Light source mask collaborative optimization method and device and storage medium

Abstract

A light source mask collaborative optimization method, device and storage medium, the method comprises: obtaining a typical lithography pattern in a target layout according to a design rule; performing light source mask collaborative optimization based on the typical lithography pattern, and outputting a light source mask collaborative optimization result; wherein the evaluation index used when performing the light source mask collaborative optimization includes edge placement error and imaging logarithmic slope variation. The method and device can expand the lithography process window while improving the dynamic stability of the imaging logarithmic slope to predict and stabilize the photoresist undercut cross section, ensuring the linearity of the etching deviation compensation rule table in the etching process development process, thereby improving the concentration and accuracy of the final etched detection critical dimension.

Description

Technical Field

[0001] This application relates to the field of semiconductor process technology, and more specifically to a method, apparatus and storage medium for collaborative optimization of light source mask. Background Technology

[0002] Source-mask optimization (SMO) is a resolution enhancement technique for advanced process nodes. It improves lithography resolution by simultaneously optimizing both the light source and the mask. The optimization direction and results of SMO are constrained by a cost function, which determines the optimal solutions for the light source and mask, and the degree of improvement in final lithography resolution. Current SMO cost functions and optimizations only consider and verify the lithography process, potentially leading to post-etching defects and significantly reducing and delaying process development efficiency and schedule. Summary of the Invention

[0003] This application is proposed to address the aforementioned problems. According to one aspect of this application, a light source mask collaborative optimization method is provided, which obtains a typical lithographic pattern in a target layout; performs light source mask collaborative optimization based on the typical lithographic pattern, and outputs the light source mask collaborative optimization result; wherein the evaluation indicators used in performing the light source mask collaborative optimization include edge placement error and the change in imaging logarithmic slope.

[0004] In the embodiments of this application, the step of performing light source mask collaborative optimization based on the typical lithographic pattern and outputting the light source mask collaborative optimization result includes: performing light source mask collaborative optimization based on the typical lithographic pattern and a first light source mask collaborative optimization evaluation function, and outputting the light source mask collaborative optimization result; wherein, the first light source mask collaborative optimization evaluation function is constructed based on edge placement error and the change in imaging logarithmic slope.

[0005] In the embodiments of this application, in the first light source mask collaborative optimization evaluation function, the optimization coefficient of the edge placement error is higher than the optimization coefficient of the imaging logarithmic slope change.

[0006] In the embodiments of this application, in the first light source mask collaborative optimization evaluation function, the optimization coefficient of the edge placement error is N times the optimization coefficient of the imaging logarithmic slope change, where N is greater than 1 and less than or equal to 5.

[0007] In the embodiments of this application, in the first light source mask collaborative optimization evaluation function, the weight coefficients corresponding to different process conditions involved in calculating the edge placement error and the change in the imaging logarithmic slope can be customized and adjusted independently according to different process requirements.

[0008] In the embodiments of this application, the light source mask co-optimization includes: solving the gradient of the first light source mask co-optimization evaluation function; determining the optimal solution descent direction based on the gradient; updating the light source and mask based on the optimal solution descent direction, reducing the value of the evaluation function until the edge placement error and the change in the imaging logarithmic slope achieve a process balance; using the common lithography process window of all typical lithography patterns as the evaluation standard, evaluating whether the depth of focus, imaging logarithmic slope, change in imaging logarithmic slope, and mask error enhancement factor meet the requirements; if the requirements are met, verifying the patterns other than the typical lithography patterns in the target layout based on the optimization results; if they can continuously pass the simulation and process evaluation, outputting the light source mask co-optimization result; otherwise, performing a second round of light source mask co-optimization iteration, optimizing the light source based on the problem pattern.

[0009] In the embodiments of this application, the step of performing light source mask co-optimization based on the typical lithographic pattern and outputting the light source mask co-optimization result includes: performing light source mask co-optimization based on the typical lithographic pattern and the second light source mask co-optimization evaluation function, and outputting the initial optimization result, including the light source and the initial mask optimization result; based on the initial optimization result, using the light source and combining the imaging logarithmic slope change, performing individual mask optimization, and outputting the mask optimization result; wherein, the second light source mask co-optimization evaluation function is constructed based on the edge placement error.

