Lithography simulation model construction method, device, equipment, medium and program product

Abstract

The invention discloses a photoetching simulation model construction method, device and equipment, a medium and a program product, and relates to the technical field of semiconductors. The method comprises the steps that multiple photoetching simulation parameters are obtained, the multiple photoetching simulation parameters at least comprise the key size between a first test pattern and a second test pattern in a photoetching simulation mask, and the second test pattern is an adjacent pattern of the first test pattern; determining a target diffusion coefficient of a target photochemical reaction substance in the exposed photoresist based on the plurality of photoetching simulation parameters; based on the target diffusion coefficient, a target reaction diffusion model of the target photochemical reaction substance in the post-baking process of photoetching simulation is constructed, and the target reaction diffusion model is used for simulating the reaction diffusion process of the target photochemical reaction substance in the post-baking process. According to the embodiment of the invention, the photoetching simulation model in the photoetching simulation post-baking process can be accurately constructed, so that the simulation accuracy of a strict photoetching simulation system in the post-baking process can be effectively improved.

Description

Technical Field

[0001] The present application belongs to the field of semiconductor technology, and in particular, relates to a method, device, equipment, medium and program product for constructing a lithography simulation model. Background Art

[0002] Currently, in the semiconductor integrated circuit manufacturing process, photolithography technology is the basic process for manufacturing circuit structures in electronic devices. With the development of electronic technology, structures with nanoscale feature sizes have gradually become the mainstream trend in semiconductor device manufacturing, and photolithography technology has become increasingly complex. As a layout design tool, strict photolithography simulation can assist semiconductor technicians in simulating photolithography processes, designing process windows, further shortening development cycles, and improving product quality.

[0003] In photolithography technology, post-exposure-baking (PEB) is an important step, and its main purpose is to promote the physical and chemical reactions in the photoresist, improve the properties of the photoresist, and volatilize the organic matter remaining on the surface of the photoresist, thereby improving the stability of the photolithography and the quality of the chip. In strict photolithography simulation, a strict photolithography simulation model is required to explain the physical and chemical changes caused by the combined action of acid, alkali and inhibitors in the photoresist under high temperature.

[0004] Based on this, whether it is possible to construct a reasonable and strict lithography simulation model for post-bake to effectively improve the simulation accuracy of the strict lithography simulation system in the post-bake process is of great significance to improving the current lithography process. Summary of the invention

[0005] The embodiments of the present application provide a method, device, equipment, medium and program product for constructing a lithography simulation model, which can more reasonably and accurately construct a lithography simulation model of the lithography simulation post-bake process, thereby effectively improving the simulation accuracy of the strict lithography simulation system in the post-bake process.

[0006] In a first aspect, an embodiment of the present application provides a method for constructing a lithography simulation model, the method comprising:

[0007] Acquire a plurality of lithography simulation parameters, wherein the plurality of lithography simulation parameters at least include a critical dimension between a first test pattern and a second test pattern in a lithography simulation mask, wherein the second test pattern is an adjacent pattern to the first test pattern;

[0008] Determining a target diffusion coefficient of a target photochemical reaction substance in the photoresist after exposure based on a plurality of photolithography simulation parameters, the target photochemical reaction substance comprising at least one of a photoacid, a base, and an inhibitor;

[0009] Based on the target diffusion coefficient, a target reaction diffusion model of the target photochemical reaction substance in the post-bake process of the lithography simulation is constructed. The target reaction diffusion model is used to simulate the reaction diffusion process of the target photochemical reaction substance in the post-bake process.

[0010] In some possible implementations, the photolithography simulation parameters further include local concentration information of a target photochemical reaction substance in a target grid point, where the target grid point is a measurement point divided in the first test pattern;

[0011] Based on the lithography simulation parameters, the target diffusion coefficient of the target photochemical reaction species in the photoresist after exposure is determined, including:

[0012] Based on the critical dimension between the first test pattern and the second test pattern and the local concentration information of the target photochemical reaction substance in the target grid point, the target diffusion coefficient corresponding to the target grid point is determined.

[0013] In some possible implementations, the first test pattern and the second test pattern are test patterns in a one-dimensional grating structure layout; and the critical dimension is the width of the opaque region between the first test pattern and the second test pattern.

[0014] In some possible implementations, the plurality of lithography simulation parameters further include local concentration information of a target photochemical reaction substance corresponding to a target grid point, where the target grid point is a measurement point divided in the first test pattern;

[0015] Determine the target diffusion coefficient of the target photochemical reaction species in the photoresist after exposure based on multiple lithography simulation parameters, including:

[0016] Based on a plurality of lithography simulation parameters, a target diffusion coefficient is calculated using a first diffusion coefficient calculation formula;

[0017] The first diffusion coefficient calculation formula is:

[0018]

[0019] in, is the target diffusion coefficient, D Q1 is the index control term, D Q2 is the linear control term, ω Q is the exponential coefficient, CD is the width of the opaque area between the first test pattern and the second test pattern, γ is the proximity coefficient, and [Q] is the local concentration information of the target photochemical reaction substance corresponding to the target grid point.

[0020] In some possible implementations, the first test pattern and the second test pattern are test patterns in a two-dimensional grating structure layout; the critical dimension is a distance between a target grid point and each reference grid point in n reference grid points, where n is a positive integer;

[0021] The target grid point is a measurement point divided in the first test pattern, and the n reference grid points are: measurement points located within a preset effective range centered on the target grid point and at edge positions of the layout divided in the second test pattern.

[0022] In some possible implementations, the plurality of lithography simulation parameters further include local concentration information of a target photochemical reaction substance corresponding to a target grid point, where the target grid point is a measurement point divided in the first test pattern; based on the plurality of lithography simulation parameters, determining a target diffusion coefficient of the target photochemical reaction substance in the photoresist after exposure includes:

[0023] Based on the plurality of lithography simulation parameters, a target diffusion coefficient is calculated by a second diffusion coefficient calculation formula;

[0024] The second diffusion coefficient calculation formula is:

[0025]

[0026] in,

[0027] g(CD)=θ1CD1+θ2CD2+…+θ n CD n

[0028] g(CD) is the average local adjacent critical dimension, CD1, CD2, ···, CD n is the distance between the target grid point and each of the n reference grid points, θ1, θ2, ···, θ n For CD1, CD2, ..., CD n One-to-one corresponding weights; is the target diffusion coefficient, D Q1 is the index control term, D Q2 is the linear control term, ω Q is the exponential coefficient, CD1, CD2, ···, CD n is the distance between the target grid point and each of the n reference grid points, γ is the proximity coefficient, and [Q] is the local concentration information of the target photochemical reaction substance corresponding to the target grid point.

