Method, electronic device, storage medium and program product for establishing OPC model

Abstract

The present disclosure provides a method, electronic device, storage medium and program product for establishing an OPC model. The method includes constructing a loss function of a least absolute shrinkage and selection operator (LASSO) linear regression based on an original loss function and an L1 regularized loss function for the OPC model, wherein the original loss function is constructed based on an OPC model threshold to be determined, and wherein the L1 regularized loss function is constructed based on multiple light resistance coefficients to be determined. The method further includes determining the values ​​of multiple light resistance coefficients and the OPC model threshold based on the loss function of the LASSO linear regression to establish the OPC model.

Description

Technical Field

[0001] The present disclosure relates generally to integrated circuits, and more particularly, to methods, electronic devices, computer-readable storage media, and computer program products for building an optical proximity correction (OPC) model. Background Art

[0002] Photolithography is a key process in the manufacturing of integrated circuits. The photolithography process uses the principle of photochemical reaction and chemical and physical etching methods to transfer the pattern prepared on the mask to the substrate. The photolithography process can be described by optical and chemical models with the help of mathematical formulas. When light is irradiated on the mask, it diffracts. The diffraction is collected by the projection lens and converged on the surface of the photoresist. This imaging process is an optical process. The image projected on the photoresist stimulates a photochemical reaction, and after baking, the photoresist becomes partially soluble in the developer. This is a chemical process.

[0003] Usually, the pattern on the mask is projected onto the photoresist through an exposure system. Due to the imperfections of the optical system and the diffraction effect, the pattern on the photoresist is not completely consistent with the pattern on the mask. Optical proximity correction (OPC) is to use a calculation method to correct the pattern on the mask so that the pattern projected onto the photoresist meets the design requirements as much as possible. Summary of the invention

[0004] According to an example embodiment of the present disclosure, a scheme for establishing an optical proximity correction (OPC) model is provided.

[0005] In a first aspect of the present disclosure, a method for establishing an OPC model is provided. The method includes constructing a loss function of a least absolute shrinkage and selection operator (LASSO) linear regression based on an original loss function and an L1 regularized loss function for the OPC model, wherein the original loss function is constructed based on an OPC model threshold to be determined, and wherein the L1 regularized loss function is constructed based on multiple light resistance coefficients to be determined. The method further includes determining the values ​​of multiple light resistance coefficients and the OPC model threshold based on the loss function of the LASSO linear regression to establish the OPC model.

[0006] In a second aspect of the present disclosure, an electronic device is provided, comprising: one or more processors; and a storage device for storing one or more programs, which, when the one or more programs are executed by the one or more processors, enables the electronic device to execute the method according to the first aspect of the present disclosure.

[0007] In a third aspect of the present disclosure, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the method according to the first aspect of the present disclosure is implemented.

[0008] In a fourth aspect of the present disclosure, a computer program product is provided, which includes a program code, and when the program code is executed by a processor, the method according to the first aspect of the present disclosure is implemented.

[0009] It should be understood that the contents described in the summary of the invention are not intended to limit the key or important features of the embodiments of the present disclosure, nor are they intended to limit the scope of the present disclosure. Other features of the present disclosure will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] The above and other features, advantages and aspects of the embodiments of the present disclosure will become more apparent with reference to the following detailed description in conjunction with the accompanying drawings. In the accompanying drawings, the same or similar reference numerals represent the same or similar elements, wherein:

[0011] Figure 1 A schematic diagram illustrating an example architecture in which embodiments of the present disclosure can be implemented;

[0012] Figure 2 A flow chart of a method for establishing an OPC model according to an embodiment of the present disclosure is shown;

[0013] Figure 3A A schematic diagram exemplarily showing a test pattern;

[0014] Figure 3B As an example, Figure 3A The correspondence between the measured line width and the measured points of the lithographic pattern after the mask pattern of the test pattern in the exposure on the photoresist;

[0015] Figure 3C The OPC model according to the embodiment of the present disclosure is exemplified. Figure 3A A correspondence between a simulated line width and a simulated point of a simulated pattern obtained by simulating a test pattern in;

[0016] Figure 4 A flow chart showing a method for determining the signal strength of each simulation point according to an embodiment of the present disclosure;

[0017] Figure 5 A block diagram of an electronic device capable of implementing an embodiment of the present disclosure is shown. DETAILED DESCRIPTION

[0018] Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although certain embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be construed as being limited to the embodiments described herein, which are instead provided for a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the present disclosure are only for exemplary purposes and are not intended to limit the scope of protection of the present disclosure.

[0019] In the description of the embodiments of the present disclosure, the term "including" and similar terms should be understood as open inclusion, that is, "including but not limited to". The term "based on" should be understood as "based at least in part on". The term "one embodiment" or "the embodiment" should be understood as "at least one embodiment". The terms "first", "second", etc. may refer to different or the same objects. Other explicit and implicit definitions may also be included below.