[0010] In embodiments of this application, the step of performing individual mask optimization based on the initial optimization result, using the light source, and combining the change in imaging logarithmic slope to output a light source mask co-optimization result includes: integrating the initial optimization result to obtain multiple rule tables of rule-based sub-resolution auxiliary graphics; performing individual mask optimization based on the multiple rule tables to obtain multiple mask optimization results; determining a final rule table that meets the process requirements based on at least two of the depth of focus, imaging logarithmic slope, change in imaging logarithmic slope, and mask error enhancement factor corresponding to each of the multiple mask optimization results; and updating the initial optimization result based on the mask optimization result corresponding to the final rule table to obtain the mask optimization result.

[0011] According to another aspect of this application, a light source mask collaborative optimization apparatus is provided, the light source mask collaborative optimization apparatus including a memory and a processor, the memory storing a computer program executed by the processor, the computer program causing the processor to execute the above-described light source mask collaborative optimization method when running.

[0012] According to another aspect of this application, a storage medium is provided, wherein a computer program executed by a processor is stored on the storage medium, and the computer program, when running, causes the processor to perform the above-described light source mask collaborative optimization method.

[0013] The light source mask co-optimization method and apparatus of this application can expand the photolithography process window and improve the dynamic change stability of the imaging logarithmic slope to predict and stabilize the photoresist undercut cross section, ensuring the linearity of the etching deviation compensation rule table during the etching process development, thereby improving the concentration and accuracy of the final post-etching inspection of key dimensions. Attached Figure Description

[0014] The above and other objects, features, and advantages of the present invention will become more apparent from the more detailed description of the embodiments of the invention in conjunction with the accompanying drawings. The drawings are provided to further illustrate the embodiments of the invention and form part of the specification. They are used together with the embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings, the same reference numerals generally represent the same parts or steps.

[0015] Figure 1 A schematic diagram illustrating the effect of ILS on the photoresist profile is shown.

[0016] Figure 2 A schematic flowchart of a light source mask collaborative optimization method according to an embodiment of this application is shown.

[0017] Figure 3 A flowchart of the light source mask collaborative optimization steps according to Embodiment 1 of this application is shown.

[0018] Figure 4 The diagram shows a comparison of the photoresist cross-sectional morphology and etching results based on the light source mask co-optimization evaluation function based solely on edge placement error and based on edge placement error and the change in the imaging logarithmic slope.

[0019] Figure 5 A flowchart of the light source mask collaborative optimization steps according to Embodiment 2 of this application is shown.

[0020] Figure 6 A schematic structural block diagram of a light source mask collaborative optimization device according to an embodiment of this application is shown. Detailed Implementation

[0021] To make the objectives, technical solutions, and advantages of the present invention more apparent, exemplary embodiments according to the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are merely a part of the embodiments of the present invention, and not all of the embodiments of the present invention. It should be understood that the present invention is not limited to the exemplary embodiments described herein. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention described herein without inventive effort should fall within the protection scope of the present invention.

[0022] Currently, the optimization process of SMO only involves consideration and verification of the photolithography process, taking into account only the two-dimensional information of the photoresist (PR) surface and ignoring the impact of PR undercut cross-section changes on etching. It only pursues high depth of focus (DOF) and image log slope (ILS), resulting in discontinuities and jumps in SRAF addition, affecting the stability of local ILS. For patterns of similar size, inconsistencies in the photoresist profile (PR profile) due to light source and SRAF issues can lead to differences in etch loading and critical dimension (CD), potentially causing dead pixels / defects after etching.

[0023] Wherein, ILS, as the slope of the intensity distribution of lithographic imaging with respect to the coordinates, characterizes the trend of lithographic imaging variation within the coordinate range, and its formula is as follows:

[0024]

[0025] Where I represents the light intensity distribution, and x represents the position coordinates of the photoresist surface.