[0029] In some possible implementations, based on the target diffusion coefficient, a target reaction-diffusion model of the target photochemical reaction substance in the post-bake process of the lithography simulation is constructed, including:

[0030] In the case where the target photochemical reaction substance is a photoacid, the target reaction-diffusion model constructed based on the target diffusion coefficient includes a first reaction-diffusion equation;

[0031] The first reaction-diffusion equation is:

[0032] In the case where the target photochemical reaction substance is a base, the target reaction-diffusion model constructed based on the target diffusion coefficient includes a second reaction-diffusion equation;

[0033] The second reaction-diffusion equation is:

[0034] When the target photochemical reaction substance is a base, the target reaction-diffusion model constructed based on the target diffusion coefficient includes a third reaction-diffusion equation;

[0035] The third reaction-diffusion equation is:

[0036] in, is the diffusion coefficient of the photoacid, is the diffusion coefficient of the base, k1 is the rate of the acid corrosion inhibitor, k2 is the thermal decomposition rate of the base, k3 is the thermal decomposition rate of the acid, k4 is the neutralization rate of the acid and base, k5 is the thermal decomposition rate of the base, and p, q, r, s are exponential control coefficients.

[0037] In some possible implementations, after constructing a target reaction-diffusion model of a target photochemical reaction substance in a post-bake process of a lithography simulation based on a target diffusion coefficient, the lithography simulation model construction method further includes:

[0038] Based on the target reaction diffusion model, the photoresist after exposure is subjected to post-baking treatment to obtain the concentration distribution information of the target photochemical reaction substance in the photoresist after post-baking;

[0039] Based on the concentration distribution information, the post-baked photoresist is simulated for development to obtain the morphology and size data of the developed photoresist.

[0040] Based on the same inventive concept, in a second aspect, an embodiment of the present application provides a lithography simulation model construction device, the lithography simulation model construction device comprising:

[0041] A first acquisition module is used to acquire a plurality of lithography simulation parameters, wherein the plurality of lithography simulation parameters at least include a critical dimension between a first test pattern and a second test pattern in a lithography simulation mask, wherein the second test pattern is an adjacent pattern of the first test pattern;

[0042] A first determination module is used to determine a target diffusion coefficient of a target photochemical reaction substance in the photoresist after exposure based on a plurality of photolithography simulation parameters, wherein the target photochemical reaction substance includes at least one of a photoacid, a base, and an inhibitor;

[0043] The first construction module is used to construct a target reaction diffusion model of the target photochemical reaction substance in the post-bake process of the lithography simulation based on the target diffusion coefficient. The target reaction diffusion model is used to simulate the reaction diffusion process of the target photochemical reaction substance in the post-bake process.

[0044] In a third aspect, an embodiment of the present application provides a lithography simulation model construction device, the lithography simulation model construction device comprising:

[0045] a processor and a memory storing computer program instructions;

[0046] When the processor executes the computer program instructions, the method for constructing a lithography simulation model as provided in any one of the above-mentioned embodiments of the present application is implemented.

[0047] In a fourth aspect, an embodiment of the present application provides a computer storage medium having computer program instructions stored thereon. When the computer program instructions are executed by a processor, a method for constructing a lithography simulation model as provided in any one of the above-mentioned embodiments of the present application is implemented.

[0048] In a fifth aspect, an embodiment of the present application provides a computer program product. When the instructions in the computer program product are executed by a processor of an electronic device, the electronic device executes a method for constructing a lithography simulation model as provided in any one of the above-mentioned embodiments of the present application.

[0049] The embodiments of the present application provide a method, device, equipment, medium and program product for constructing a lithography simulation model, by obtaining multiple lithography simulation parameters including at least a critical dimension between a first test pattern and a second test pattern in a lithography simulation mask, wherein the second test pattern is an adjacent pattern of the first test pattern. Then, based on the multiple lithography simulation parameters, a target diffusion coefficient of a target photochemical reaction substance in the photoresist after exposure is determined. In this way, a target reaction diffusion model of a target photochemical reaction substance in a post-bake process of lithography simulation is constructed by using the target diffusion coefficient improved based on the critical dimension between adjacent patterns, and the target reaction diffusion model can be used to simulate the reaction diffusion process of the target photochemical reaction substance in the post-bake process.

[0050] From the above description, it can be known that a method, device, equipment, medium and program product for constructing a lithography simulation model in an embodiment of the present application determines the value of the diffusion coefficient by introducing the critical dimension between adjacent graphics, and then determines the target reaction-diffusion model in the lithography simulation post-bake process according to the improved diffusion coefficient. In this way, by introducing the critical dimension between adjacent graphics as one of the conditions for controlling the diffusion coefficient into the reaction-diffusion model, the influence of adjacent graphics on the reaction-diffusion model in the post-bake process can be effectively simulated, and the lithography results under various layout conditions can be more effectively simulated, so that the lithography simulation model of the lithography simulation post-bake process can be constructed more reasonably and accurately, which is conducive to the lithography simulation results being closer to expectations, thereby being able to fully improve the simulation accuracy of the strict lithography simulation system in the post-bake process. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] In order to more clearly illustrate the technical solution of the embodiments of the present application, the following is a brief introduction to the drawings required for use in the embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0052] Figure 1 It is a schematic diagram for comparing simulation effects under different diffusion coefficients provided in one embodiment of the present application;

[0053] Figure 2 It is a flowchart of a method for constructing a lithography simulation model provided in one embodiment of the present application;

[0054] Figure 3 It is a schematic diagram of the exposure principle of photoresist in photolithography simulation provided by an embodiment of the present application;

[0055] Figure 4 It is a schematic diagram of the layout of the exposure area and the non-exposure area in the photoresist provided in one embodiment of the present application;

[0056] Figure 5 This is a schematic diagram of the principle of photoacid diffusion provided by an embodiment of the present application;

[0057] Figure 6 is a schematic diagram of the principle of photoacid diffusion provided by another embodiment of the present application;

[0058] Figure 7 is a flow chart of a method for constructing a lithography simulation model provided by another embodiment of the present application;

[0059] Figure 8 is a structural schematic diagram of a one-dimensional grating structure layout provided by an embodiment of the present application;

[0060] Fig. 9 is a structural schematic diagram of a two-dimensional grating structure layout provided by an embodiment of the present application;

[0061] Fig.10 is a structural schematic diagram of a two-dimensional grating structure layout provided by another embodiment of the present application;

[0062] Fig.11 It is a structural schematic diagram of a lithography simulation model building device provided in one embodiment of the present application;

[0063] Fig.12 It is a structural schematic diagram of a lithography simulation model building device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0064] The features and exemplary embodiments of various aspects of the present application will be described in detail below. In order to make the purpose, technical solutions and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only intended to explain the present application, rather than to limit the present application. For those skilled in the art, the present application can be implemented without the need for some of these specific details. The following description of the embodiments is only to provide a better understanding of the present application by illustrating the examples of the present application.

[0065] 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 statement "include..." do not exclude the presence of other identical elements in the process, method, article or device including the elements.

[0066] It should be noted that in the embodiments of the present application, certain software, components, models and other existing solutions in the industry may be mentioned, and they should be regarded as exemplary. Their purpose is only to illustrate the feasibility of implementing the technical solution of the present application, but it does not mean that the applicant has or will necessarily use the solution.

[0067] As described in the background technology section, in strict lithography simulation, a strict lithography simulation model is required to explain the physical and chemical changes caused by the interaction of acid, alkali and inhibitor in the photoresist under high temperature. In existing strict lithography simulation software, the reaction diffusion equation is usually used to describe the change law of acid, alkali and inhibitor in the post-bake process, and then the total post-bake time is decomposed into unit time through a discrete method, and the distribution of acid, alkali and inhibitor after a certain time can be iteratively calculated.

