matches the designed wafer pattern. This indicates that the target OPC model has been obtained, completing the OPC model modeling process.

[0064] See also Figure 1 As shown, the present application provides a method, system, and computer device for modeling an optical proximity correction model, which can improve the fit between the simulated pattern of an asymmetric pattern and the wafer pattern, thereby increasing the accuracy of the proximity correction model in correcting asymmetric patterns. Specifically, the present invention provides a method for obtaining an optical proximity correction model, including steps S10 to S80.

[0065] Step S10: Acquire a plurality of target pattern data and wafer pattern data corresponding to each target pattern data.

[0066] Step S20: Differentiate the target graphics into symmetrical graphics and asymmetrical graphics.

[0067] Step S30: setting the initial kernel and parameter range of the optical proximity correction model.

[0068] Step S40: Set a cost function representing the degree of fitting.

[0069] Step S50 : Under each parameter within the parameter range, input the target pattern data into the optical proximity correction model, obtain simulated pattern data corresponding to the target pattern data, and obtain a cost function under each parameter based on the simulated pattern data and the wafer pattern data.

[0070] Step S60 , outputting the optical proximity correction model with the minimum cost function within the parameter range.

[0071] Step S70: Determine whether the minimum cost function is less than a preset cost. If so, execute step S80: Use the optical proximity correction model corresponding to the minimum cost function as the target optical proximity correction model. If the minimum cost function is greater than or equal to the preset cost, return to step S30 and reset the kernel and parameter range in step S30 until the minimum cost function is less than the preset cost.

[0072] See also Figure 1 As shown, in one embodiment of the present invention, in step S10, the target pattern is a corrected mask pattern, and the target pattern data is the critical dimension (mask CD) of the corrected mask pattern. The wafer pattern data corresponding to the target pattern data is the designed wafer pattern data, i.e., the critical dimension (wafer CD) of the designed wafer pattern.

[0073] See also Figures 1 to 3 As shown, in one embodiment of the present invention, in step S20, the target pattern is divided into a symmetrical pattern and an asymmetrical pattern, and whether the target pattern is a symmetrical pattern or an asymmetrical pattern can be determined based on the target pattern data and the corresponding wafer pattern data. Specifically, steps S201 to S207 are included.

[0074] Step S201: inputting target pattern data and wafer pattern data into an optical proximity correction model.

[0075] Specifically, the target pattern data and the wafer pattern data are input into the optical model and the photoresist chemical reaction model of the optical proximity correction model. Figure 3In the Mask CD shown in part (a), the wafer pattern data is, for example, Figure 3 The wafer CD shown in part (a).

[0076] Step S202 : Using an optical proximity correction model to simulate an optical signal curve of a wafer pattern formed by a target pattern on a wafer.

[0077] Specifically, the optical proximity correction model is used to simulate the optical signal curve 210 of the wafer pattern formed by the target pattern on the wafer, for example Figure 3 As shown in part (b) of .

[0078] Step S203 : determining the exposure threshold according to the distance between the two intersection points of the light signal curve and the exposure threshold being equal to the wafer pattern data.

[0079] Specifically, such as Figure 3 As shown in part (c) of the figure, the exposure threshold 211 is adjusted so that the distance between the intersection of light signal curve 210 and exposure threshold 211 is equal to the size of the wafer pattern data, thus obtaining exposure threshold 211. It should be noted that the obtained light signal curve 210 and exposure threshold 211 do not necessarily meet the requirements of the subsequent cost function; they are only used here to determine whether the target pattern is symmetrical.

[0080] Step S204: Obtain the distances from the two intersection points of the light signal curve and the exposure threshold to the target graphic data, and determine the two intersection points of the light signal curve and the exposure threshold.

[0081] Specifically, such as Figure 3 Part (d) of Figure 3 As shown in section (e) of FIG. 2 , the two points where the light signal curve 210 intersects the exposure threshold 211 include, for example, a left intersection point L and a right intersection point R. The distance from the left intersection point L to the target pattern data is, for example, a first distance Y1, and the distance from the right intersection point R to the target pattern is, for example, a second distance Y2. It should be noted that in this application, the left intersection point L and the right intersection point R are used merely as names and do not indicate specific locations.