[0026] The applicant discovered that the etch bias varies with the pattern cycle and is dynamically regulated by the stability of the ILS. However, existing SMO optimizations only pursue high DOF and ILS, neglecting the stability of the ILS, which leads to jumps in SRAF and increases the instability of etchloading and CD.

[0027] Figure 1 A schematic diagram illustrating the effect of ILS on the photoresist profile is shown. Figure 1As shown, the left and right parabolas represent different ILS sizes. Specifically, compared to the right parabola, the left parabola corresponds to a larger ILS, with a clearer photoresist outline edge and better lithographic imaging quality (better control of photoresist pattern linewidth). Conversely, the right parabola corresponds to a smaller ILS, with a rougher photoresist outline edge and poorer lithographic imaging quality (poorer control of photoresist pattern linewidth). For patterns of similar size, it is necessary to ensure the consistency of the photoresist cross-sectional morphology as much as possible, i.e., maintain the dynamic stability of the local ILS, reduce the impact of changes in the photoresist undercut cross-section on etching behavior, thereby improving the concentration and accuracy of after-etching inspection (AEI) CD. Therefore, based on the above problems and the applicant's findings, this application provides a novel light source mask co-optimization scheme.

[0028] Figure 2 A schematic flowchart of a light source mask collaborative optimization method 100 according to an embodiment of this application is shown. Figure 2 As shown, the light source mask collaborative optimization method 100 includes the following steps:

[0029] In step S110, a typical lithographic pattern in the target layout is obtained according to the design rules.

[0030] In step S120, light source mask co-optimization is performed based on typical photolithography patterns, and the light source mask co-optimization results are output. The evaluation indicators used in the light source mask co-optimization include edge placement error and the change in imaging logarithmic slope.

[0031] In the light source and mask co-optimization method 100 of this application, typical lithography patterns in the target layout are first obtained according to design rules, such as anchor patterns, difficult lithography patterns, test patterns, static random-access memory (SRAM), and key patterns of logic devices. These patterns can represent the structural features of the entire layout and are sensitive to the light source. Therefore, the light source and mask optimization results obtained by SMO calculation based on these typical patterns can be applied to all patterns in the target layout. In the process of light source and mask co-optimization (SMO), not only the edge placement error (EPE) evaluation index is considered, but also the imaging logarithmic slope change (ΔILS) evaluation index. Since the applicant found that ΔILS affects etching deviation, the ΔILS evaluation index is added to SMO to supplement the three-dimensional information of PR and solve the problem that etching defects may be caused by only considering the two-dimensional information of PR. Combining EPE and ΔILS for light source and mask optimization can balance the process window of the lithography process and the process stability of the etching process.

[0032] In one embodiment of this application, step S120, which involves performing light source mask co-optimization based on the typical lithographic pattern and outputting the light source mask co-optimization result, may include: performing light source mask co-optimization based on the typical lithographic pattern and a first light source mask co-optimization evaluation function, and outputting the light source mask co-optimization result; wherein the first light source mask co-optimization evaluation function is constructed based on the edge placement error EPE and the change in imaging logarithmic slope ΔILS.

[0033] In this embodiment, the dynamic difference of ILS (ΔILS) is added to the evaluation function to construct a new SMO evaluation function (i.e., the first light source mask collaborative optimization evaluation function). This allows for the prediction and stabilization of the PR undercut cross section by improving the dynamic change stability of ILS while expanding the lithography process window. This ensures the linearity of the rule table during the etching process development, thereby improving the concentration and accuracy of the final AEI CD.