[0020] As briefly mentioned above, optical proximity correction (OPC) is to use a calculation method to correct the pattern on the mask so that the pattern projected onto the photoresist meets the design requirements as much as possible. Model-based optical proximity correction (hereinafter referred to as "OPC model") has been widely used. Establishing an OPC model is a key step in the optical proximity correction process. When establishing the OPC model, the difference between the simulation result of the OPC model and the actual measurement result is minimized through fitting calculation. For example, the gap between the pattern simulated by the OPC model and the photoresist pattern is minimized.

[0021] In the process of modeling the OPC model, an algorithm is usually used to simulate a series of optical effects and photoresist effects in the lithography machine, and the optimal solution is obtained by optimizing the corresponding algorithm parameters, thereby establishing the OPC model. In the prior art, the commonly used algorithm is the genetic algorithm. The genetic algorithm is a search algorithm for solving optimization. In the process of applying the genetic algorithm to OPC modeling, a specified number of model parameters are generated according to the initial OPC seed model and algorithm parameters (for example, maximum value, minimum value, step size, etc.). The optimal solution is further obtained by calculating the generated model parameters, thereby establishing an OPC model with improved accuracy.

[0022] However, in practical applications, genetic algorithms can lead to a slow iterative convergence speed of the OPC model, and the optimal solution is strongly related to the generated algorithm parameters. Therefore, the ability to find the optimal solution through genetic algorithms is limited. Therefore, in the process of modeling the OPC model, a solution that can accelerate the model to find the optimal solution is desired, so that the iterative convergence speed of the OPC model is faster, and the established OPC model can also have a higher accuracy.

[0023] According to an embodiment of the present disclosure, a method for establishing an OPC model based on a LASSO linear regression algorithm is proposed. The method includes constructing a loss function of LASSO linear regression based on an original loss function and an L1 regularized loss function for the OPC model, wherein the original loss function is constructed based on an OPC model threshold to be determined, and wherein the L1 regularized loss function is constructed based on multiple light resistance coefficients to be determined. The method further includes determining the values ​​of multiple light resistance coefficients and the OPC model threshold based on the loss function of LASSO linear regression to establish the OPC model.

[0024] According to the method for establishing an OPC model proposed herein, the OPC model is modeled based on the LASSO linear regression algorithm. In the process of modeling the OPC model, the model can be accelerated to find the optimal solution, so that the iterative convergence speed of the OPC model is faster, and the established OPC model can also have a higher accuracy.

[0025] Figure 1 1 shows an example environment 100 in which various embodiments of the present disclosure can be implemented. Figure 1 As shown, electronic device 110 may receive first mask pattern 102 and target pattern 104. Target pattern 104 is a complete or partial wafer pattern expected to be obtained on a silicon wafer. First mask pattern 102 may be a mask pattern of a complete circuit layout or a portion thereof.

[0026] The first mask pattern 102 is associated with the target pattern 104. The first mask pattern 102 may be a mask pattern determined based on the target pattern 104. In some embodiments, the first mask pattern 102 may be the same as the target pattern 104. In some embodiments, the first mask pattern 102 may be a modified target pattern 104.

[0027] In some embodiments, an OPC model 112 is deployed in the electronic device 110. The OPC model 112 can use a calculation method to correct the mask pattern (e.g., the first mask pattern 102) and obtain a corrected mask pattern (e.g., the second mask pattern 106), so that the pattern projected onto the photoresist meets the design requirements as much as possible, for example, closer to the target pattern 104.

[0028] In some embodiments, the OPC model 112 in the electronic device 110 can determine the graphic edge in the target pattern 104 based on the received target pattern 104, and can obtain the graphic edge of the simulated pattern after the first mask pattern 102 is photolithographically processed through a simulation operation. The OPC model 112 in the electronic device 110 can compare the graphic edge in the target pattern 104 with the graphic edge of the simulated pattern to determine the edge placement error. The OPC model 112 corrects the first mask pattern 102 by minimizing the edge placement error or making it an acceptable value, and outputs the second mask pattern 106. Accordingly, the second mask pattern 106 is a corrected version of the first mask pattern 102 obtained based on the OPC model 112. Compared with the wafer pattern obtained based on the first mask pattern 102, the wafer pattern obtained based on the second mask pattern 106 is closer to the target pattern 104.