[0082] Step S205: Determine whether the distances from the two intersection points of the light signal curve and the exposure threshold to the target graphic data are equal. If the distances from the two intersection points of the light signal curve and the exposure threshold to the target graphic data are equal, execute step S206: Determine that the target graphic is a symmetrical graphic. If the distances from the two intersection points of the light signal curve and the exposure threshold to the target graphic data are not equal, execute step S207: Determine that the target graphic is an asymmetrical graphic.

[0083] In this embodiment, if Figure 3 Part (d) of Figure 3As shown in part (e) of the figure, a determination is made as to whether the distances from the two intersection points of the light signal curve 210 and the exposure threshold 211 to the target pattern data are equal. Specifically, a determination is made as to whether a first distance Y1 from the left intersection point L to the target pattern data and a second distance Y2 from the right intersection point R to the target pattern data are equal. If the first distance Y1 and the second distance Y2 are equal, the target pattern is symmetrical. If the first distance Y1 and the second distance Y2 are unequal, the target pattern is asymmetrical.

[0084] See also Figures 1 to 2 As shown, in one embodiment of the present invention, after all target graphics are divided into symmetric graphics and asymmetric graphics, the symmetric graphics and asymmetric graphics are placed into different sets, namely, a symmetric graphics set and an asymmetric graphics set. In this embodiment, all target graphics are represented by the set (1, N), the symmetric graphics set is represented by the set (1, b), and the asymmetric graphics set is represented by the set (b+1, N). Where N is the number of target graphics, b is the number of symmetric graphics, and Nb is the number of asymmetric graphics.

[0085] See also Figure 1 As shown, in one embodiment of the present invention, in step S30, the kernel refers to the basic function or algorithm used to describe the pattern transformation process of transferring the mask pattern to the wafer surface. These transformation processes include effects such as diffraction, reflection, and interference of light. The parameter range refers to the parameter setting range of these kernel functions when simulating the actual lithography process. These parameters will affect the accuracy of optical proximity correction and the final correction effect. The selection of the kernel and its parameters is directly related to the effectiveness and efficiency of the optical proximity correction model modeling process. In the present application, when setting the initial kernel and parameter range of the optical proximity correction model, any kernel and the corresponding parameter range can be selected from the modeling program library based on experience.

[0086] See also Figure 1 As shown, in one embodiment of the present invention, the cost function in step S40 is obtained by the following formula:

[0087] Cost function=A×(RMS-all / RMS-all-spec)+B×(RMS-part / RMS-part-spec);

[0088] Among them, Cost function is the cost function, RMS-all is the standard deviation of all input target graphics, RMS-all-spec is the specification value of the standard deviation of all input target graphics, A is the weight coefficient of all input target graphics, RMS-part is the standard deviation of all asymmetric graphics, RMS-part-spec is the specification value of the standard deviation of all asymmetric graphics, and B is the weight coefficient of all asymmetric graphics.

[0089] See also Figure 1 As shown in FIG. 1 , in one embodiment of the present invention, RMS-all is the root mean square of the fitting errors for all target patterns, representing the error between the simulated patterns and the wafer patterns for all target patterns. RMS-part is the root mean square of the fitting errors for all asymmetric patterns, representing the error between the simulated patterns and the wafer patterns for all asymmetric patterns. The fitting error is the data difference between the simulated pattern output by the optical proximity correction model and the designed wafer pattern.

[0090] Specifically, the standard deviation RMS-all of all input target graphics is obtained by the following formula:

[0091] RMS-all=SQRT(SUMSQ E(1,N) / COUNTA E(1,N));

[0092] The standard deviation RMS-part of all asymmetric patterns is obtained by the following formula:

[0093] RMS-part=SQRT(SUMSQ E(b+1,N) / COUNTA E(b+1,N));

[0094] Among them, SQRT represents the square root function, SUMSQ represents the sum of squares function, COUNTA represents the function of counting non-empty units within a given range, E(1, N) is the set of errors between the simulated graphic data and the wafer graphic data corresponding to all target graphics, and E(b+1, N) is the set of errors between the simulated graphic data and the wafer graphic data corresponding to the asymmetric graphics.

[0095] See also Figure 1As shown, in one embodiment of the present invention, RMS-all-spec and RMS-part-spec are specific specification values of standard deviation set to achieve lithography quality standards. The specific values of RMS-all-spec and RMS-part-spec are set based on process requirements and process node characteristics to ensure the accuracy and consistency of the simulated graphics and target graphics. Among them, the value range of RMS-all-spec is, for example, 1.6 to 2.0, and specifically, it can be 1.6, 1.8, or 2.0. The value range of RMS-part-spec is, for example, between 1.4 and 1.6, and specifically, it can be 1.4, 1.5, or 1.6.

