OPC modeling method, system and computer readable storage medium

Abstract

The present invention provides an OPC modeling method, system and computer-readable storage medium, which are applied to the field of semiconductor technology and include determining an optical signal differentiation function of an initial OPC model and its corresponding initial optical model, and establishing a target cost function based on the optical signal differentiation function, so that by adding a differentiated optical signal differentiation function to the existing cost function, the modeling process of the OPC model is optimized not only towards a trend of better fitting, but also towards a trend of minimizing the optical signal difference between the OPC model and its corresponding optical model, so as to avoid over-fitting of the OPC model and reduce the heterodyne of the OPC model, that is, to improve the modeling efficiency and accuracy of the OPC model, that is, to improve the accuracy of the OPC correction.

Description

Technical Field

[0001] The present invention relates to the field of semiconductor technology, and in particular to an OPC modeling method, system and computer-readable storage medium. Background Art

[0002] In semiconductor manufacturing, as the design size continues to shrink, the diffraction effect of light becomes more and more obvious, resulting in the optical proximity effect (OPE). Generally, before using the lithography machine to actually operate on the wafer, in order to correct the OPE phenomenon, the optical proximity correction (OPC) model is first used to offset the OPE effect as much as possible, so that the lithography pattern obtained based on the OPC model is close to the target pattern that the user actually wants to obtain.

[0003] However, with the gradual advancement of process nodes, more and more kernel functions are selected into the kernel library to improve the efficiency of establishing the OPC model. As a result, different signal strengths are matched with different thresholds to obtain the same model simulation data, which is referred to as over fitting. This reduces the accuracy of the OPC correction results, affecting product yield and R&D progress. Summary of the invention

[0004] The object of the present invention is to provide an OPC modeling method, system and computer-readable storage medium to avoid over-adaptation of the OPC model and reduce the heterodyne of the OPC model, that is, to improve the modeling efficiency and accuracy of the OPC model and also improve the accuracy of the OPC correction.

[0005] In a first aspect, in order to solve the above technical problems, the present invention provides an OPC modeling method, comprising: acquiring a wafer data set, wherein the wafer data set comprises actual measurement values ​​of key dimensions of a target pattern on a mask after exposure on a wafer.

[0006] Based on the wafer data set, an initial OPC model of the mask is determined, wherein the initial OPC model includes an initial optical model and an initial resist model.

[0007] Based on the light intensity thresholds of the initial OPC model and its corresponding initial optical model and the simulation graph curve of the target graph, the optical signal differentiation function of the initial OPC model and its corresponding initial optical model is determined.

[0008] Based on the optical signal differentiation function, a target cost function is established.

[0009] The initial OPC model is adjusted using the target cost function to obtain a target OPC model of the target graphic.

[0010] In some optional examples, the step of establishing the initial OPC model may include: determining the parameters of the optical model and the value range of the parameters, determining the parameters of the resist model and the value range of the parameters, the parameters of the optical model are parameters characterizing the optical characteristics of the lithography machine, and the parameters of the resist model are parameters characterizing the resist characteristics of the lithography machine.

[0011] For the parameters of the optical model or the parameters of the resist model, the corresponding parameters are valued within the value range to obtain multiple groups of value combinations required for an iterative operation of the parameters of the corresponding model, and the parameters are iteratively operated for a preset number of times, and in each iterative operation, the target graphics are respectively photolithographically simulated using the multiple groups of value combinations corresponding to the iterative operation to obtain multiple groups of photolithographic simulation critical dimensions, and based on the root mean square value of the difference between the multiple groups of photolithographic simulation critical dimensions and the wafer data set, the optical model and resist model corresponding to the minimum root mean square value are determined.

[0012] The optical model and the resist model corresponding to the minimum root mean square value are used as the initial optical model and the initial resist model, and the initial optical model and the initial resist model are integrated to obtain the initial OPC model.

[0013] In some optional examples, the simulation graphic curve of the target graphic may be used to describe the corresponding relationship between the intensity of light incident on the wafer and the change of the critical dimension of the lithography simulation obtained by the model.

[0014] In some optional examples, the optical signal differentiation function of the initial OPC model and the corresponding initial optical model may be:

[0015]

[0016] Among them, S1 is the area of ​​the closed area enclosed by the light intensity threshold of the initial OPC model and the corresponding simulation graphic curve of the target graphic, S2 is the area of ​​the closed area enclosed by the light intensity threshold of the initial optical model contained in the initial OPC model and the corresponding simulation graphic curve of the target graphic, ABS function is the absolute value function, and Diff is the light signal differentiation function.

[0017] In some optional examples, the target cost function may be:

[0018]

[0019] Among them, C is the target cost function, RMS -all is the standard deviation of the difference between N simulation values ​​of the initial OPC model and N exposure test data of N iterations performed in the process of establishing the initial OPC model, k is a coefficient, and k>0.