[0068] One of the most important parameters in the above reaction-diffusion equation is the diffusion coefficient. In existing photolithography simulations, it is considered that the diffusion length is an increasing function of the diffusion coefficient and time under ideal conditions. Figure 1 As shown, Figure 1 is a schematic diagram comparing simulation effects under different diffusion coefficients provided by an embodiment of the present application, Figure 1 The left half shows the simulation results when the lateral diffusion length is 10nm (nanometer). Figure 1 The right half shows the simulation results when the lateral diffusion length is 100nm (nanometer). Figure 1 From the simulation effect comparison diagram in the figure, we can see that with the increase of diffusion length, the acid in the photoresist will be more evenly distributed, and the interaction with the base and inhibitor will be more sufficient, which can effectively repair the standing wave effect generated by exposure in the photoresist. Therefore, in photolithography simulation, it is generally believed that the larger the diffusion coefficient is set, the better the simulation effect can be obtained in the end.

[0069] However, after careful study by the inventors of this application, it was found that with the improvement of photolithography technology, the critical dimension (CD) on the layout is getting smaller and smaller, which leads to the distance between the photoacid accumulation areas generated after exposure becoming relatively smaller. If the distance between adjacent graphics is less than the diffusion length, if the constant diffusion coefficient is still used, then the simulation results in the overlapping area of ​​the diffusion length will be abnormal, which will eventually lead to problems in the post-baking simulation results. Therefore, how to construct a reasonable and strict photolithography simulation model for post-baking is of great significance to improving the current photolithography process.

[0070] In view of the above, in order to solve the problems of the prior art, the embodiments of the present application provide a method, device, equipment, medium and program product for constructing a lithography simulation model, so as to effectively improve the simulation accuracy of a strict lithography simulation system in a post-bake process. It should be noted that the embodiments provided in the present application are not intended to limit the scope of the present application.

[0071] The following first introduces the method for constructing a lithography simulation model provided in an embodiment of the present application.

[0072] Figure 2The schematic diagram of the process of constructing a lithography simulation model provided by an embodiment of the present application is shown. The lithography simulation model construction method is applied to an electronic device, which may include a server or a user terminal. Figure 2 As shown, the lithography simulation model construction method includes the following steps:

[0073] S210, obtaining a plurality of lithography simulation parameters, wherein the plurality of lithography simulation parameters at least include a critical dimension between a first test pattern and a second test pattern in a lithography simulation mask, wherein the second test pattern is an adjacent pattern to the first test pattern;

[0074] S220, determining a target diffusion coefficient of a target photochemical reaction substance in the photoresist after exposure based on a plurality of photolithography simulation parameters, the target photochemical reaction substance comprising at least one of a photoacid, a base, and an inhibitor;

[0075] S230, constructing a target reaction-diffusion model of the target photochemical reaction substance in the post-bake process of the photolithography simulation based on the target diffusion coefficient, wherein the target reaction-diffusion model is used to simulate the reaction-diffusion process of the target photochemical reaction substance in the post-bake process.

[0076] The embodiment of the present application provides a method for constructing a lithography simulation model, by obtaining a plurality of lithography simulation parameters including at least a critical dimension between a first test pattern and a second test pattern in a lithography simulation mask, wherein the second test pattern is an adjacent pattern of the first test pattern. Then, based on the plurality of lithography simulation parameters, a target diffusion coefficient of a target photochemical reaction substance in the photoresist after exposure is determined. In this way, a target reaction diffusion model of a target photochemical reaction substance in a post-bake process of lithography simulation is constructed by using the target diffusion coefficient improved based on the critical dimension between adjacent patterns, and the target reaction diffusion model can be used to simulate the reaction diffusion process of the target photochemical reaction substance in the post-bake process.

[0077] From the above description, it can be seen that a method for constructing a lithography simulation model in an embodiment of the present application determines the value of the diffusion coefficient by introducing the critical dimensions between adjacent graphics, and then determines the target reaction-diffusion model in the lithography simulation post-bake process according to the improved diffusion coefficient. In this way, by introducing the critical dimensions between adjacent graphics as one of the conditions for controlling the diffusion coefficient into the reaction-diffusion model, the influence of adjacent graphics on the reaction-diffusion model in the post-bake process can be effectively simulated, and the lithography results under various layout conditions can be more effectively simulated, so that the lithography simulation model of the lithography simulation post-bake process can be constructed more reasonably and accurately, which is conducive to the lithography simulation results being closer to expectations, thereby being able to fully improve the simulation accuracy of the strict lithography simulation system in the post-bake process.

[0078] The specific implementation of the above steps 210 to 230 is described in detail below. It should be noted that, in order to facilitate a more comprehensive understanding of the technical effects achieved by the embodiments of the present application, the diffusion scenario between adjacent graphics is first described below.

[0079] See also Figure 3 In the photolithography process, a photolithography machine is usually used to project the pattern on the mask 100 through a light source onto the photoresist 200 on the silicon wafer for alignment and exposure. The mask 100 carries the designed circuit pattern (test pattern), and these patterns are transferred to the photoresist 200 through the exposure process. During the exposure process, the incident light enters the photoresist 200 through the mask 100, and the light incident on the photoresist 200 reacts with the photoacid generator and other substances in the photoresist 200 to produce a photochemical reaction. After the above exposure, partitions appear in the photoresist 200, as shown in the figure below. Figure 4 As shown, the black area D1 is the exposure area irradiated by the incident light, which will produce photoacid. The black area D1 usually corresponds to the test pattern in the mask 100; the diagonal area D2 is the unexposed area. The diagonal area D2 usually corresponds to the opaque area in the mask 100 except the test pattern.

[0080] After entering the post-bake process of the photolithography simulation, due to the increase in temperature, the acid, alkali, inhibitor and other substances in the photoresist 200 will accelerate diffusion and react. Figure 5 As shown, in general, acids and bases diffuse from areas with higher concentrations to areas with lower concentrations. Figure 5 It is reflected in the black area D1 to the white area D2 (corresponding to Figure 4 The slash area D2 in the figure diffuses, and finally reaches a globally uniform steady state at the end of the post-baking period. Figure 6 As shown, since the critical dimension CD of the opaque area between the test patterns on the mask plate is getting smaller and smaller, the distance between the accumulation areas of the photoacid and other substances generated on the photoresist 200 after exposure, that is, the critical dimension CD of the white area D2 is also relatively smaller, and even much smaller than the diffusion length of the photoacid, so that the diffusion process of the real photoacid and other substances is suppressed, and the actual diffusion length is shorter than the expected simulation length.

[0081] Therefore, in the embodiment of the present application, the critical dimension CD between adjacent graphics is introduced into the reaction-diffusion model as one of the conditions for controlling the diffusion coefficient, so as to effectively construct a reasonable and strict reaction-diffusion model for the post-bake process in lithography simulation. For specific implementation details, please refer to the following.

[0082] In S110, during specific implementation, a plurality of lithography simulation parameters are obtained, wherein the plurality of lithography simulation parameters at least include a critical dimension between a first test pattern and a second test pattern in a lithography simulation mask, and the second test pattern is an adjacent pattern of the first test pattern.