[0096] See also Figure 1 As shown, in one embodiment of the present invention, the weight coefficient A of all input target graphics is, for example, 0.8, and the weight coefficient B of all asymmetric graphics is, for example, 0.2. Therefore, the cost function can be expressed as:

[0097] Cost function=0.8×(RMS-all / RMS-all-spec)+0.2×(RMS-part / RMS-part-spec).

[0098] In other embodiments, the specific values of the weight coefficients A of all input target graphics and the weight coefficients B of all asymmetric graphics may also be readjusted.

[0099] See also Figure 1 and Figure 4 As shown, in one embodiment of the present invention, step S50 specifically includes steps S501 to S504.

[0100] Step S501: Select a parameter within a parameter range.

[0101] Step S502: input all target graphic data into the optical proximity correction model to obtain simulation graphic data corresponding to each target graphic data.

[0102] Specifically, all target pattern data are input into the optical model and photoresist chemical reaction model in the optical proximity correction model, and the optical proximity correction model is used to simulate the wafer pattern data formed by all target pattern data on the wafer, that is, simulated pattern data. Figure 3As shown, when simulating wafer pattern data formed on a wafer using target pattern data, the optical proximity correction model adjusts the optical signal curve 210 and the exposure threshold 211 based on the kernel and parameters. The simulated pattern data is the position data of the two intersection points of the optical signal curve 210 and the exposure threshold 211, which are formed on the wafer using the optical proximity correction model to simulate the target pattern. In this embodiment, the simulated pattern data is the position data of the left intersection point L and the right intersection point R where the optical signal curve 210 intersects the exposure threshold 211. Therefore, during the iterative execution of steps S501 to S504, in step S502, for each target pattern under each parameter within the parameter range, the position data of the left intersection point L and the right intersection point R where the optical signal curve 210 intersects the exposure threshold 211 are obtained, i.e., the simulated pattern data for each target pattern.

[0103] Step 503 : Obtain a cost function under the parameters according to the simulation pattern data and wafer pattern data corresponding to each target pattern data.

[0104] Specifically, under the parameters set in step S501, the simulation graphic data and wafer graphic data corresponding to all target graphic data are obtained, including the simulation graphic data and wafer graphic data corresponding to symmetrical graphics, and the simulation graphic data and wafer graphic data corresponding to asymmetrical graphics. At this time, the cost function under the parameters can be calculated based on the formula of the cost function set in step S40. Specifically, based on the simulation graphic data and wafer graphic data corresponding to all target graphic data, the root mean square of the fitting error of all target graphics, that is, RMS-all, is obtained. Based on the simulation graphic data and wafer graphic data corresponding to all asymmetric graphics, the root mean square of the fitting error of all asymmetric graphics, that is, RMS-part, is obtained. Therefore, in the process of repeatedly iterating steps S501 to S504, a cost function corresponding to the parameters can be obtained in step S503.

[0105] Step S504: determine whether the parameters within the parameter range have been traversed. If the parameters within the parameter range have been traversed, end the process; if the parameters within the parameter range have not been traversed, return to step S501.

[0106] It should be noted that the parameters selected within the parameter range in step S501 are not repeated in each loop. Steps S501 to S504 are iteratively executed for each parameter within the set parameter range, thereby obtaining a cost function for each parameter within the parameter range. In this embodiment, the number of target graphics is N, and the loop of steps S501 to S504 is also iteratively executed N times for each parameter.

[0107] See also Figure 1As shown, in one embodiment of the present invention, in step S60, the multiple cost functions obtained in step S50 may be compared to obtain the minimum cost function, and the optical proximity correction model with the minimum cost function may be output.

[0108] See also Figure 1 As shown, in one embodiment of the present invention, in step S70, it is determined whether the minimum cost function is less than the preset cost. If the cost function is less than the preset cost, it means that the optical proximity correction model corresponding to the current minimum cost function meets the requirements, and the optical proximity correction model corresponding to the minimum cost function is used as the target optical proximity correction model to complete the modeling process.