[0020] In some optional examples, the step of adjusting the initial OPC model using the target cost function may include: determining whether the target cost function value corresponding to the initial OPC model is less than a preset threshold; if not, returning to execute the steps of determining the parameters of the optical model and the value range of the parameters, and determining the parameters of the resist model and the value range of the parameters, until the target cost function value corresponding to the initial OPC model is less than the preset threshold.

[0021] In a second aspect, based on the same inventive concept, the present invention also provides an OPC modeling system, including: an acquisition module for acquiring a wafer data set, wherein the wafer data set includes actual measurement values ​​of key dimensions of a target pattern on a mask after exposure on a wafer.

[0022] The initial OPC model determination module is used to determine the initial OPC model of the mask based on the wafer data set, and the initial OPC model includes an initial optical model and an initial resist model.

[0023] The optical signal differentiation function determination module is used to determine the optical signal differentiation function of the initial OPC model and its corresponding initial optical model based on the light intensity threshold of the initial OPC model and its corresponding initial optical model and the simulation graphic curve of the target graphic.

[0024] The target cost function establishing module is used to establish a target cost function based on the optical signal differentiation function.

[0025] The target OPC model determination module is used to adjust the initial OPC model using the target cost function to obtain the target OPC model of the target graphic.

[0026] In some optional examples, the initial OPC model determination module may include:

[0027] The first determination unit is used to determine the parameters of the optical model and the range of the parameters, and determine the parameters of the resist model and the range of the parameters. The parameters of the optical model are parameters that characterize the optical characteristics of the lithography machine, and the parameters of the resist model are parameters that characterize the resist characteristics of the lithography machine.

[0028] The second determination unit is used to take values ​​of the corresponding parameters of the optical model or the parameters of the resist model within the value range to obtain multiple groups of value combinations required for an iterative operation of the parameters of the corresponding model, and perform a preset number of iterative operations on the parameters, and in each iterative operation, use the multiple groups of value combinations corresponding to the iterative operation to perform lithography simulation on the target graphics respectively to obtain multiple groups of lithography simulation critical dimensions, and based on the root mean square value of the difference between the multiple groups of lithography simulation critical dimensions and the wafer data set, determine the optical model and resist model corresponding to the minimum root mean square value.

[0029] The integration unit is used to use the optical model and the resist model corresponding to the minimum RMS value as the initial optical model and the initial resist model, and integrate the initial optical model and the initial resist model to obtain the initial OPC model.

[0030] In some optional examples, the target OPC model determination module can be specifically used to: determine whether the target cost function value corresponding to the initial OPC model is less than a preset threshold; if not, return to execute the steps of determining the parameters of the optical model and the value range of the parameters, and determining the parameters of the resist model and the value range of the parameters, until the target cost function value corresponding to the initial OPC model is less than the preset threshold.

[0031] In a third aspect, the present invention further provides an electronic device, comprising a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other via the communication bus;

[0032] Memory, used to store computer programs;

[0033] The processor is used to implement the above-mentioned OPC modeling method steps when executing the program stored in the memory.

[0034] In a fourth aspect, the present invention further provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the method steps of the OPC modeling described above are implemented.

[0035] Compared with the prior art, the technical solution provided by the present invention has at least one of the following beneficial effects:

[0036] The present invention provides an OPC modeling method, comprising: acquiring a wafer data set, wherein the wafer data set comprises actual measurement values ​​of key dimensions of a target pattern on a mask after exposure on the wafer; determining an initial OPC model of the mask based on the wafer data set, wherein the initial OPC model comprises an initial optical model and an initial resist model; determining an optical signal differentiation function of the initial OPC model and its corresponding initial optical model based on light intensity thresholds of the initial OPC model and its corresponding initial optical model and a simulation graphic curve of the target pattern; establishing a target cost function based on the optical signal differentiation function; and adjusting the initial OPC model using the target cost function to obtain a target OPC model of the target pattern.

[0037] In the present invention, the light intensity threshold of the OPC model and its corresponding optical model is used to determine the area ratio of the closed area enclosed by the simulation graphic curve of the target graphic, and then the light signal differentiation function that can determine the difference between the two is constructed, and then the light signal differentiation function is combined with the fitting degree function to obtain the target cost function, and the unexpected effect is obtained: based on the characteristic that the iterative calculation in the OPC modeling process will be optimized towards the trend of minimizing the target cost function, a differentiated light signal differentiation function is added to the existing cost function, so that the modeling process of the OPC model is not only optimized towards the trend of better fitting, but also optimized towards the trend of minimizing the light signal difference between the OPC model and its corresponding optical model, thereby avoiding over-fitting of the OPC model and reducing the heterodyne of the OPC model, that is, improving the modeling efficiency and accuracy of the OPC model, that is, improving the accuracy of the OPC correction. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] The accompanying drawings are used to provide a further understanding of the present application and constitute a part of the specification. Together with the following specific embodiments, they are used to explain the present application but do not constitute a limitation to the present application. In the accompanying drawings:

[0039] Figure 1 It is a flowchart of the OPC modeling method in the prior art.