[0083] In this embodiment, the mask includes a plurality of test patterns in the lithography simulation, and these test patterns are used to ensure that the quality and performance of the mask meet the manufacturing standards. The test patterns may include specific patterns for evaluating parameters such as line width uniformity, overlay accuracy, and defects. For example, some test patterns may be designed as periodic structures to generate diffraction in the alignment system, thereby helping to achieve precise alignment between the mask and the existing patterns on the wafer. Based on this, the first test pattern and the second test pattern may specifically be adjacent test patterns in the mask.

[0084] Furthermore, the critical dimension between the first test pattern and the second test pattern can be flexibly defined according to the graphic position, shape and size of the first test pattern and the second test pattern in the mask. For example, when the first test pattern and the second test pattern in the mask are periodically designed, the critical dimension can be, for example, the spacing distance between the first test pattern and the second test pattern, the line width, etc., which is not strictly limited in the embodiments of the present application.

[0085] In S120, when specifically implemented, based on multiple lithography simulation parameters, a target diffusion coefficient of a target photochemical reaction substance in the photoresist after exposure is determined, and the target photochemical reaction substance includes at least one of a photoacid, a base, and an inhibitor. In this embodiment, after obtaining the above-mentioned lithography simulation parameters, the target diffusion coefficient of the above-mentioned target photochemical reaction substance can be calculated in combination with the lithography simulation parameters according to the actual simulation requirements of the lithography simulation, etc., and no strict limitation is made here.

[0086] In order to make the above determined target diffusion coefficient effectively simulate the influence of adjacent graphics on the reaction diffusion model in the post-bake process, so as to more effectively simulate the lithography results under various layout conditions, please refer to the following Figure 7 , Figure 7 It is a flow chart of a method for constructing a lithography simulation model provided in another embodiment of the present application.

[0087] like Figure 7As shown, considering that the diffusion of photoacid will be affected by the concentration of photoacid, this embodiment, under the premise of introducing the critical size between the above-mentioned adjacent test patterns as one of the conditions for controlling the diffusion coefficient into the reaction-diffusion model, also considers the concentration characteristics of local acid / base and other substances to calculate the target diffusion coefficient. Specifically, according to some embodiments of the present application, optionally, the above-mentioned multiple lithography simulation parameters can also include local concentration information of the target photochemical reaction substance corresponding to the target grid point, and the target grid point is the measurement point divided in the first test pattern.

[0088] The above step S220, determining the target diffusion coefficient of the target photochemical reaction substance in the photoresist after exposure based on multiple lithography simulation parameters, can be specifically replaced by Figure 7 Step 720 is shown:

[0089] S720 , determining a target diffusion coefficient corresponding to the target grid point based on the key size between the first test pattern and the second test pattern and the local concentration information of the target photochemical reaction substance corresponding to the target grid point.

[0090] Specifically, the target grid point refers to the smallest unit point / measurement point on the layout when designing a photolithography mask. The grid points in the first test pattern are arranged according to certain rules to ultimately form the basic structure of the first test pattern. The size and precision of the target grid point can be set and divided according to actual manufacturing and measurement requirements, and are not strictly limited here.

[0091] The local concentration information of the target photochemical reaction substance in the target grid point may be the local concentration information of the target photochemical reaction substance in the mapping area of ​​the target grid point on the photoresist. Taking the target photochemical reaction substance as photoacid as an example, the local concentration information of the target photochemical reaction substance in the target grid point may be the local concentration information of the photoacid at the corresponding photoacid grid point on the photoresist.

[0092] In this embodiment, the diffusion coefficient is improved by simultaneously considering the local concentration information of the target photochemical reaction substance, so that the improved diffusion coefficient is more accurate. This is conducive to building a more accurate lithography simulation model based on the diffusion coefficient to simulate the post-bake process in the future, thereby being able to fully improve the simulation accuracy of the strict lithography simulation system in the post-bake process.

[0093] In combination with different mask design scenarios in actual lithography simulations, in order to fully utilize the local concentration information of the target photochemical reaction substance corresponding to the key size and target grid points between the first test pattern and the second test pattern to more accurately determine the target diffusion coefficient, the calculation method of the target diffusion coefficient is described in detail below.

[0094] First see Figure 8 , Figure 8 1 is a schematic diagram of the structure of a one-dimensional grating structure layout provided by an embodiment of the present application. According to some embodiments of the present application, optionally, when the mask 100 is a one-dimensional grating structure layout 100, considering the different design situations of the mask 100 in the actual lithography simulation, in order to more accurately realize the determination of the critical size between adjacent patterns in the above-mentioned one-dimensional grating structure layout 100, the above-mentioned first test pattern 10 and the second test pattern 20 are test patterns in the one-dimensional grating structure layout 100. In this embodiment, the one-dimensional grating structure layout 100 generally refers to a periodic structure repeated in one direction, for example, a regularly arranged slit or ridge pattern.

[0095] The critical dimension CD is the width of the opaque area between the first test pattern 10 and the second test pattern 20. Specifically, in the test pattern of the mask during the exposure stage, the transparent and opaque areas jointly determine the pattern finally formed on the wafer. The design on the mask usually includes transparent and opaque parts.

[0096] In this embodiment, the first test pattern 10 and the second test pattern 20 can represent the light-transmitting area that needs to be exposed, and the mask area corresponding to the first test pattern 10 and the second test pattern 20 allows light to pass through, thereby forming the desired pattern on the photoresist. On the mask, the area between the first test pattern 10 and the second test pattern 20 is not transparent, and will block the light from passing through during the exposure process, so that the photoresist area corresponding to the area will not be exposed and maintain the original state. Therefore, the interval area between the first test pattern 10 and the second test pattern 20 is the opaque area between the first test pattern 10 and the second test pattern 20, and the width of the opaque area is defined as the critical dimension CD between adjacent patterns in this application.

[0097] Please continue to see Figure 8 According to some embodiments of the present application, optionally, under the premise that the first test pattern 10 and the second test pattern 20 are both test patterns in the one-dimensional grating structure layout 100, in order to more accurately determine the above-mentioned target diffusion coefficient, a specific calculation method of the target diffusion coefficient is provided below. Specifically, the multiple lithography simulation parameters may also include local concentration information of the target photochemical reaction substance corresponding to the target grid point, and the target grid point is the measurement point divided in the first test pattern;

[0098] The above-mentioned determination of the target diffusion coefficient of the target photochemical reaction substance in the photoresist after exposure based on the multiple photolithography simulation parameters may include:

[0099] Based on a plurality of lithography simulation parameters, a target diffusion coefficient is calculated using a first diffusion coefficient calculation formula;

[0100] The first diffusion coefficient calculation formula is:

[0101]

[0102] in, is the target diffusion coefficient, D Q1 is the index control term, D Q2 is the linear control term, ω Q is an exponential coefficient, CD is the width of the opaque region between the first test pattern 10 and the second test pattern 20, γ is a proximity coefficient, and [Q] is the local concentration information of the target photochemical reaction substance corresponding to the target grid point.

[0103] In this embodiment, under the premise that the first test pattern 10 and the second test pattern 20 are both test patterns in the one-dimensional grating structure layout 100, the critical dimension CD between adjacent patterns is used as one of the conditions for controlling the diffusion coefficient to calculate the diffusion coefficient, and the local concentration information of the target photochemical reaction substance is considered at the same time, thereby establishing a new exponential / linear mixed diffusion coefficient design method.