[0034] In the embodiments of this application, in the aforementioned first light source mask collaborative optimization evaluation function, the optimization coefficient of edge placement error (EPE) is higher than the optimization coefficient of imaging logarithmic slope change (ΔILS). That is, although both EPE and ΔILS are used as evaluation indicators for light source mask collaborative optimization, EPE still carries a higher weight than ΔILS. EPE remains the primary evaluation indicator for light source mask collaborative optimization, but considering the new evaluation indicator ΔILS helps to expand the lithography process window while ensuring the linearity of the etching process rule table. For example, in the first light source mask collaborative optimization evaluation function, the optimization coefficient of EPE is N times the optimization coefficient of ΔILS, where N is greater than 1 and less than or equal to 5. For instance, the optimization coefficient of EPE is twice the optimization coefficient of ΔILS. In other examples, the relationship between the optimization coefficients of EPE and ΔILS can also be different, and can be set according to specific scenarios. In the embodiments of this application, in the first light source mask collaborative optimization evaluation function, the process conditions involved in calculating EPE and ΔILS have different weighting coefficients according to different process requirements. These weighting coefficients of different process conditions can be customized and adjusted independently to minimize the change in the imaging logarithmic slope ΔILS while optimizing the common lithography process window (without sacrificing too much DOF and ILS, etc.), further ensuring the stability of the photoresist cross-sectional morphology and etching process. In this embodiment, the process condition parameters introduced in the first light source mask collaborative optimization evaluation function have their own weighting coefficients. Users can customize their respective weighting coefficients to customize the relationship between DOF size, ILS size, and ILS stability, thereby increasing the stability of ILS while reducing the sacrifice of DOF and ILS. In one example, the above-mentioned first light source mask collaborative optimization evaluation function can be expressed as the following formula:

[0035]

[0036] In the above formula, CF represents the light source variable v src and mask graph variable v mask The evaluation function is the independent variable. pw represents the process conditions involved in calculating the evaluation indicators, including exposure dose, defocusing amount, and mask pattern size deviation. w pw and w eval represents the weight coefficients of pw under different process conditions and eval on the target graph, which are used to measure the EPE evaluation point. sidelobe p slope p MRC and p srcThis is a penalty function used to control special effects or add rule checks during the optimization process. The term 'p' represents the optimization coefficient. The specific meanings of these terms are consistent with those in the existing SMO evaluation function and will not be explained further here. In this evaluation function, as an example, the optimization coefficient of EPE is twice the optimization coefficient of ΔILS.

[0037] In the embodiments of this application, based on the aforementioned first light source mask collaborative optimization evaluation function, the light source mask collaborative optimization in step S120 may include: solving the gradient of the first light source mask collaborative optimization evaluation function. The optimal descent direction is determined based on the gradient. The light source and mask are updated based on this descent direction to reduce the value of the evaluation function until a balance is achieved between edge placement error and the change in imaging logarithmic slope. Finally, the common lithography process window of all typical lithography patterns is used as the evaluation criterion to assess whether parameters such as depth of focus, imaging logarithmic slope, change in imaging logarithmic slope, and mask error enhancement factor meet the requirements. If the requirements are met, more patterns in the target layout are verified based on the optimization results. If they consistently pass simulation and process evaluation, the light source-mask co-optimization result is output. Otherwise, a second round of SMO optimization iteration is performed, adding the problematic pattern to the input pattern and optimizing the light source to address these weaknesses. The final output is the light source-mask co-optimization result, i.e., the output light source distribution map and the corresponding mask layout.

[0038] In the above embodiments, a new SMO evaluation function was constructed, which incorporates the dynamic difference of ILS into the evaluation function. While expanding the lithography process window, it improves the dynamic change stability of ILS to predict and stabilize the PR undercut cross section, ensuring the linearity of the rule table during the etching process development, thereby improving the concentration and accuracy of the final AEI CD.

[0039] Can be combined Figure 3 The steps of the above embodiments will be described below.

[0040] Figure 4 A schematic diagram comparing the photoresist cross-sectional morphology and etching results obtained by the light source mask co-optimization method of the above embodiment with those obtained by the existing light source mask co-optimization method is shown.