[0040] Figure 2 For Figure 1 The modeling method shown is used to obtain a local simulation graph curve of an OPC model and its corresponding optical model.

[0041] Figure 3 For Figure 1 Another OPC model obtained by the modeling method shown and a local simulation graph curve of its corresponding optical model.

[0042] Figure 4 For Figure 1Another OPC model obtained by the modeling method shown and a local simulation graph curve of its corresponding optical model.

[0043] Figure 5 For Figure 1 A comparison chart of the fitting results of different active area graphics corresponding to the OPC model obtained by the modeling method shown.

[0044] Figure 6 For Figure 1 A comparison chart of the fitting results of different metal layer graphics corresponding to the OPC model obtained by the modeling method shown.

[0045] Figure 7 It is a flowchart of an OPC modeling method in one embodiment of the present invention.

[0046] Figure 8 This is an exemplary diagram of the area S1 of the closed region enclosed by the local simulation pattern of the initial OPC model under a certain light intensity threshold according to an embodiment of the present invention.

[0047] Fig. 9 This is an exemplary diagram of the area S2 of a closed region enclosed by a local simulation pattern of an initial lithography model included in an initial OPC model according to an embodiment of the present invention under a certain light intensity threshold.

[0048] Fig.10 For Figure 7 A comparison chart of the fitting results of different active area graphics corresponding to the OPC model obtained by the modeling method shown.

[0049] Fig.11 For Figure 7 A comparison chart of the fitting results of different metal layer graphics corresponding to the OPC model obtained by the modeling method shown.

[0050] In the drawings, the same reference numerals are used for the same components and the drawings are not drawn to scale. DETAILED DESCRIPTION

[0051] In order to make the technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions of the present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. Although the exemplary implementation methods of the present invention are shown in the accompanying drawings, it should be understood that the present invention can be implemented in various forms and should not be limited by the implementation methods described here. On the contrary, these implementation methods are provided in order to enable a more thorough understanding of the present invention and to fully convey the scope of the present invention to those skilled in the art.

[0052] In the process of making semiconductor chips, the photomask uses photolithography technology to form a pattern on the semiconductor; in order to copy the pattern on the wafer, the projected circuit is photoetched by an integrated circuit lithography machine. Its production and processing procedures generally include: exposure, development, photoresist removal, and photoetching. The general process of the photolithography process is: first obtain a specific graphic structure on the mask, and then copy the pattern on the mask to the silicon wafer through the photolithography equipment. However, the process of generating patterns through photolithography will produce more or less distortion, especially as the line width continues to decrease, the degree of distortion becomes more serious. Typically, such as corners become rounded or line ends are shortened. The reason for the above phenomena is due to the optical proximity effect (OPE), which is caused by the nonlinear filtering of the optical imaging system. The industry uses the optical proximity correction (OPC) technology to solve the above problems. In the optical proximity correction (OPC) technology, the pattern on the integrated circuit mask is pre-corrected to compensate for the distortion caused by the photolithography process, so that the corrected pattern can obtain the pre-designed graphic structure after photolithography.

[0053] Please refer to Figure 1 As shown, Figure 1 FIG. 1 is a flow chart of the prior art OPC modeling method. Figure 1 As shown, the prior art OPC modeling method generally includes: after obtaining N wafer data, selecting a kernel and a corresponding parameter range from a modeling program library, presetting a cost function representing the degree of fit, and using a processor to automatically iterate and calculate N times. Since the iterative calculation will optimize toward the trend of minimizing the cost function, the model with the best degree of fit can be obtained after N iterations. The smaller the cost function, the smaller the difference between the N simulation data and the N wafer data, which means the better the degree of fit, that is, the OPC model is close to the real lithography machine; if the degree of fit is within the corresponding standard range set by us, the modeling is completed. If it exceeds the corresponding standard range, it is necessary to reselect the kernel parameters and ranges respectively and calculate again. This is repeated until the degree of fit is less than the corresponding standard range, and the modeling is completed, so that the OPC model is used to obtain the target graphics that are close to the actual user's expectations.

[0054] At present, the OPC model is mainly composed of optical model and resist model. Compared with the optical model, the resist model has more empirical components and the prediction accuracy needs to be improved. However, due to the step-by-step advancement of semiconductor technology nodes, in order to make the fitting degree within the corresponding standard range, OPC modeling engineers have to use more and more resist model kernels in photoresists to establish OPC models.

[0055] Please refer to Figures 2 to 4 As shown, Figures 2 to 4 For Figure 1The modeling method shown in the figure shows the local simulation graphic curves corresponding to the three different OPC models and their corresponding optical models. Among them, the lithography simulation data (i.e., line width size) of the model is the difference (e.g., a2-a1) of the critical size of the horizontal axis obtained by simulating the mask pattern using the OPC model under a certain light intensity threshold (e.g., i1, i2, or i3) of the simulation graphic curve. By comparison, it can be seen that the three simulation graphic curves correspond to the same mask size under the same light intensity threshold and different OPC models; however, signals that are too steep or too flat are harmful to the OPC model, and overfitting will occur. The OPC model performs well for the N data taken in for modeling, but the prediction accuracy of the customer's design graphics is not high (i.e., the model heterodyne phenomenon).