[0104] This is conducive to building a more accurate lithography simulation model based on the diffusion coefficient to simulate the post-bake process in the future, thereby fully improving the simulation accuracy of the strict lithography simulation system in the post-bake process and making the simulation results closer to expectations.

[0105] See the figure below, Fig. 9 Schematic diagram of the structure of a two-dimensional grating structure provided by an embodiment of the present application. Fig. 9 The mask shown is a non-periodic two-dimensional grating structure layout 100, taking a layout with an inflection point as an example. This type of layout is prone to photoresist accumulation at the inflection point (Corner), eventually forming a fillet.

[0106] Therefore, in order to simulate this kind of fillet strictly, please refer to Fig.10 , Fig.10 1 is a schematic diagram of the structure of a two-dimensional grating structure layout provided by another embodiment of the present application. According to some embodiments of the present application, optionally, considering the different design situations of the mask in the actual lithography simulation, in order to more accurately determine the critical size of the adjacent graphics in the above-mentioned two-dimensional grating structure layout 100, the above-mentioned first test pattern 10 and the second test pattern 20 are test patterns in the two-dimensional grating structure layout 100. The above-mentioned critical size is the distance between the target grid point and each reference grid point in the n reference grid points, and n is a positive integer.

[0107] The target grid point is a measurement point divided in the first test pattern 10 , and the n reference grid points are: measurement points located within a preset effective range centered on the target grid point and at edge positions of the layout divided in the second test pattern 20 .

[0108] In this embodiment, the two-dimensional grating structure layout 100 is more complex, which involves changes in the pattern arrangement in two orthogonal directions. This structure can be a rectangular arrangement of periodic holes or a columnar structure. In this case, Fig.10 For example, the critical size between the first test pattern 10 and the second test pattern 20 is the distance CD1, CD2, . . . , CD between the target grid point and each of the n reference grid points. n .

[0109] When selecting the n reference grid points, a circle may be drawn with the target grid point as the center and the preset size as the radius to determine the preset effective range. In this way, the measurement points within the preset effective range and at the edge of the layout in the second test pattern 20 are selected as the n reference grid points.

[0110] Please continue to see Fig. 9 According to some embodiments of the present application, optionally, under the premise that the first test pattern 10 and the second test pattern 20 are both test patterns in the two-dimensional grating structure layout 100, in order to more accurately determine the above-mentioned target diffusion coefficient, a specific calculation method of the target diffusion coefficient is further provided below. Specifically, the plurality of the above-mentioned photolithography simulation parameters may also include local concentration information of the target photochemical reaction substance corresponding to the target grid point, and the target grid point is the measurement point divided in the first test pattern 10; based on the plurality of photolithography simulation parameters, determining the target diffusion coefficient of the target photochemical reaction substance in the photoresist after exposure may include:

[0111] Based on the plurality of lithography simulation parameters, a target diffusion coefficient is calculated by a second diffusion coefficient calculation formula;

[0112] The second diffusion coefficient calculation formula is:

[0113]

[0114] in,

[0115] g(CD)=θ1CD1+θ2CD2+…+θ n CD n

[0116] g(CD) is the average local adjacent critical dimension, CD1, CD2, ···, CD n is the distance between the target grid point and each of the n reference grid points, θ1, θ2, ···, θn For CD1, CD2, ..., CD n One-to-one corresponding weights; is the target diffusion coefficient, D Q1 is the index control term, D Q2 is the linear control term, ω Q is the exponential coefficient, CD1, CD2, ···, CD n is the distance between the target grid point and each of the n reference grid points, γ is the proximity coefficient, and [Q] is the local concentration information of the target photochemical reaction substance corresponding to the target grid point.

[0117] In this embodiment, under the premise that the first test pattern 10 and the second test pattern 20 are both test patterns in the two-dimensional grating structure layout 100, the above-mentioned multiple key dimensions and their weights are combined to calculate the average local adjacent key dimension, and the local concentration information of the target photochemical reaction substance is considered at the same time, and the above-mentioned target diffusion coefficient is finally calculated by designing an exponential / linear mixture.

[0118] In this way, a more accurate lithography simulation model can be constructed based on the target diffusion coefficient to simulate the post-bake process, thereby fully improving the simulation accuracy of the strict lithography simulation system in the post-bake process and making the simulation results closer to expectations.

[0119] It should be added that the weights corresponding to the above-mentioned different key sizes may be randomly assigned, or may be assigned according to the actual distance, and this embodiment does not impose a strict limitation thereto.

[0120] In S130, based on the target diffusion coefficient, a target reaction-diffusion model of the target photochemical reaction substance in the post-bake process of the lithography simulation is constructed, and the target reaction-diffusion model is used to simulate the reaction-diffusion process of the target photochemical reaction substance in the post-bake process.

[0121] More specifically, the target reaction-diffusion model of the target photochemical reaction substance in the post-bake process of the lithography simulation based on the target diffusion coefficient may include:

[0122] In the case where the target photochemical reaction substance is a photoacid, the target reaction-diffusion model constructed based on the target diffusion coefficient may include a first reaction-diffusion equation;

[0123] The first reaction-diffusion equation is:

[0124] In the case where the target photochemical reaction substance is a base, the target reaction-diffusion model constructed based on the target diffusion coefficient may include a second reaction-diffusion equation;

[0125] The second reaction-diffusion equation is:

[0126] In the case where the target photochemical reaction substance is a base, the target reaction-diffusion model constructed based on the target diffusion coefficient may include a third reaction-diffusion equation;

[0127] The third reaction-diffusion equation is:

[0128] in, is the diffusion coefficient of the photoacid, is the diffusion coefficient of the base, k1 is the rate of the acid corrosion inhibitor, k2 is the thermal decomposition rate of the base, k3 is the thermal decomposition rate of the acid, k4 is the neutralization rate of the acid and base, k5 is the thermal decomposition rate of the base, and p, q, r, s are exponential control coefficients.

[0129] In this embodiment, the first reaction-diffusion equation, the second reaction-diffusion equation and the third reaction-diffusion equation corresponding to three different target photochemical substances, namely, photoacid, base and inhibitor, are specifically constructed according to the improved target diffusion coefficient.

[0130] In the actual post-bake process, by using the corresponding reaction-diffusion equations under the above-mentioned different target photochemical substances to simulate the influence of adjacent graphics on the reaction-diffusion in the post-bake process, it is possible to more effectively simulate the lithography results under various layout situations, thereby effectively improving the simulation accuracy of the strict lithography simulation system for the post-bake process.

[0131] According to some embodiments of the present application, optionally, in combination with the specific application scenario of the reaction-diffusion model in the actual lithography simulation process, after constructing the target reaction-diffusion model of the target photochemical reaction substance in the post-bake process of the lithography simulation based on the target diffusion coefficient, the lithography simulation model construction method may further include:

[0132] Based on the target reaction diffusion model, the photoresist after exposure is subjected to post-baking treatment to obtain the concentration distribution information of the target photochemical reaction substance in the photoresist after post-baking;

[0133] Based on the concentration distribution information, the post-baked photoresist is simulated for development to obtain the morphology and size data of the developed photoresist.