[0109] See also Figure 1 As shown, in one embodiment of the present invention, in step S70, if the cost function is greater than or equal to the preset cost, it means that the optical proximity correction model corresponding to the current minimum cost function does not meet the requirements, and it is necessary to add a kernel and adjust the parameter range to redefine the optical proximity correction model.

[0110] See also Figure 1 As shown, in one embodiment of the present invention, in step S70, the kernel and parameter range in the optical proximity correction model can be adjusted toward a specific target direction by setting a cost function. In this application, the cost function introduces data of asymmetric patterns, so that the kernel and parameter range in the optical proximity correction model are adjusted toward a range that is favorable for asymmetric patterns, thereby improving the accuracy of asymmetric pattern correction.

[0111] See also Figure 1 、 Figure 5 as well as Figure 6As shown, during the optical proximity correction modeling process, the primary focus is adjusting the optical signal curve 210 and the exposure threshold 211 so that the positional data (simulated pattern data) of the left intersection point L and the right intersection point R, where the optical signal curve 210 intersects the exposure threshold 211, coincide with the designed wafer pattern data. When the target pattern is symmetrical, the positional data of the left intersection point L and the right intersection point R are easily determined due to their central symmetry. For example, by taking the difference between the designed wafer pattern data and the target pattern data and dividing by 2, a first distance Y1 from the left intersection point L to the target pattern data and a second distance Y2 from the right intersection point R to the target pattern data can be obtained. The data for the left intersection point L can be obtained by subtracting the first distance Y1 from the target pattern data, and the data for the right intersection point R can be obtained by adding the second distance Y2 to the target pattern data. However, for asymmetrical patterns, using this method to determine the left intersection point L and the right intersection point R can result in a larger error between the simulated pattern data and the designed wafer pattern data. Since it is difficult to determine the first distance Y1 from the left intersection point L to the target graphic data and the second distance Y2 from the right intersection point R to the target graphic data, the kernel and parameter range can only be adjusted to make the position data of the left intersection point L and the right intersection point R where the light signal curve 210 intersects the exposure threshold 211 continuously approach the designed wafer graphic data. In the present application, by adding the relevant data of the asymmetric graphic to the cost function and adjusting the weights corresponding to the relevant data of the asymmetric graphic, the obtained target optical proximity correction model improves the fitting results of the asymmetric graphic while ensuring the fitting degree of the symmetric graphic. Specifically, the modeling method of the optical proximity correction model of the present application can reduce the root mean square of the asymmetric graphic fitting error of the active layer from 2.5 to 1.5, and the accuracy is improved by 40%, and reduce the root mean square of the fitting error of the asymmetric graphic in the metal layer from 2.8 to 1.4, and the accuracy is improved by 50%.

[0112] See also Figure 7As shown, the present invention also provides a modeling system for an optical proximity correction model, and the modeling system includes a data acquisition module 201, a pattern differentiation module 202, a kernel and parameter range adjustment module 203, a cost function setting module 204, an optical proximity correction and cost function output module 205, an optical proximity correction model output module 206, a judgment module 207, and a target optical proximity correction model output module 208. The data acquisition module 201 executes step S10 to acquire multiple target pattern data and wafer pattern data corresponding to each target pattern data. The pattern differentiation module 202 executes step S20 to distinguish the target pattern into symmetric patterns and asymmetric patterns. The kernel and parameter range adjustment module 203 executes step S30 to set the initial kernel and parameter range of the optical proximity correction model. The cost function setting module 204 executes step S40 to set a cost function representing the degree of fit. The optical proximity correction and cost function output module 205 executes step S50, inputs the target pattern data into the optical proximity correction model at each parameter within the parameter range, obtains simulated pattern data corresponding to the target pattern data, and obtains the cost function at each parameter based on the simulated pattern data and the wafer pattern data. The optical proximity correction model output module 206 executes step S60, outputs the optical proximity correction model that minimizes the cost function within the parameter range. The determination module 207 executes step S70, determines whether the minimum cost function is less than a preset cost. If the minimum cost function is less than the preset cost, the module executes step S80; if the minimum cost function is greater than or equal to the preset cost, the module returns to step S30. The target optical proximity correction model output module 208 executes step S80, and uses the optical proximity correction model corresponding to the minimum cost function as the target optical proximity correction model.

[0113] See also Figure 8 As shown, the present invention also provides a computer device, which can be a server. The computer device includes a processor, a memory, a network interface and a database connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile and / or volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with an external client via a network connection. When the computer program is executed by the processor, it implements the functions or steps of a modeling method of an optical proximity correction model.