[0056] Please refer to Figures 5 and 6 As shown, Figure 5 For Figure 1 The comparison chart of the fitting results of different active area graphics corresponding to the OPC model obtained by the modeling method shown, Figure 6 For Figure 1 The comparison chart of the fitting results of different metal layer graphics corresponding to the OPC model obtained by the modeling method shown. Figure 5 and Figure 6 In the figure, reference symbol patternM, such as pattern1, pattern2, pattern3, etc., is used to identify multiple different graphics, reference symbol fit error identifies the difference between the wafer data (water CD) of the graphics and its corresponding lithography simulation data (Model CD), and reference symbol spec and reference symbol spec- identify the difference interval (fit error) between the wafer data of multiple different graphics and their corresponding lithography simulation data.

[0057] like Figures 5 and 6 As shown, if the active area graphics and metal layer graphics are taken as examples, the root mean square (RMS) obtained by calibrating the N wafer data modeled by the OPC model is verified to be 1.1 (active area graphics) and 1.0 (metal layer graphics), and the RMS obtained by actually verifying the customer's design graphics using the OPC model is 3.4 (active area graphics) and 3.6 (metal layer graphics). Obviously, the root mean square (RMS) obtained by calibrating the N wafer data modeled by the OPC model is significantly different from the RMS obtained by actually verifying the customer's design graphics using the OPC model, which cannot meet the market demand for the accuracy of the OPC model and affects the product yield and R&D progress.

[0058] In order to solve the above problems, the present invention provides an OPC modeling method, system and computer-readable storage medium to avoid over-adaptation of the OPC model and reduce the heterodyne of the OPC model, that is, to improve the modeling efficiency and accuracy of the OPC model, that is, to improve the accuracy of the OPC correction.

[0059] Please refer to Figure 7 As shown, Figure 7 FIG. 1 is a flow chart of an OPC modeling method in an embodiment of the present invention. Figure 7 As shown, the OPC modeling method may include at least the following steps:

[0060] Step S701 , obtaining a wafer data set, wherein the wafer data set includes actual measurement values ​​of key dimensions of a target pattern on a mask after exposure on a wafer.

[0061] Step S702 : determining an initial OPC model of the mask based on the wafer data set, wherein the initial OPC model includes an initial optical model and an initial resist model.

[0062] Step S703 , determining the optical signal differentiation function of the initial OPC model and its corresponding initial optical model based on the light intensity threshold of the initial OPC model and its corresponding initial optical model and the simulation graph curve of the target graph.

[0063] Step S704: establishing a target cost function based on the optical signal differentiation function.

[0064] Step S705: using the target cost function to adjust the initial OPC model to obtain a target OPC model of the target graphic.

[0065] In the above step S701, based on the target pattern to be corrected on the mask (such as the metal layer pattern or the active area pattern), the actual silicon wafer (or wafer) under specific photolithography process conditions can be exposed and developed to obtain N wafer data (hereinafter referred to as wafer data set); wherein each wafer data can be obtained by sampling. Usually, a mask has multiple layers, such as active area layers, and each layer can include multiple graphics, such as active area graphics, and each graphic corresponds to a wafer data, and a wafer data corresponds to multiple sets of parameters of a graphic, and the above multiple sets of parameters include but are not limited to the line width, coordinates, size and spacing of the graphics. For ease of understanding, the OPC modeling method in the embodiment of the present invention exemplarily models a type of graphics on a layer, so this type of graphics can be called a target graphic, and its wafer data can be specifically the line width of the graphic, that is, the critical dimension.

[0066] In the above step S702, based on the wafer data set, the step of determining the initial OPC model of the mask can be specifically as follows: determining the parameters of the optical model and the value range of the parameters, determining the parameters of the resist model and the value range of the parameters, for the parameters of the optical model or the parameters of the resist model, taking values ​​for the corresponding parameters within the value range, obtaining multiple groups of value combinations required for one iterative operation of the parameters of the corresponding model, and performing a preset number of iterative operations on the parameters, and in each iterative operation, using the multiple groups of value combinations corresponding to the iterative operation to perform lithography simulation on the target graphics respectively to obtain multiple groups of lithography simulation critical dimensions, and based on the root mean square value of the difference between the multiple groups of lithography simulation critical dimensions and the wafer data set, determining the optical model and the resist model corresponding to the minimum root mean square value, using the optical model and the resist model corresponding to the minimum root mean square value as the initial optical model and the initial resist model, and integrating the initial optical model and the initial resist model to obtain the initial OPC model.