[0134] In specific implementation, based on the above-mentioned target reaction diffusion model, the photoresist after exposure is simulated baked by controlling the post-baking temperature, time, etc., to obtain the concentration distribution information of the target photochemical reaction substance (such as inhibitor) in the post-baked photoresist. Then, the post-baked photoresist is simulated developed to obtain the morphological size data of the developed photoresist. For example, the developer is used to dissolve the exposed and post-baked photoresist, so that the desired pattern can be formed.

[0135] In this embodiment, the diffusion in the post-bake process is precisely controlled by the above-mentioned target reaction diffusion model, and then simulated development is performed based on the diffused photoresist, thereby effectively optimizing the quality of the developed pattern, reducing pattern defects, and improving device performance and consistency.

[0136] Based on the lithography simulation model construction method provided in the above embodiment, out of the same inventive concept, the present application also provides a lithography simulation model construction device corresponding to the above lithography simulation model construction method. Fig.11 The device for building a lithography simulation model is introduced in detail.

[0137] Fig.11 A schematic diagram of the structure of a lithography simulation model building device provided in one embodiment of the present application is shown. Fig.11 The lithography simulation model building apparatus 1100 shown includes:

[0138] A first acquisition module 1110 is used to acquire a plurality of lithography simulation parameters, wherein the plurality of lithography simulation parameters at least include a critical dimension between a first test pattern and a second test pattern in a lithography simulation mask, wherein the second test pattern is an adjacent pattern of the first test pattern;

[0139] A first determination module 1120 is used to determine a target diffusion coefficient of a target photochemical reaction substance in the photoresist after exposure based on a plurality of photolithography simulation parameters, wherein the target photochemical reaction substance includes at least one of a photoacid, a base, and an inhibitor;

[0140] The first construction module 1130 is used to construct a target reaction-diffusion model of the target photochemical reaction substance in the post-bake process of the lithography simulation based on the target diffusion coefficient. The target reaction-diffusion model is used to simulate the reaction-diffusion process of the target photochemical reaction substance in the post-bake process.

[0141] The embodiment of the present application provides a lithography simulation model construction device, which obtains multiple lithography simulation parameters including at least a critical dimension between a first test pattern and a second test pattern in a lithography simulation mask by setting corresponding functional modules, wherein the second test pattern is an adjacent pattern of the first test pattern. Then, based on the multiple lithography simulation parameters, the target diffusion coefficient of the target photochemical reaction substance in the photoresist after exposure is determined. In this way, the target reaction diffusion model of the target photochemical reaction substance in the post-bake process of the lithography simulation is constructed by the above-mentioned target diffusion coefficient after the improvement based on the critical dimension between the adjacent patterns, and the target reaction diffusion model can be used to simulate the reaction diffusion process of the target photochemical reaction substance in the post-bake process.

[0142] From the above description, it can be known that a lithography simulation model construction device of an embodiment of the present application determines the value of the diffusion coefficient by introducing the critical dimension between adjacent graphics, and then determines the target reaction-diffusion model in the lithography simulation post-bake process according to the improved diffusion coefficient. In this way, by introducing the critical dimension between adjacent graphics as one of the conditions for controlling the diffusion coefficient into the reaction-diffusion model, the influence of adjacent graphics on the reaction-diffusion model in the post-bake process can be effectively simulated, and the lithography results under various layout conditions can be more effectively simulated, so that the lithography simulation model of the lithography simulation post-bake process can be constructed more reasonably and accurately, which is conducive to the lithography simulation results being closer to expectations, thereby being able to fully improve the simulation accuracy of the strict lithography simulation system in the post-bake process.

[0143] According to some embodiments of the present application, optionally, the above-mentioned multiple lithography simulation parameters may also include local concentration information of a target photochemical reaction substance in a target grid point, where the target grid point is a measurement point divided in the first test pattern;

[0144] The first determination module 1120 may be specifically used to determine a target diffusion coefficient corresponding to a target grid point based on a critical dimension between the first test pattern and the second test pattern and local concentration information of a target photochemical reaction substance in the target grid point.

[0145] According to some embodiments of the present application, optionally, the first test pattern and the second test pattern are test patterns in a one-dimensional grating structure layout; and the critical dimension is the width of the opaque region between the first test pattern and the second test pattern.

[0146] According to some embodiments of the present application, optionally, the plurality of lithography simulation parameters may further include local concentration information of a target photochemical reaction substance corresponding to a target grid point, where the target grid point is a measurement point divided in the first test pattern;

[0147] The first determination module 1120 may be specifically configured to: calculate a target diffusion coefficient using a first diffusion coefficient calculation formula based on a plurality of lithography simulation parameters;

[0148] The first diffusion coefficient calculation formula is:

[0149]

[0150] in, is the target diffusion coefficient, D Q1 is the index control term, D Q2 is the linear control term, ω Q is the exponential coefficient, CD is the width of the opaque area between the first test pattern and the second test pattern, γ is the proximity coefficient, and [Q] is the local concentration information of the target photochemical reaction substance corresponding to the target grid point.

[0151] According to some embodiments of the present application, optionally, the first test pattern and the second test pattern are test patterns in a two-dimensional grating structure layout; the critical dimension is a distance between a target grid point and each reference grid point in n reference grid points, where n is a positive integer;

[0152] The target grid point is a measurement point divided in the first test pattern, and the n reference grid points are: measurement points located within a preset effective range centered on the target grid point and at edge positions of the layout divided in the second test pattern.

[0153] According to some embodiments of the present application, optionally, the plurality of lithography simulation parameters may further include local concentration information of a target photochemical reaction substance corresponding to a target grid point, where the target grid point is a measurement point divided in the first test pattern; the first determination module 1120 may be specifically used to: calculate the target diffusion coefficient by using the second diffusion coefficient calculation formula based on the plurality of lithography simulation parameters;

[0154] The second diffusion coefficient calculation formula is:

[0155]

[0156] in,

[0157] g(CD)=θ1CD1+θ2CD2+…+θ n CD n

[0158] g(CD) is the average local adjacent critical dimension, CD1, CD2, ···, CD n is the distance between the target grid point and each of the n reference grid points, θ1, θ2, ···, θ n For CD1, CD2, ..., CD n One-to-one corresponding weights; is the target diffusion coefficient, D Q1 is the index control term, D Q2 is the linear control term, ω Q is the exponential coefficient, CD1, CD2, ···, CD n is the distance between the target grid point and each of the n reference grid points, γ is the proximity coefficient, and [Q] is the local concentration information of the target photochemical reaction substance corresponding to the target grid point.

[0159] According to some embodiments of the present application, optionally, the first construction module 1130 may be specifically used for: when the target photochemical reaction substance is a photoacid, the target reaction-diffusion model constructed based on the target diffusion coefficient may include a first reaction-diffusion equation;

[0160] The first reaction-diffusion equation is:

[0161] In the case where the target photochemical reaction substance is a base, the target reaction-diffusion model constructed based on the target diffusion coefficient may include a second reaction-diffusion equation;

[0162] The second reaction-diffusion equation is:

[0163] In the case where the target photochemical reaction substance is a base, the target reaction-diffusion model constructed based on the target diffusion coefficient may include a third reaction-diffusion equation;

[0164] The third reaction-diffusion equation is:

[0165] in, is the diffusion coefficient of the photoacid, is the diffusion coefficient of the base, k1 is the rate of the acid corrosion inhibitor, k2 is the thermal decomposition rate of the base, k3 is the thermal decomposition rate of the acid, k4 is the neutralization rate of the acid and base, k5 is the thermal decomposition rate of the base, and p, q, r, s are exponential control coefficients.