[0029] In the process of establishing an OPC model, an important indicator for evaluating the degree of fit of the OPC model is line width (critical dimension; CD; also known as "critical dimension"). For example, the measured line width of the photolithography pattern obtained after the mask pattern of the test pattern is exposed on the photoresist can be measured. The degree of fit of the OPC model is evaluated by comparing the measured line width with the simulated line width in the simulated pattern obtained by simulating the test pattern with the OPC model. The smaller the difference between the measured line width and the simulated line width, the better the degree of fit of the OPC.

[0030] According to an embodiment of the present disclosure, in the process of modeling the OPC model, the difference between the simulated line width of the graphics in the simulated pattern obtained by the OPC model simulation and the measured line width of the corresponding graphics in the lithography pattern is converted into a signal intensity difference between the signal intensity of the simulation point corresponding to the simulated line width and the signal intensity of the measured point corresponding to the measured line width. The smaller the signal intensity difference, the higher the accuracy of the model.

[0031] In some embodiments, the electronic device 110 can construct a loss function of a least absolute shrinkage and selection operator (LASSO) linear regression based on the original loss function and the L1 regularized loss function for the OPC model 112, wherein the original loss function is constructed based on an OPC model threshold to be determined, and wherein the L1 regularized loss function is constructed based on multiple light resistance coefficients to be determined. In some embodiments, the loss function of the LASSO linear regression characterizes the loss function of the OPC model. The electronic device 110 can also further determine the values ​​of multiple light resistance coefficients and the OPC model threshold based on the loss function of the LASSO linear regression to establish the OPC model 112.

[0032] The electronic device 110 may be any device with computing capabilities. As a non-limiting example, the electronic device 110 may be any type of fixed electronic device, mobile electronic device, or portable electronic device, including but not limited to a desktop computer, a laptop computer, a notebook computer, a netbook computer, a tablet computer, a multimedia computer, a mobile phone, a smart home device, a wearable electronic device, etc. In some embodiments, all or part of the components of the electronic device 110 may be distributed in the cloud. The present disclosure does not limit the specific type of the electronic device 110.

[0033] Figure 2 FIG. 2 is a flowchart of a method 200 for establishing an OPC model according to some embodiments of the present disclosure. Figure 1 Method 200 is described. Figure 2 The method 200 in can be Figure 1 In addition, the numbers in the flowchart do not represent the order in which these steps are executed. Some or all of these steps can be executed in parallel, or the execution order can be interchanged, and this is not limited in the present disclosure.

[0034] In block 202, the electronic device 110 may construct a loss function of a least absolute shrinkage and selection operator (LASSO) linear regression based on an original loss function and an L1 regularized loss function for an OPC model. In some embodiments, the original loss function may be constructed based on an OPC model threshold to be determined, and the L1 regularized loss function may be constructed based on a plurality of light blocking coefficients to be determined.

[0035] In some embodiments, the electronic device 110 can construct an original loss function for the OPC model 112. In some embodiments, the electronic device 110 can construct an original loss function based on the OPC model threshold to be determined and the signal strength of each simulation point in the multiple simulation points of the OPC model obtained. In some embodiments, the original loss function can be constructed based on the mean square error or variance of the signal strength of each simulation point and the OPC model threshold to be determined. The specific implementation process of constructing the original loss function will be described in detail below in conjunction with the formula.

[0036] In some embodiments, the OPC model threshold is a threshold to be determined and is used in the OPC model 112. In an algorithm based on LASSO linear regression, the OPC model threshold may be equivalent to an intercept term in the algorithm and is also a fitting value to be obtained by modeling the OPC model 112.

[0037] According to the method for establishing an OPC model based on the LASSO linear regression algorithm of the embodiment of the present disclosure, the model 112 is modeled by converting the difference between the simulated line width of the simulated graphic obtained by simulating the test pattern by the OPC model 112 and the measured line width of the corresponding graphic in the lithography pattern obtained after the mask pattern of the test pattern is subjected to lithography exposure into the signal intensity difference between the signal intensity of the simulation point corresponding to the simulated line width and the signal intensity of the measurement point corresponding to the measured line width, so as to obtain a more accurate OPC model 112 through a faster iterative convergence speed.

[0038] Figure 3A A schematic diagram showing a test pattern. The test pattern may be a target pattern provided by a user, for example, representing a wafer pattern that is expected to be obtained on a silicon wafer. Figure 3B Show that Figure 3A The correspondence between the measured line width and the measured point of the lithography pattern after the mask pattern of the test pattern in the exposure on the photoresist. Figure 3B As shown, the line width wafercd of the lithography pattern is Figure 3B The intersection of the line width with the edge of the lithographic pattern in the X direction can be defined as a "measurement point". Figure 3B As shown in FIG, the intersection point on the left side of the photolithography pattern is selected as the measurement point P wafer . With the measuring point P wafer The relative point P' wafer With the measurement point P wafer The same signal strength, therefore, can also be point P' wafer The measurement point is determined to be a measurement point, which is not limited in the present disclosure. In some embodiments, the line width wafercd of the photolithography pattern is generally known. The measurement point P can be determined based on the correspondence between the line width wafercd of the photolithography pattern and the measurement point by methods such as table lookup. wafer signal strength.