[0067] Specifically, the parameters of the optical model are parameters that characterize the optical characteristics of the lithography machine, such as describing the focusing position of the incident light inside the photoresist, the measurement position of the critical dimension scanning electron microscope (CDSEM), that is, the measurement position of the CDSEM during measurement, the optical numerical aperture (NA) of the lithography machine, the function of the lithography machine that describes the light intensity distribution at the edge position of the light source, and the function that describes the error distribution at the edge position of the mask, but are not limited to these; the parameters of the resist model are parameters that characterize the resist characteristics of the lithography machine, such as one or more of the refractive index of the photoresist, the film thickness, the optical propagation and the polarization effect, but are not limited to these; the simulation graphic curve of the target graphic is used to describe the corresponding relationship between the intensity of the light incident on the wafer and the change of the critical dimension of the lithography simulation obtained by the model.

[0068] In one embodiment, when performing OPC modeling, at least one parameter (hereinafter referred to as the first parameter) and its corresponding N groups of values ​​required for the corresponding optical model and resist model to perform the modeling can be determined first, and then the first parameters of the optical model and the resist model are trained to obtain the corresponding relationship between the first parameter and the photolithography critical dimension of the target pattern, that is, the optical model and the resist model are obtained, which is an iterative operation; then based on the same principle, the first parameter can be iterated for a preset number of times (represented by the N times); and after each iterative operation, the target pattern is respectively subjected to photolithography simulation using multiple groups of values ​​of the parameters corresponding to the iterative operation to obtain multiple groups of photolithography simulation critical dimensions, and then the RMS values ​​of the optical model and the resist model corresponding to each iteration can be obtained, and then the RMS values ​​corresponding to the N iterations can be sorted from small to large, and then the corresponding relationship between the first parameter corresponding to the minimum RMS value and the photolithography critical dimension of the target pattern, that is, the optical model and the resist model, is selected, and then the corresponding relationship between the selected optical model and the resist model is convolved to obtain the initial OPC model.

[0069] It should be understood that the above embodiment only takes the training of the optical model and the resist model using one parameter as an example, while in actual applications, in each iterative operation of OPC modeling, the optical model and the resist model need to use multiple parameters at the same time, such as the first parameter, the second parameter, the third parameter, etc. In this way, the optical model and the resist model obtained after each iterative operation specifically have the first parameter, the second parameter, and the third parameter as independent variables, and the lithography critical dimension of the target pattern (also a parameter) as the corresponding relationship of the dependent variable.

[0070] In the above step S703, since in the modeling process of the initial OPC model including the initial optical model and the initial resist model, the corresponding cost function only includes the fitting degree function (i.e., the RMS function) used to characterize the fitting degree between the OPC model and the actual lithography machine, there is bound to be a problem of model heterodyne. For this reason, in the embodiment of the present invention, based on the initial OPC model and its corresponding initial optical model determined in the above step S702, it is proposed to use the light intensity threshold of the initial OPC model and its corresponding initial optical model and the area ratio of the closed area surrounded by the simulation graphic curve of the target graphic to construct an optical signal differentiation function that can judge the difference between the two, and then use the optical signal differentiation function to reconstruct the target cost function in the OPC modeling process.

[0071] Specifically, the simulation graph curves of the initial OPC model and its corresponding initial optical model can be drawn first, and the area of ​​the closed area enclosed by the initial OPC model and its corresponding initial optical model under the respective determined light intensity thresholds can be determined, such as Figure 8S1 and Fig. 9 The S2 shown in the figure can be used to obtain the optical signal differentiation function shown in the following formula:

[0072]

[0073] Among them, S1 is the area of ​​the closed area enclosed by the light intensity threshold of the initial OPC model and the corresponding simulation graphic curve of the target graphic, S2 is the area of ​​the closed area enclosed by the light intensity threshold of the initial optical model contained in the initial OPC model and the corresponding simulation graphic curve of the target graphic, ABS function is the absolute value function, and Diff is the light signal differentiation function.

[0074] In the above step S704, since the iterative calculation in the OPC modeling process will be optimized towards the trend of minimizing the target cost function, the embodiment of the present invention proposes to combine the optical signal differentiation function with the fitting degree function to obtain the target cost function of the OPC model, so that the modeling process of the OPC model is not only optimized towards the trend of better fitting degree, but also optimized towards the trend of minimizing the optical signal difference between the OPC model and its corresponding optical model, thereby reducing the number of parameters in the resist modeling and reducing its impact on the accuracy of the OPC model, that is, reducing the heterodyne of the OPC model.

[0075] Specifically, the target cost function is:

[0076]

[0077] Among them, C is the target cost function, RMS - all is the standard deviation of the difference between N simulation values ​​of the initial OPC model and N exposure test data of N iterations performed in the process of establishing the initial OPC model, k is a coefficient, and k>0.

[0078] In the above step S705, the step of adjusting the initial OPC model using the target cost function includes: determining whether the target cost function value corresponding to the initial OPC model is less than a preset threshold; if not, returning to execute the step of determining the parameters of the optical model and the value range of the parameters, determining the parameters of the resist model and the value range of the parameters, until the target cost function value corresponding to the initial OPC model is less than the preset threshold, and if so, ending the modeling.