[0166] According to some embodiments of the present application, optionally, after constructing a target reaction-diffusion model of a target photochemical reaction substance in a post-bake process of a lithography simulation based on a target diffusion coefficient, the lithography simulation model construction device may further include:

[0167] A first post-baking module can be used to perform post-baking on the photoresist after exposure based on a target reaction diffusion model to obtain concentration distribution information of a target photochemical reaction substance in the photoresist after post-baking;

[0168] The first developing module can be used to simulate the development of the post-baked photoresist based on the concentration distribution information to obtain the shape and size data of the developed photoresist.

[0169] Based on the lithography simulation model construction method provided in the above embodiment, out of the same inventive concept, the present application also provides a lithography simulation device corresponding to the above lithography simulation model construction method. Fig.12 A detailed introduction to the equipment for building lithography simulation models is given.

[0170] See below Fig.12 , Fig.12 It is a structural schematic diagram of a lithography simulation model building device provided in one embodiment of the present application.

[0171] The lithography simulation model building device may include a processor 1201 and a memory 1202 storing computer program instructions.

[0172] Specifically, the processor 1201 may include a central processing unit (CPU), or an application specific integrated circuit (ASIC), or may be configured to implement one or more integrated circuits of the embodiments of the present application.

[0173] The memory 1202 may include a large capacity memory for data or instructions. By way of example and not limitation, the memory 1202 may include a hard disk drive (HDD), a floppy disk drive, a flash memory, an optical disk, a magneto-optical disk, a magnetic tape, or a universal serial bus (USB) drive or a combination of two or more of these. In appropriate cases, the memory 1202 may include a removable or non-removable (or fixed) medium. In appropriate cases, the memory 1202 may be inside or outside the integrated gateway disaster recovery device. In a specific embodiment, the memory 1202 is a non-volatile solid-state memory.

[0174] The memory may include read-only memory (ROM), random access memory (RAM), magnetic disk storage media devices, optical storage media devices, flash memory devices, electrical, optical or other physical / tangible memory storage devices. Thus, typically, the memory includes one or more tangible (non-transitory) computer-readable storage media (e.g., memory devices) encoded with software including computer-executable instructions, and when the software is executed (e.g., by one or more processors), it is operable to perform the operations described with reference to the method according to an aspect of the present disclosure.

[0175] The processor 1201 implements any one of the lithography simulation model building methods in the above embodiments by reading and executing computer program instructions stored in the memory 1202 .

[0176] In one example, the data lithography simulation model construction device may further include a communication interface 1203 and a bus 1210. Fig.12 As shown, the processor 1201, the memory 1202, and the communication interface 1203 are connected via a bus 1210 and communicate with each other.

[0177] The communication interface 1203 is mainly used to implement communication between various modules, devices, units and / or equipment in the embodiments of the present application.

[0178] Bus 1210 includes hardware, software or both, and the parts of lithography simulation model construction equipment are coupled to each other.For example, but not limitation, bus may include accelerated graphics port (AGP) or other graphics bus, enhanced industrial standard architecture (EISA) bus, front side bus (FSB), hypertransport (HT) interconnection, industrial standard architecture (ISA) bus, infinite bandwidth interconnection, low pin count (LPC) bus, memory bus, micro channel architecture (MCA) bus, peripheral component interconnection (PCI) bus, PCI-Express (PCI-X) bus, serial advanced technology attachment (SATA) bus, video electronics standard association local (VLB) bus or other suitable bus or two or more of these combinations. In appropriate cases, bus 1210 may include one or more buses. Although the present application embodiment describes and shows a specific bus, the application considers any suitable bus or interconnection.

[0179] The lithography simulation model construction device executes the lithography simulation model construction method in the embodiment of the present application, thereby realizing the lithography simulation model construction method described in the embodiment of the present application.

[0180] In addition, in combination with the lithography simulation model construction method in the above embodiment, the embodiment of the present application can provide a computer storage medium for implementation. The computer storage medium stores computer program instructions; when the computer program instructions are executed by a processor, any one of the lithography simulation model construction methods in the above embodiment is implemented.

[0181] Based on the lithography simulation model construction method in the above-mentioned embodiment, an embodiment of the present application provides a computer program product. When the instructions in the computer program product are executed by a processor of an electronic device, the electronic device executes the lithography simulation model construction method provided in any one of the above-mentioned embodiments of the present application.

[0182] It should be clear that the present application is not limited to the specific configuration and processing described above and shown in the figures. For the sake of simplicity, a detailed description of the known method is omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the method process of the present application is not limited to the specific steps described and shown, and those skilled in the art can make various changes, modifications and additions, or change the order between the steps after understanding the spirit of the present application.

[0183] The functional blocks shown in the above-described block diagram can be implemented as hardware, software, firmware or a combination thereof. When implemented in hardware, it can be, for example, an electronic circuit, an application specific integrated circuit (ASIC), appropriate firmware, a plug-in, a function card, etc. When implemented in software, the elements of the present application are programs or code segments that are used to perform the required tasks. The program or code segment can be stored in a machine-readable medium, or transmitted on a transmission medium or a communication link by a data signal carried in a carrier wave. "Machine-readable medium" can include any medium capable of storing or transmitting information. Examples of machine-readable media include electronic circuits, semiconductor memory devices, ROM, flash memory, erasable ROM (EROM), floppy disks, CD-ROMs, optical disks, hard disks, optical fiber media, radio frequency (RF) links, etc. The code segment can be downloaded via a computer network such as the Internet, an intranet, etc.

[0184] It should also be noted that the exemplary embodiments mentioned in this application describe some methods or systems based on a series of steps or devices. However, this application is not limited to the order of the above steps, that is, the steps can be performed in the order mentioned in the embodiment, or in a different order from the embodiment, or several steps can be performed simultaneously.

[0185] Aspects of the present disclosure are described above with reference to the flowchart and / or block diagram of the method, device (system) and computer program product according to the embodiment of the present disclosure. It should be understood that each box in the flowchart and / or block diagram and the combination of each box in the flowchart and / or block diagram can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device to produce a machine so that these instructions executed by the processor of the computer or other programmable data processing device enable the implementation of the function / action specified in one or more boxes of the flowchart and / or block diagram. Such a processor can be, but is not limited to, a general-purpose processor, a special-purpose processor, a special application processor, or a field programmable logic circuit. It can also be understood that each box in the block diagram and / or flowchart and the combination of boxes in the block diagram and / or flowchart can also be implemented by dedicated hardware that performs a specified function or action, or can be implemented by a combination of dedicated hardware and computer instructions.