[0039] Figure 3C FIG. 1 shows a simulated line width simcd of a simulated pattern obtained by simulating a test pattern by the OPC model 112 according to an embodiment of the present disclosure. i (where i is a positive integer, 1≤i≤n; n is the number of simulation points in the model OPC model 112) and the simulation point p i The corresponding relationship. Figure 3C As shown, the simulation pattern may be a simulation pattern obtained after simulating the received test pattern (or target pattern). Figure 3C The line width of the simulated pattern is shown in simcd i The intersection of the line width with the edge of the graphic in the X direction can be defined as a "simulation point". Figure 3CAs shown in FIG, a point on the left side of the simulation pattern is selected as the simulation point P i . With the simulation point P i The relative point P' i With the simulation point P i The same signal strength, therefore, can also be point P' i The simulation point is determined, and the present disclosure does not limit this.

[0040] It is understandable that for multiple test patterns, the OPC model 112 can simulate and generate multiple corresponding simulation patterns, and accordingly, the OPC model 112 has multiple simulation points. Each simulation point is associated with a measurement point in the lithography pattern after the mask pattern of the test pattern is exposed on the photoresist.

[0041] In some embodiments, Figure 3C The simulation pattern in the example is a combination of OPC model 112 and Figure 3A After simulating the test pattern in Figure 3B The photolithographic pattern in Figure 3A The mask pattern of the test pattern in is obtained after exposure on the photoresist, Figure 3B The measuring point P in wafer and Figure 3C The simulation point P in i In some embodiments, the signal strength of the simulation point can be determined according to the optical effect signal strength and the light blocking effect signal strength at the simulation point. The determination process of the signal strength of the simulation point will be described in detail below.

[0042] In some embodiments, the electronic device 110 may also construct an L1 regularized loss function. In some embodiments, the electronic device 110 may construct an L1 regularized loss function based on multiple light blocking coefficients to be determined, wherein the signal strength of each simulation point is related to the multiple light blocking coefficients. In some embodiments, the L1 regularized loss function coefficient may be the sum of the absolute values ​​of the multiple light blocking coefficients. The specific construction method of the L1 regularized loss function will be described in detail below in conjunction with a specific formula.

[0043] In some embodiments, the plurality of light resistance coefficients correspond to a plurality of light resistance effect parameters. The light resistance effect signal strength of each simulation point can be obtained by: multiplying the plurality of light resistance effect parameters with the corresponding plurality of light resistance coefficients to obtain a plurality of products; then summing the plurality of products, and taking the summed value as the light resistance effect signal strength of each simulation point. Accordingly, the signal strength of each simulation point is related to the plurality of light resistance coefficients.

[0044] return Figure 2As shown in block 202, the electronic device 110 may construct a loss function of LASSO linear regression based on the original loss function and the L1 regularization loss function. In some embodiments, the loss function of LASSO linear regression represents the loss function of the OPC model.

[0045] By adding the L1 regularization loss function to the original loss function, more model weight coefficients can be made zero or very small, thereby preventing the established OPC model from overfitting by reducing the weight and lowering the weight value.

[0046] In block 204 , the electronic device 112 may determine a plurality of light resistance coefficient values ​​and an OPC model threshold based on a loss function of a LASSO linear regression to establish an OPC model.

[0047] In some embodiments, the OPC model threshold represents a reference signal strength associated with a reference lithographic pattern in the lithographic pattern set, and corresponds to a measured line width wafercd of the reference lithographic pattern. The plurality of lithographic patterns in the lithographic pattern set are lithographic patterns obtained by respectively exposing mask patterns of the plurality of test patterns on a photoresist. In some embodiments, each simulation point p in the simulation pattern i Signal strength sim i The simulated line width simcd corresponding to the graphics in the simulated pattern i (where i is a positive integer, 1≤i≤n, and n is the number of simulation points in the OPC model).

[0048] In some embodiments, the loss function of the LASSO linear regression can be minimized to determine multiple light resistance coefficient values ​​and OPC model thresholds. In some embodiments, based on the determined multiple light resistance coefficient values ​​and OPC model thresholds, multiple simulation points p i The corresponding simulation line width simcd i The weighted sum of multiple differences between the measured line width wafercd of the reference lithography pattern and the measured line width wafercd of the reference lithography pattern is minimized.