[0079] Specifically, after constructing the target cost function, it can be determined whether the target cost function value corresponding to the initial OPC model is less than the preset threshold. If not, it means that the number of parameters involved in the iterative calculation of the initial resist model is large, and the heterodyne of the target OPC model is larger, so it is necessary to reduce the number of parameters involved in the iterative calculation of the initial resist model. Specifically, it is possible to return to step S702, adjust the number of parameters of the resist model and their corresponding parameter values, and re-model the resist model to obtain the integrated OPC model after this iteration, and then determine whether its target cost function value is less than the preset threshold, and repeat this process until the final target cost value is less than the preset threshold OPC model, that is, the target OPC model.

[0080] Please refer to Figure 10 to Figure 11 As shown, combined with Figure 5 and Figure 6 ,in Fig.10 For Figure 7 A comparison diagram of the fitting results of different active area graphs corresponding to the OPC model obtained by the modeling method shown; Fig.11 For Figure 7 The comparison chart of the fitting results of different metal layer graphics corresponding to the OPC model obtained by the modeling method shown. Fig.10 and Fig.11 It can be seen that the root mean square (RMS) obtained by calibrating the N wafer data modeled using the target OPC model in the embodiment of the present invention is specifically 1.3 (active area graphics) and 1.2 (metal layer graphics), and the RMS obtained by actually verifying the customer design graphics using the target OPC model in the embodiment of the present invention is specifically 1.6 (active area graphics) and 1.4 (metal layer graphics). Among them, Fig.10 and Fig.11 The reference numeral vali - patternM, such as vali - pattern1、vali - pattern2、vali - pattern3 and the like identify multiple different graphics, the reference symbol Fit error identifies the difference between the wafer data (water CD) of the graphics and its corresponding lithography simulation data (Model CD), and the reference symbols spec and spec- identify the difference intervals (fit error) between the wafer data of multiple different graphics and their corresponding lithography simulation data.

[0081] By comparison Fig.10 and Figure 5 , and contrast Fig.11 and Figure 6It can be seen that compared with the prior art, for active area graphics, the RMS obtained by actual verification of customer design graphics using the OPC modeling method provided in the embodiment of the present invention is reduced from the existing 3.4 to 1.6, that is, the improvement rate is 53%; for metal layer graphics, the RMS obtained by actual verification of customer design graphics using the OPC modeling method provided in the embodiment of the present invention is reduced from the existing 3.6 to 1.4, that is, the improvement rate is 61%. Obviously, the OPC modeling method provided in the embodiment of the present invention achieves the problem of avoiding over-fitting of the OPC model and the problem of too large heterodyne of the OPC model, that is, improving the modeling efficiency and accuracy of the OPC model, that is, improving the accuracy of the OPC correction, and further reducing the expenditure cost of the mask, promoting the progress of research and development, and improving the product yield.

[0082] In addition, based on the same inventive concept, an OPC modeling system is also provided in an embodiment of the present invention, which may specifically include: an acquisition module, an initial OPC model determination module, an optical signal differentiation function determination module, a target cost function establishment module and a target OPC model determination module.

[0083] In one embodiment, the acquisition module is used to acquire a wafer data set, and the wafer data set includes actual measurement values ​​of key dimensions of a target pattern on a mask after exposure on a wafer; the initial OPC model determination module is used to determine an initial OPC model of the mask based on the wafer data set, and the initial OPC model includes an initial optical model and an initial resist model; the optical signal differentiation function determination module is used to determine an optical signal differentiation function of the initial OPC model and its corresponding initial optical model based on a light intensity threshold of the initial OPC model and its corresponding initial optical model and a simulation graphic curve of the target pattern; the target cost function establishment module is used to establish a target cost function based on the optical signal differentiation function; the target OPC model determination module is used to adjust the initial OPC model using the target cost function to obtain a target OPC model of the target pattern.

[0084] Specifically, the initial OPC model determination module may include: a first determination unit, used to determine the parameters of the optical model and the value range of the parameters, and determine the parameters of the resist model and the value range of the parameters, wherein the parameters of the optical model are parameters characterizing the optical characteristics of the lithography machine, and the parameters of the resist model are parameters characterizing the resist characteristics of the lithography machine; a second determination unit, used to take values ​​of the corresponding parameters within the value range for the parameters of the optical model or the parameters of the resist model, to obtain multiple groups of value combinations required for an iterative operation of the parameters of the corresponding model, and to perform a preset number of iterative operations on the parameters. The method comprises the steps of: performing lithography simulation on the target graphics respectively using the multiple groups of value combinations corresponding to the iterative operation in each iteration, so as to obtain multiple groups of lithography simulation key dimensions, and determining the optical model and the resist model corresponding to the minimum root mean square value based on the root mean square value of the difference between the multiple groups of lithography simulation key dimensions and the wafer data set; and an integration unit, which is used to use the optical model and the resist model corresponding to the minimum root mean square value as the initial optical model and the initial resist model, and integrate the initial optical model and the initial resist model to obtain the initial OPC model.