[0186] The above is only a specific implementation of the present application. Those skilled in the art can clearly understand that for the convenience and simplicity of description, the specific working processes of the systems, modules and units described above can refer to the corresponding processes in the aforementioned method embodiments, and will not be repeated here. It should be understood that the protection scope of the present application is not limited to this. Any technician familiar with the technical field can easily think of various equivalent modifications or replacements within the technical scope disclosed in this application, and these modifications or replacements should be included in the protection scope of this application.

Claims

1. A method for constructing a lithography simulation model, characterized in that: The method comprises: Acquire a plurality of lithography simulation parameters, wherein the plurality of lithography simulation parameters at least include a critical dimension between a first test pattern and a second test pattern in a lithography simulation mask, wherein the second test pattern is an adjacent pattern to the first test pattern; Determining a target diffusion coefficient of a target photochemical reaction substance in the photoresist after exposure based on the plurality of photolithography simulation parameters, the target photochemical reaction substance comprising at least one of a photoacid, a base, and an inhibitor; Based on the target diffusion coefficient, a target reaction-diffusion model of the target photochemical reaction substance in the post-bake process of lithography simulation is constructed, and the target reaction-diffusion model is used to simulate the reaction-diffusion process of the target photochemical reaction substance in the post-bake process.

2. The method according to claim 1, characterized in that The plurality of photolithography simulation parameters further include local concentration information of the target photochemical reaction substance in a target grid point, and the target grid point is a measurement point divided in the first test pattern; The step of determining a target diffusion coefficient of a target photochemical reaction substance in the photoresist after exposure based on the plurality of photolithography simulation parameters comprises: Based on the critical dimension between the first test pattern and the second test pattern and the local concentration information of the target photochemical reaction substance in the target grid point, a target diffusion coefficient corresponding to the target grid point is determined.

3. The method according to claim 1 or 2, characterized in that: The first test pattern and the second test pattern are test patterns in a one-dimensional grating structure layout; the critical dimension is the width of the opaque region between the first test pattern and the second test pattern.

4. The method according to claim 3, characterized in that The plurality of photolithography simulation parameters further include local concentration information of the target photochemical reaction substance corresponding to a target grid point, wherein the target grid point is a measurement point divided in the first test pattern; The step of determining a target diffusion coefficient of a target photochemical reaction substance in the photoresist after exposure based on the plurality of photolithography simulation parameters comprises: Based on the plurality of lithography simulation parameters, calculating the target diffusion coefficient by using a first diffusion coefficient calculation formula; The first diffusion coefficient calculation formula is: in, is the target diffusion coefficient, D Q1 is the index control term, D Q2 is the linear control term, ω Q is an exponential coefficient, CD is a width of a light-proof region between the first test pattern and the second test pattern, γ is a proximity coefficient, and [Q] is the local concentration information of the target photochemical reaction substance corresponding to the target grid point.

5. The method according to claim 1 or 2, characterized in that: The first test pattern and the second test pattern are test patterns in a two-dimensional grating structure layout; the critical dimension is the distance between the target grid point and each reference grid point in n reference grid points, where n is a positive integer; The target grid point is a measurement point divided in the first test pattern, and the n reference grid points are measurement points located within a preset effective range centered on the target grid point and at edge positions of a layout divided in the second test pattern.

6. The method according to claim 5, characterized in that The plurality of photolithography simulation parameters further include local concentration information of the target photochemical reaction substance corresponding to a target grid point, wherein the target grid point is a measurement point divided in the first test pattern; The step of determining a target diffusion coefficient of a target photochemical reaction substance in the photoresist after exposure based on the plurality of photolithography simulation parameters comprises: Based on the plurality of lithography simulation parameters, calculating the target diffusion coefficient by a second diffusion coefficient calculation formula; The second diffusion coefficient calculation formula is: in, g(CD)=θ1CD1+θ2CD2+…+θ n CD n g(CD) is the average local adjacent critical dimension, CD1, CD2, ···, CD n is the distance between the target grid point and each of the n reference grid points, θ1, θ2, ···, θ n For CD1, CD2, ..., CD n One-to-one corresponding weights; is the target diffusion coefficient, D Q1 is the index control term, D Q2 is the linear control term, ω Q is the exponential coefficient, CD1, CD2, ···, CD n is the distance between the target grid point and each reference grid point in the n reference grid points, γ is the proximity coefficient, and [Q] is the local concentration information of the target photochemical reaction substance corresponding to the target grid point.

7. The method according to claim 1, characterized in that The step of constructing a target reaction-diffusion model of the target photochemical reaction substance in a post-bake process of photolithography simulation based on the target diffusion coefficient includes: In the case where the target photochemical reaction substance is a photoacid, the target reaction-diffusion model constructed based on the target diffusion coefficient includes a first reaction-diffusion equation; The first reaction-diffusion equation is: In the case where the target photochemical reaction substance is a base, the target reaction-diffusion model constructed based on the target diffusion coefficient includes a second reaction-diffusion equation; The second reaction diffusion equation is: In the case where the target photochemical reaction substance is a base, the target reaction-diffusion model constructed based on the target diffusion coefficient includes a third reaction-diffusion equation; The third reaction-diffusion equation is: in, is the diffusion coefficient of the photoacid, is the diffusion coefficient of the base, k1 is the rate of the acid corrosion inhibitor, k2 is the thermal decomposition rate of the base, k3 is the thermal decomposition rate of the acid, k4 is the neutralization rate of the acid and base, k5 is the thermal decomposition rate of the base, and p, q, r, s are exponential control coefficients.

8. The method according to claim 1, characterized in that After constructing a target reaction-diffusion model of the target photochemical reaction substance in a post-bake process of photolithography simulation based on the target diffusion coefficient, the method further includes: Based on the target reaction-diffusion model, post-baking the photoresist after the exposure process to obtain the concentration distribution information of the target photochemical reaction substance in the photoresist after the post-baking; Based on the concentration distribution information, the photoresist after post-baking is simulated developed to obtain the shape and size data of the photoresist after development.

9. A device for constructing a lithography simulation model, characterized in that: The device comprises: A first acquisition module is used to acquire a plurality of lithography simulation parameters, wherein the plurality of lithography simulation parameters at least include a critical dimension between a first test pattern and a second test pattern in a lithography simulation mask, wherein the second test pattern is an adjacent pattern of the first test pattern; A first determination module is used to determine a target diffusion coefficient of a target photochemical reaction substance in the photoresist after exposure based on a plurality of the photolithography simulation parameters, wherein the target photochemical reaction substance includes at least one of a photoacid, a base, and an inhibitor; The first construction module is used to construct a target reaction-diffusion model of the target photochemical reaction substance in the post-bake process of lithography simulation based on the target diffusion coefficient, and the target reaction-diffusion model is used to simulate the reaction-diffusion process of the target photochemical reaction substance in the post-bake process.

10. A lithography simulation model building device, characterized in that: The device comprises: a processor and a memory storing computer program instructions; When the processor executes the computer program instructions, the method for constructing a lithography simulation model as described in any one of claims 1 to 8 is implemented.

11. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer program instructions, and when the computer program instructions are executed by a processor, the method for constructing a lithography simulation model according to any one of claims 1 to 8 is implemented.

12. A computer program product, characterized in that When the instructions in the computer program product are executed by a processor of an electronic device, the electronic device executes the method for constructing a lithography simulation model as described in any one of claims 1 to 8.

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