[0049] Advantageously, according to the method for establishing an OPC model according to an embodiment of the present disclosure, in the process of modeling the OPC model, based on the LASSO linear regression algorithm, the model can be accelerated to find the optimal solution, thereby making the iterative convergence speed of the OPC model faster, and can also make the established OPC model have higher accuracy.

[0050] Figure 4 A flow chart of a method 400 for determining the signal strength of each simulation point according to an embodiment of the present disclosure is shown. Figure 1 Method 400 is described. Figure 4 Method 400 in can be Figure 1 In addition, the numbers in the flowchart do not represent the order in which these steps are executed. Some or all of these steps can be executed in parallel, or the execution order can be interchanged, and this is not limited in the present disclosure.

[0051] At block 402, for each simulation point p i , the electronic device 110 can obtain each simulation point p i The optical effect signal intensity y i (where i is a positive integer, 1≤i≤n, and n is the number of simulation points in the OPC model 112). In some embodiments, the optical effect signal intensity of the simulation point can be determined based on at least one of the following optical effect parameters: light source distribution, light intensity, light source focal depth, and lens aperture value. In addition, it can be understood that the optical effect parameters can also include other optical effect parameters in addition to the parameters listed above, and the present disclosure does not limit this.

[0052] Generally, after the light source is determined, optical effect parameters such as light source distribution, illumination intensity, etc. have been determined. The electronic device 110 can set the optical effect parameters in the OPC model 112 according to the set light source. The electronic device 110 can use an algorithm known in the art to determine the optical effect signal intensity of each simulation point based on the optical effect parameters.

[0053] At block 404, the electronic device 110 may determine the light resistance effect based on the plurality of light resistance effect parameters x j (where j is a positive integer, 1≤j≤p, where p is the number of light resistance effect parameters in the OPC model) and a plurality of light resistance coefficients β to be determined corresponding to the plurality of light resistance effect parameters j (where j is a positive integer, 1≤j≤p, where p is the number of photoresistance effect parameters in the OPC model) to determine each simulation point p i The light blocking effect signal intensity b i In some embodiments, multiple light blocking coefficients β j is the light resistance coefficient to be determined. For example, the electronic device 110 can determine the multiple light resistance coefficients β by using a LASSO linear regression algorithm. j .

[0054] In some embodiments, the electronic device 110 may determine a plurality of light blocking coefficients β j Respectively with multiple photoresistance effect parameters x jThe electronic device 120 may add the obtained multiple products and use the sum of the multiple products as the simulation point p. i The light blocking effect signal intensity b i .

[0055] In some embodiments, the photoresist effect parameters may include one or more of the following items: simulated acid-base diffusion effect parameters and simulated post-drying shrinkage effect parameters. In addition, it is understood that the photoresist effect parameters may also include other photoresist effect parameters in addition to the above-mentioned parameters, which are not limited in the present disclosure.

[0056] In some embodiments, each simulation point p i The light blocking effect signal intensity b i It can be determined by the following formula (1):

[0057]

[0058] Where i is a positive integer, 1≤i≤n; where n is the number of simulation points in the OPC model. It can be understood that due to the multiple light resistance coefficients β j is the light blocking effect signal intensity b to be determined. i It is also to be determined.

[0059] In block 406, the electronic device 110 may calculate the optical effect signal strength y at each simulation point. i The light blocking effect signal intensity b i The signal strength difference Δs i In some embodiments, the intensity difference can be determined by the following formula (2):

[0060]

[0061] In block 408, the electronic device 110 may calculate the signal strength difference Δs i Determine for each simulation point p i signal strength.

[0062] Through the above process, the electronic device 110 can calculate each simulation p i The signal strength Δs i . And, each simulation p iThe signal strength is generated by the optical effect parameter and the light resistance effect parameter. In addition, in some embodiments, the values ​​of the optical effect parameter and the light resistance effect parameter used in the OPC model 112 can be generated by combining iterative algorithms such as genetic algorithms. In order to achieve the effect of model convergence faster, the modeling method according to the embodiment of the present disclosure adopts a LASSO linear regression algorithm to determine the light resistance coefficient for the light resistance effect parameter and the OPC model threshold.

[0063] The following will describe in detail a schematic process of constructing a loss function for the OPC model 112 based on the LASSO linear regression algorithm to establish the OPC model 112. In some embodiments, when constructing the loss function of the OPC model, the electronic device 110 can construct an original loss function based on the OPC model threshold to be determined and the acquired signal strength of each simulation point of the OPC model.

[0064] In some embodiments, Δs defined in formula (2) i For example, the electronic device 110 constructs the original loss function O loss When , it can be constructed according to the following formula (3):

[0065]

[0066] Wherein, β0 is the OPC model threshold to be determined. In the algorithm based on LASSO linear regression, the OPC model threshold can be equivalent to the intercept term in the algorithm, and is also the fitting value to be obtained by modeling the OPC model 112.