[0085] Specifically, the target OPC model determination module is used to determine whether the target cost function value corresponding to the initial OPC model is less than a preset threshold. If the target cost function value is greater than or equal to the preset threshold, the module returns to the steps of determining the parameters of the optical model and the parameter value range, and determining the parameters of the resist model and the parameter value range, until the target cost function value corresponding to the initial OPC model is less than the preset threshold. If the target cost function value is less than the preset threshold, the modeling is terminated directly.

[0086] In summary, the present invention provides an OPC modeling method, including: acquiring a wafer data set, the wafer data set including actual measurement values ​​of key dimensions of a target pattern on a mask after exposure on the wafer, determining an initial OPC model of the mask based on the wafer data set, the initial OPC model including an initial optical model and an initial resist model, determining an optical signal differentiation function of the initial OPC model and its corresponding initial optical model based on a light intensity threshold of the initial OPC model and its corresponding initial optical model and a simulation graphic curve of the target pattern, establishing a target cost function based on the optical signal differentiation function, and using the target cost function to adjust the initial OPC model to obtain a target OPC model of the target pattern.

[0087] In the present invention, the light intensity threshold of the OPC model and its corresponding optical model is used to determine the area ratio of the closed area enclosed by the simulation graphic curve of the target graphic, and then the light signal differentiation function that can determine the difference between the two is constructed, and then the light signal differentiation function is combined with the fitting degree function to obtain the target cost function, and the unexpected effect is obtained: based on the characteristic that the iterative calculation in the OPC modeling process will be optimized towards the trend of minimizing the target cost function, a differentiated light signal differentiation function is added to the existing cost function, so that the modeling process of the OPC model is not only optimized towards the trend of better fitting, but also optimized towards the trend of minimizing the light signal difference between the OPC model and its corresponding optical model, thereby avoiding over-fitting of the OPC model and reducing the heterodyne of the OPC model, that is, improving the modeling efficiency and accuracy of the OPC model, that is, improving the accuracy of the OPC correction.

[0088] An embodiment of the present invention further provides an electronic device, including a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other via the communication bus.

[0089] Memory, used to store computer programs;

[0090] The processor is used to implement an OPC modeling method provided by an embodiment of the present invention when executing a program stored in the memory.

[0091] In addition, other implementations of the OPC modeling method implemented by the processor executing the program stored in the memory are the same as the implementations mentioned in the aforementioned method embodiment part, and will not be repeated here.

[0092] The communication bus mentioned in the above control terminal can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. The communication bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, only one thick line is used in the figure, but it does not mean that there is only one bus or one type of bus.

[0093] The communication interface is used for communication between the above electronic device and other devices.

[0094] The memory may include a random access memory (RAM) or a non-volatile memory (NVM), such as at least one disk memory. Optionally, the memory may also be at least one storage device located away from the aforementioned processor.

[0095] The above-mentioned processor can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.

[0096] In another embodiment provided by the present invention, a computer-readable storage medium is provided, in which instructions are stored. When the computer-readable storage medium is run on a computer, the computer executes an OPC modeling method described in any one of the above embodiments.

[0097] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented by software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function described in the embodiment of the present invention is generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions may be transmitted from one website site, computer, server or data center to another website site, computer, server or data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium may be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more available media integrated. The available medium may be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid-state drive Solid State Disk (SSD)), etc.

[0098] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0099] Each embodiment in this specification is described in a related manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the device, electronic device, and computer-readable storage medium embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiments.

[0100] The above description is only a preferred embodiment of the present invention and is not intended to limit the protection scope of the present invention. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention are included in the protection scope of the present invention.

Claims

1. An OPC modeling method, characterized in that: include: Acquire a wafer data set, wherein the wafer data set includes actual measurement values ​​of key dimensions of a target pattern on a mask after exposure on a wafer; Determine an initial OPC model of the mask based on the wafer data set, wherein the initial OPC model includes an initial optical model and an initial resist model; Determine the optical signal differentiation function of the initial OPC model and the initial optical model corresponding thereto based on the light intensity threshold of the initial OPC model and the simulation graph curve of the target graph; Establishing a target cost function based on the optical signal differentiation function; The initial OPC model is adjusted using the target cost function to obtain a target OPC model of the target graphic; Specifically, the optical signal differentiation function of the initial OPC model and the corresponding initial optical model is: ； Wherein, S1 is the area of ​​the closed area enclosed by the light intensity threshold of the initial OPC model and the corresponding simulation graph curve of the target graph, S2 is the area of ​​the closed area enclosed by the light intensity threshold of the initial optical model contained in the initial OPC model and the corresponding simulation graph curve of the target graph, ABS function is the absolute value function, Diff is the optical signal differentiation function; and the target cost function is: ； Among them, C is the target cost function, RMS - all is the standard deviation of the difference between N simulation values ​​of the initial OPC model and N exposure test data of N iterations performed in the process of establishing the initial OPC model, k is a coefficient, and k>0.