[0114] In summary, the present invention provides a modeling method, system, and computer device for an optical proximity correction model, the modeling method comprising: acquiring a plurality of target graphic data and wafer graphic data corresponding to each of the target graphic data; distinguishing the target graphics into symmetric graphics and asymmetric graphics; setting an initial kernel and parameter range of the optical proximity correction model, and setting a cost function representing the degree of fit, wherein the cost function includes the standard deviation of all target graphics and the standard deviation of the asymmetric graphics; inputting the target graphic data into the optical proximity correction model at each parameter within the parameter range, acquiring simulated graphic data of the target graphic, and acquiring the cost function at each parameter based on the simulated graphic data and the wafer graphic data; outputting the optical proximity correction model at the minimum cost function within the parameter range; and determining whether the minimum cost function is less than a preset cost; if the minimum cost function is less than the preset cost, using the output optical proximity correction model as the target optical proximity correction model; and if the minimum cost function is greater than or equal to the preset cost, adjusting the kernel and parameter range of the optical proximity correction model until the minimum cost function is less than the preset cost.

[0115] The present invention provides a modeling method, system, and computer device for an optical proximity correction model, which have the unexpected effect of significantly improving the optical proximity correction model's processing capabilities for asymmetric design graphics, particularly the correction accuracy when processing asymmetric graphics of key components such as static random access memory. The modeled optical proximity correction model can more accurately predict and correct the optical distortion of asymmetric graphics, thereby directly improving the results of optical proximity correction and the yield of the product. By enabling the modeled optical proximity correction model to adapt to asymmetric graphics in the initial stage, the tedious adjustment and compensation work that engineers need to perform in the subsequent process is greatly reduced, thereby accelerating the R&D progress and improving production efficiency. In addition, the application of this technical solution reduces the reliance on subsequent manual adjustment process steps and parameter compensation, reduces the risk of compensation failure, and further ensures the accuracy of electrical performance and the reliability of the product.

[0116] The embodiments of the present invention disclosed above are intended only to illustrate the present invention. They do not describe all details in detail, nor do they limit the present invention to the specific embodiments described. Obviously, many modifications and variations are possible based on the content of this specification. These embodiments are selected and described in detail in this specification to better explain the principles and practical applications of the present invention, thereby enabling those skilled in the art to better understand and utilize the present invention. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. A method for modeling an optical proximity correction model, characterized in that: The following steps are involved: Acquire a plurality of target pattern data and wafer pattern data corresponding to each of the target pattern data; Distinguish target figures into symmetrical figures and asymmetrical figures; Setting an initial kernel and parameter range of an optical proximity correction model, and setting a cost function representing the degree of fit, wherein the cost function includes the standard deviation of all target patterns and the standard deviation of the asymmetric pattern; Under each parameter within the parameter range, inputting the target pattern data into the optical proximity correction model to obtain simulated pattern data of the target pattern, and obtaining a cost function under each parameter based on the simulated pattern data and the wafer pattern data; Outputting the optical proximity correction model when the cost function is minimized within the parameter range; and Determine whether the minimum cost function is less than a preset cost; if the minimum cost function is less than the preset cost, use the output optical proximity correction model as the target optical proximity correction model; if the minimum cost function is greater than or equal to the preset cost, adjust the kernel and parameter range of the optical proximity correction model until the minimum cost function is less than the preset cost.

2. The method for modeling an optical proximity correction model according to claim 1, wherein: Obtaining the cost function under each parameter involves the following steps: Select a parameter within the parameter range; Inputting all target graphic data into the optical proximity correction model to obtain a simulation data graphic corresponding to each target graphic data; Obtaining a cost function under the parameters according to the simulation graphic data and the wafer graphic data corresponding to each target graphic data; and Determine whether the parameters within the parameter range have been traversed. If the parameters within the parameter range have been traversed, end the process. If the parameters within the parameter range have not been traversed, select another parameter within the parameter range and repeat the process of obtaining the cost function.

3. The method for modeling an optical proximity correction model according to claim 1, wherein: The simulated pattern data is position data of two intersections of a light signal curve of a wafer pattern formed on a wafer by simulating a target pattern using the optical proximity correction model and an exposure threshold.