[0067] In some embodiments, the electronic device 110 may construct an L1 regularized loss function based on multiple light blocking coefficients to be determined. In some embodiments, the L1 regularized loss function coefficient may be the sum of the absolute values ​​of the multiple light blocking coefficients. For example, the electronic device may construct the L1 regularized loss function L based on formula (4): loss .

[0068] in, is a regularization coefficient used to control the strength of the penalty term. In some embodiments, It can be a predetermined value. By adding the L1 regularization loss function to the original loss function, more model weight coefficients can be made 0 or very few, thereby preventing the established OPC model from overfitting by reducing the weight and lowering the weight value.

[0069] exist Figure 2In block 202, the electronic device 110 may construct a loss function L of LASSO linear regression based on the original loss function and the L1 regularization loss function. In some embodiments, the loss function L may be determined by the following formula (5).

[0070] Therefore, through the above formula (5), a loss function based on LASSO linear regression is constructed, and the electronic device 110 can use the loss function L as the loss function of the OPC model 112.

[0071] In addition, in some embodiments, at each simulation point p i With weight i In the case of i weight i , to obtain the loss function L' of LASSO linear regression. For example, for each simulation point p i The corresponding weight can be weight i The electronic device 110 constructs the original loss function O' loss When , it can be constructed according to the following formula (6):

[0072]

[0073] Wherein, i is a positive integer, 1≤i≤n; and n is the number of simulation points in the OPC model. The electronic device 110 can construct an L1 regularization loss function L based on the above formula (4): loss .

[0074] Furthermore, considering each simulation point p i With corresponding weight weight i In the case of , the electronic device 110 can construct the loss function L' of LASSO linear regression based on the following formula (7):

[0075]

[0076] The electronic device 110 can construct a loss function of LASSO linear regression based on the above formula (5) or formula (7), and characterize the constructed loss function of LASSO linear regression as the loss function of the OPC model 112.

[0077] The electronic device 110 may determine a plurality of light blocking coefficients β based on the loss function of the LASSO linear regression. j(where j is a positive integer, 1≤j≤p, where p is the number of photoresistance effect parameters in the OPC model) and the OPC model threshold β0 to establish the OPC model 112 .

[0078] In some embodiments, the electronic device 110 can determine multiple light blocking coefficients β by minimizing the loss function L or L' of LASSO linear regression. j In the process of minimizing the loss function L or L', the electronic device 110 can use known algorithms such as coordinate descent method or minimum angle regression method or various algorithms developed in the future to solve the loss function L or L' to obtain multiple light blocking coefficients β j The value of and the value of the OPC model threshold β0.

[0079] Thus, the electronic device 110 can solve the fixed parameters (such as the optical effect parameters and the light-blocking effect parameters) to obtain the OPC model 112 with the optimal solution, and the OPC model 112 has a faster convergence speed and higher accuracy.

[0080] In some embodiments, the OPC model threshold β0 determined by solving the loss function L or L′ may represent a reference signal strength associated with a reference lithographic pattern in a lithographic pattern set obtained based on a test pattern set, and corresponds to a reference measured linewidth of the reference lithographic pattern.

[0081] In some embodiments, the test pattern set may include multiple test patterns for use in the process of establishing the OPC model 112. By exposing the mask pattern of each test pattern in the test pattern set on the photoresist, a corresponding photolithography pattern may be obtained, thereby obtaining a photolithography pattern set. Each photolithography pattern may have a corresponding measurement line width, and a measurement point associated with the corresponding measurement line width has a corresponding signal strength.

[0082] For example, the photolithography pattern set may include a plurality of photolithography patterns T1, T2, T3, T4, T5, T6, T7, T8, T9, T10, T11, T12, T13, T14, T15, T16, T17, T18, T20, T21, T31, T42, T5 2…… T m (m is a positive integer greater than or equal to 1, and represents the number of lithographic patterns in the lithographic pattern set). Each lithographic pattern has a corresponding measurement line width, and the measurement line width corresponds to the signal strength s of the corresponding measurement point. k (1≤k≤m; m represents the number of photolithography patterns in the photolithography pattern set.) Accordingly, each photolithography pattern has a signal strength at a corresponding measurement point.

[0083] In some embodiments, the OPC model threshold β0 can be obtained by solving the loss function L or L' (for example, minimizing the loss function L or L') and based on the signal strength of each lithography pattern in the lithography pattern set. For example, the electronic device 110 can use the lithography pattern in the lithography pattern set where the measurement point with the minimum signal strength difference compared with the preliminarily determined OPC model threshold is located as the reference lithography pattern, and use the signal strength associated with the reference lithography pattern as the determined OPC model threshold β0. The electronic device 110 can use the measurement point as the reference measurement point, and use the signal strength of the reference measurement point as the reference signal strength. In other words, the determined OPC model threshold β0 is the reference signal strength of the reference measurement point of the determined reference lithography pattern. In addition, the determined OPC model threshold β0 corresponds to the reference measurement line width of the reference lithography pattern.