2. The OPC modeling method according to claim 1, characterized in that: The steps of establishing the initial OPC model include: Determine the parameters of the optical model and the range of the parameters, determine the parameters of the resist model and the range of the parameters, the parameters of the optical model are the parameters characterizing the optical characteristics of the lithography machine, and the parameters of the resist model are the parameters characterizing the resist characteristics of the lithography machine; For the parameters of the optical model or the parameters of the resist model, the corresponding parameters are selected within the value range to obtain multiple groups of value combinations required for one iterative operation of the parameters of the corresponding model, and the parameters are iteratively operated for a preset number of times, and in each iterative operation, the target graphics are respectively photolithographically simulated using the multiple groups of value combinations corresponding to the iterative operation to obtain multiple groups of photolithographic simulation critical dimensions, and based on the root mean square value of the difference between the multiple groups of photolithographic simulation critical dimensions and the wafer data set, the optical model and resist model corresponding to the minimum root mean square value are determined; The optical model and the resist model corresponding to the minimum root mean square value are used as the initial optical model and the initial resist model, and the initial optical model and the initial resist model are integrated to obtain the initial OPC model.

3. The OPC modeling method according to claim 1, characterized in that: The simulation graph curve of the target graph is used to describe the corresponding relationship between the intensity of light incident on the wafer and the change of the critical dimension of the photolithography simulation obtained by the model.

4. The OPC modeling method according to claim 2, characterized in that: The step of adjusting the initial OPC model using the target cost function comprises: Determine whether the target cost function value corresponding to the initial OPC model is less than a preset threshold; If not, the process returns to the step of determining the parameters of the optical model and the value ranges of the parameters, and determining the parameters of the resist model and the value ranges of the parameters, until the target cost function value corresponding to the initial OPC model is less than the preset threshold.

5. An OPC modeling system, characterized in that: include: An acquisition module is used to acquire a wafer data set, wherein the wafer data set includes actual measurement values ​​of key dimensions of a target pattern on a mask after exposure on a wafer; An initial OPC model determination module is used to determine the initial OPC model of the mask based on the wafer data set, wherein the initial OPC model includes an initial optical model and an initial resist model; An optical signal differentiation function determination module, used to determine the optical signal differentiation function of the initial OPC model and its corresponding initial optical model based on the light intensity threshold of the initial OPC model and its corresponding initial optical model and the simulation graphic curve of the target graphic; A target cost function establishing module, used to establish a target cost function based on the optical signal differentiation function; A target OPC model determination module, used to adjust the initial OPC model using the target cost function to obtain a target OPC model of the target graphic; Specifically, the optical signal differentiation function of the initial OPC model and the corresponding initial optical model is: ； Wherein, S1 is the area of ​​the closed area enclosed by the light intensity threshold of the initial OPC model and the corresponding simulation graph curve of the target graph, S2 is the area of ​​the closed area enclosed by the light intensity threshold of the initial optical model contained in the initial OPC model and the corresponding simulation graph curve of the target graph, ABS function is the absolute value function, Diff is the optical signal differentiation function; and the target cost function is: ； Among them, C is the target cost function, RMS - all is the standard deviation of the difference between N simulation values ​​of the initial OPC model and N exposure test data of N iterations performed in the process of establishing the initial OPC model, k is a coefficient, and k>0.

6. The OPC modeling system according to claim 5, characterized in that: The initial OPC model determination module includes: A first determination unit is used to determine parameters of an optical model and a value range of the parameters, and to determine parameters of a resist model and a value range of the parameters, wherein the parameters of the optical model are parameters characterizing optical characteristics of a lithography machine, and the parameters of the resist model are parameters characterizing resist characteristics of a lithography machine; A second determination unit is used to take values ​​of the corresponding parameters of the optical model or the resist model within the value range, obtain multiple groups of value combinations required for one iterative operation of the parameters of the corresponding model, and perform a preset number of iterative operations on the parameters, and in each iterative operation, use the multiple groups of value combinations corresponding to the iterative operation to perform lithography simulation on the target graphics respectively to obtain multiple groups of lithography simulation critical dimensions, and determine the optical model and resist model corresponding to the minimum root mean square value based on the root mean square value of the difference between the multiple groups of lithography simulation critical dimensions and the wafer data set; The integration unit is used to use the optical model and the resist model corresponding to the minimum RMS value as the initial optical model and the initial resist model, and integrate the initial optical model and the initial resist model to obtain the initial OPC model.

7. The OPC modeling system according to claim 6, characterized in that: The target OPC model determination module is used for: Determine whether the target cost function value corresponding to the initial OPC model is less than a preset threshold; If not, the process returns to the step of determining the parameters of the optical model and the value ranges of the parameters, and determining the parameters of the resist model and the value ranges of the parameters, until the target cost function value corresponding to the initial OPC model is less than the preset threshold.

8. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the OPC modeling method according to any one of claims 1 to 4 are implemented.

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