4. The method for modeling an optical proximity correction model according to claim 1, wherein: The cost function is obtained by the following formula: Cost function=A×(RMS-all / RMS-all-spec)+B×(RMS-part / RMS-part-spec); Among them, Cost function is the cost function, RMS-all is the standard deviation of the target graphics of all inputs, RMS-all-spec is the specification value of the standard deviation of the target graphics of all inputs, A is the weight coefficient of the target graphics of all inputs, RMS-part is the standard deviation of all the asymmetric graphics, RMS-part-spec is the specification value of the standard deviation of all the asymmetric graphics, and B is the weight coefficient of all the asymmetric graphics.

5. The method for modeling an optical proximity correction model according to claim 4, wherein: RMS-all is the root mean square of the fitting errors of all the target graphics, which is used to characterize the errors between the simulated graphics and the wafer graphics of all the target graphics. RMS-part is the root mean square of the fitting errors of all the asymmetric graphics, which is used to characterize the errors between the simulated graphics and the wafer graphics of all the asymmetric graphics.

6. The method for modeling an optical proximity correction model according to claim 4, wherein: The standard deviation RMS-all of the target graphs for all inputs is obtained by the following formula: RMS-all=SQRT(SUMSQ E(1,N) / COUNTA E(1,N)); The standard deviation RMS-part of all asymmetric patterns is obtained by the following formula: RMS-part=SQRT(SUMSQ E(b+1,N) / COUNTA E(b+1,N)); Among them, SQRT represents the square root function, SUMSQ represents the sum of squares function, COUNTA represents the function of counting non-empty units within a given range, E(1, N) is the set of errors between the simulation graphic data and the wafer graphic data corresponding to all the target graphics, and E(b+1, N) is the set of errors between the simulation graphic data and the wafer graphic data corresponding to the asymmetric graphics.

7. The method for modeling an optical proximity correction model according to claim 4, wherein: The value of the weight coefficient A of all the input target graphics is 0.8, and the value of the weight coefficient B of all the asymmetric graphics is 0.

2.

8. The method for modeling an optical proximity correction model according to claim 1, wherein: Differentiating a target graphic into a symmetrical graphic and an asymmetrical graphic includes the following steps: inputting the target pattern data and the wafer pattern data into the optical proximity correction model; Using the optical proximity correction model to simulate an optical signal curve of a wafer pattern formed by a target pattern on a wafer; Determining the exposure threshold according to the distance between two intersection points of the light signal curve and the exposure threshold being equal to the wafer pattern data; Obtaining distances from two intersection points of the light signal curve and the exposure threshold to the target graphic data, and determining two intersection points of the light signal curve and the exposure threshold; and Determine whether the distances from the two intersection points of the light signal curve and the exposure threshold to the target graphic data are equal. If the distances from the two intersection points of the light signal curve and the exposure threshold to the target graphic data are equal, determine that the target graphic is the symmetrical graphic. If the distances from the two intersection points of the light signal curve and the exposure threshold to the target graphic data are not equal, determine that the target graphic is the asymmetrical graphic.

9. A modeling system for an optical proximity correction model, characterized in that: include: A data acquisition module, which acquires a plurality of target graphic data and wafer graphic data corresponding to each of the target graphic data; A graphic distinction module, which distinguishes target graphics into symmetrical graphics and asymmetrical graphics; Kernel and parameter range adjustment module, which sets the initial kernel and parameter range of the optical proximity correction model; A cost function setting module is configured to set a cost function representing the degree of fitting, wherein the cost function includes the standard deviation of all target graphics and the standard deviation of the asymmetric graphics; an optical proximity correction and cost function output module, which inputs the target pattern data into the optical proximity correction model at each parameter within the parameter range, obtains simulated pattern data of the target pattern, and obtains a cost function at each parameter based on the simulated pattern data and the wafer pattern data; An optical proximity correction model output module, configured to output the optical proximity correction model when the cost function is minimized within the parameter range; as well as A judgment module, which judges whether the minimum cost function is less than a preset cost; as well as a target optical proximity correction model output module, which uses the output optical proximity correction model as the target optical proximity correction model when the minimum cost function is less than the preset cost; If the minimum cost function is greater than or equal to the preset cost, the kernel and parameter range adjustment module adjusts the kernel and parameter range of the optical proximity correction model until the minimum cost function is less than the preset cost.

10. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the processor implements the steps of the method for modeling an optical proximity correction model according to any one of claims 1 to 8.

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