[0084] For example, by solving the loss function L or L' (for example, minimizing the loss function L or L'), the value of the OPC model threshold β0 is initially determined to be x0. Assume that the lithography pattern set includes three lithography patterns: a first lithography pattern T1, a second lithography pattern T2, and a third lithography pattern T3, and the signal strength associated with each lithography pattern is s1, s2, and s3, that is, the signal strength associated with the first lithography pattern T1 is s1, the signal strength associated with the second lithography pattern T2 is s2, and the signal strength associated with the third lithography pattern T3 is s3. Assume that the electronic device 110 determines: |s1- x0|<|s2- x0|<|s3- x0|. Since the difference between the signal strength s1 associated with the first lithography pattern T1 and the initially determined OPC model threshold x0 is the smallest, the electronic device 110 can determine the first lithography pattern T1 as the reference lithography pattern, and use the signal strength s1 corresponding to the first lithography pattern as the determined OPC model threshold β0. When the differences between the signal strengths associated with multiple lithography patterns and the preliminarily determined OPC model threshold are all minimized, the electronic device 110 can select any one of the multiple lithography patterns as a reference lithography pattern, and use the reference signal strength corresponding to the reference lithography pattern as the determined OPC model threshold β0.

[0085] It is understandable that the above description is only for illustration. According to actual needs and applications, the test pattern set includes more or fewer test patterns, which is not limited in the present disclosure.

[0086] In some embodiments, the OPC model 112 is modeled by minimizing the loss function L or L', and the values ​​of multiple photoresistance coefficients for multiple photoresistance parameters in the OPC model 112 and the OPC model threshold β0 are determined, so that the weighted sum of the differences between the signal strengths of multiple simulation points and the reference signal strengths can be minimized. Accordingly, the weighted sum of the differences between multiple simulation line widths and the reference measured line widths can be minimized. In some embodiments, depending on actual needs, the weights corresponding to each simulation point can be the same or different, can be 1 or not 1, etc., and the present disclosure does not limit this.

[0087] In some embodiments, the OPC model 112 is modeled by minimizing the loss function L or L', and the values ​​of multiple photoresistance coefficients for multiple photoresistance parameters in the OPC model 112 and the OPC model threshold β0 are determined so that the mean square error of the difference between the signal strength of multiple simulation points and the reference signal strength is minimized.

[0088] Table 1 shows the comparison of the number of iterations and the line width difference between establishing an OPC model based on a LASSO linear regression algorithm according to an embodiment of the present disclosure and establishing an OPC model based on a genetic algorithm under the same conditions.

[0089] It can be seen from the data in Table 1 that, under the same conditions, the OPC model established by using the LASSO linear regression algorithm according to the embodiment of the present disclosure is significantly faster in iterative convergence speed than the OPC model established by using the genetic algorithm, and the accuracy of the model is significantly improved.

[0090] Figure 5 Schematic block diagram of an example device 500 that can be used to implement embodiments of the present disclosure. The device 500 can be used to implement Figure 1 Electronic device 110. As shown in the figure, device 500 includes a processing unit 501, such as a central processing unit (CPU), which can perform various appropriate actions and processes according to computer program instructions stored in a read-only memory (ROM) 502 or computer program instructions loaded from a storage unit 508 to a random access memory (RAM) 503. In RAM503, various programs and data required for the operation of device 500 can also be stored. Processing unit 501, ROM 502 and RAM503 are connected to each other via a bus 504. Input / output (I / O) interface 505 is also connected to bus 504.

[0091] A number of components in the device 500 are connected to the I / O interface 505, including: an input unit 506, such as a keyboard, a mouse, etc.; an output unit 507, such as various types of displays, speakers, etc.; a storage unit 508, such as a disk, an optical disk, etc.; and a communication unit 509, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 509 allows the device 500 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0092] The processing unit 501 performs the various methods and processes described above, such as methods 200 and 400. For example, in some embodiments, methods 200 and 400 may be implemented as a computer software program, which is tangibly contained in a machine-readable medium, such as a storage unit 508. In some embodiments, part or all of the computer program may be loaded and / or installed on the device 500 via the ROM 502 and / or the communication unit 509. When the computer program is loaded into the RAM 503 and executed by the processing unit 501, one or more steps of the methods 200 and 400 described above may be performed. Alternatively, in other embodiments, the processing unit 501 may be configured to perform methods 200 and 400 in any other suitable manner (e.g., by means of firmware).

[0093] The functions described above herein may be performed at least in part by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chip systems (SOCs), load programmable logic devices (CPLDs), and the like.

[0094] The program code for implementing the method of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that the program code, when executed by the processor or controller, enables the functions / operations specified in the flow chart and / or block diagram to be implemented. The program code may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0095] In the context of the present disclosure, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or device, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer 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 foregoing.

[0096] In addition, although each operation is described in a specific order, this should be understood as requiring such operation to be performed in the specific order shown or in a sequential order, or requiring that all illustrated operations should be performed to obtain desired results. Under certain circumstances, multitasking and parallel processing may be advantageous. Similarly, although some specific implementation details are included in the above discussion, these should not be interpreted as limiting the scope of the present disclosure. Some features described in the context of a separate embodiment can also be implemented in a single implementation in combination. On the contrary, the various features described in the context of a single implementation can also be implemented in multiple implementations individually or in any suitable sub-combination mode.

[0097] Although the subject matter has been described in language specific to structural features and / or methodological logical actions, it should be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or actions described above. On the contrary, the specific features and actions described above are merely example forms of implementing the claims.

Claims

1. A method for establishing an optical proximity effect correction (OPC) model, comprising: Based on an original loss function and an L1 regularized loss function for an OPC model, constructing a loss function of a minimum absolute value shrinkage and selection operator LASSO linear regression, wherein the original loss function is constructed based on an OPC model threshold to be determined, and wherein the L1 regularized loss function is constructed based on a plurality of light blocking coefficients to be determined; as well as Based on the loss function of LASSO linear regression, the values ​​of the plurality of light resistance coefficients and the OPC model threshold are determined to establish the OPC model.

2. The method according to claim 1, further comprising: Constructing the original loss function for the OPC model, comprising: The original loss function is constructed based on the OPC model threshold to be determined and the acquired signal strength of each simulation point among the multiple simulation points of the OPC model.

3. The method according to claim 2, further comprising: Constructing the L1 regularization loss function includes: Based on the multiple light blocking coefficients to be determined, the L1 regularized loss function is constructed, wherein the signal intensity of each simulation point is related to the multiple light blocking coefficients.

4. The method according to claim 1, further comprising: Obtaining the signal strength of each simulation point among the multiple simulation points of the OPC model, including: for each simulation point, obtaining the optical effect signal strength of each simulation point, The optical effect signal intensity is determined according to at least one of the following optical effect parameters: light source distribution, light intensity, light source focal depth and lens aperture value.

5. The method according to claim 4, wherein obtaining the signal strength of each simulation point further comprises: Multiplying the plurality of light blocking coefficients to be determined by a plurality of light blocking effect parameters respectively to obtain a plurality of products; as well as The sum of the multiple products is determined as the light blocking effect signal intensity of each simulation point.

6. The method according to claim 5, wherein obtaining the signal strength of each simulation point further comprises: Determine the signal intensity difference between the optical effect signal intensity and the light resistance effect signal intensity at each simulation point; as well as The signal strength difference is determined as the signal strength of each simulation point. 7 . The method according to claim 5 , wherein the photoresist effect parameter comprises at least one of the following items: a simulated acid-base diffusion effect parameter and a simulated post-drying shrinkage effect parameter. 8 . The method according to claim 2 , wherein the signal strength of each simulation point corresponds to a simulated line width of a test pattern associated with the simulation point simulated in the OPC model.

9. The method of claim 8, wherein the OPC model threshold represents a reference signal strength associated with a reference lithographic pattern in a lithographic pattern set and corresponds to a measured linewidth of the reference lithographic pattern, wherein a plurality of lithographic patterns in the lithographic pattern set correspond to a plurality of test patterns, respectively.

10. The method according to claim 9, wherein for the determined values ​​of the plurality of photoresistance coefficients and the OPC model threshold, a weighted sum of a plurality of differences between a plurality of simulated line widths corresponding to the plurality of simulation points in the OPC model and the measured line width is minimized.

11. The method according to claim 1, wherein determining the values ​​of the plurality of light blocking coefficients and the OPC model threshold based on the loss function of LASSO linear regression comprises: The loss function is minimized to determine the values ​​of the plurality of light resistance coefficients and the OPC model threshold.

12. The method of claim 1, wherein the loss function of the LASSO linear regression characterizes the loss function of the OPC model.

13. An electronic device, comprising: one or more processors; as well as A storage device for storing one or more programs, which, when executed by the one or more processors, enables the electronic device to execute the method as described in any one of claims 1 to 12.

14. A computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the method according to any one of claims 1 to 12.

15. A computer program product, comprising a program code, wherein when the program code is executed by a processor, the method according to any one of claims 1 to 12 is implemented.

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