[0041] from Figure 4 The comparison results show that, for typical lithographic patterns obtained from the same target layout, in the schematic diagram of the photoresist cross-sectional morphology and etching results obtained after co-optimization of the light source mask based on the SMO evaluation function with EPE as the evaluation index, the three-dimensional information of the photoresist PR is not taken into account (therefore, the PR undercut cross-section is trapezoidal rather than vertical). The final etched pattern linewidth deviates from AEI CD, as shown in the diagram. Figure 4As shown in the figure above; however, in the final etching result obtained after co-optimization of the light source mask using the SMO evaluation function with EPE and ΔILS as evaluation indicators, the three-dimensional information of the photoresist PR is taken into account (therefore, the undercut cross-section of PR is a vertical shape instead of a trapezoid), and the linewidth of the final etched pattern is closer to AEI CD, as shown in the figure above. Figure 4 As shown in the image below.

[0042] Now return to the reference Figure 2 In another embodiment of this application, step S120, which involves performing light source mask co-optimization based on a typical lithographic pattern and outputting the light source mask co-optimization result, may include: performing light source mask co-optimization based on a typical lithographic pattern and a second light source mask co-optimization evaluation function, and outputting initial optimization results, including light source and initial mask results; based on the initial optimization results, using the light source and combining the change in the imaging logarithmic slope, performing individual mask optimization and outputting mask optimization results; wherein, the second light source mask co-optimization evaluation function is constructed based on edge placement error.

[0043] In this embodiment, the dynamic difference of ILS (ΔILS) is not included in the evaluation function. Instead, the original SMO evaluation function (i.e., the second light source mask co-optimization evaluation function with EPE as the evaluation index) is used for light source mask co-optimization, outputting initial optimization results, including the light source distribution map and mask layout. Subsequently, without changing the current light source, the output results of the current SMO are further integrated to summarize a series of initial SRAF rule tables. Then, individual mask optimization (MO) calculations are performed, incorporating ΔILS as an evaluation index. Finally, with the light source unchanged, the optimal SRAF rule table and mask optimization results are obtained. By adjusting SRAF, the stability of local ILS is maintained, preserving the consistency of PR profiles with similar dimensions as much as possible. Since ΔILS is used as an evaluation index in the further mask optimization process, PR three-dimensional information is considered. This allows for the prediction and stabilization of the PR undercut cross-section by improving the dynamic change stability of ILS while expanding the lithography process window, ensuring the linearity of the rule table during etching process development, and thus improving the concentration and accuracy of the final AEI CD.

[0044] The process, based on the initial optimization results, involves using the light source and combining it with the change in the imaging logarithmic slope to perform individual mask optimization, outputting mask optimization results. This can include: integrating the initial optimization results to obtain multiple rule tables for rule-based sub-resolution auxiliary graphics; performing individual mask optimization based on the multiple rule tables using the light source to obtain multiple mask optimization results; determining the final rule table (i.e., the optimal rule table) that meets the process requirements based on at least two of the following: depth of focus, imaging logarithmic slope, change in imaging logarithmic slope, and mask error enhancement factor corresponding to each of the multiple mask optimization results; and updating the initial optimization results based on the mask optimization results corresponding to the optimal rule table to obtain the mask optimization results.

[0045] Figure 5 A flowchart of the light source mask collaborative optimization steps in Embodiment 2 of this application is shown.

[0046] The above describes in detail the light source mask collaborative optimization method 100 according to embodiments of this application. Based on the above description, the light source mask collaborative optimization method 100 according to embodiments of this application can, while expanding the lithography process window, improve the dynamic change stability of ILS to predict and stabilize the PR undercut cross section, ensuring the linearity of the etching deviation compensation rule table during the etching process development process, thereby improving the concentration and accuracy of the final AEI CD.

[0047] The following is combined Figure 6 Describes a light source mask collaborative optimization device 200 provided according to another aspect of this application. For example... Figure 6 As shown, the light source mask collaborative optimization device 200 may include a memory 210 and a processor 220. The memory 210 stores a computer program executed by the processor 220. When the computer program is executed, it causes the processor 220 to perform the aforementioned light source mask collaborative optimization method 100 according to the embodiments of this application. The light source mask collaborative optimization method 100 has been described in detail above. Those skilled in the art can understand the structure and operation of the light source mask collaborative optimization device 200 in conjunction with the foregoing description. For the sake of brevity, it will not be described again here.

[0048] Furthermore, according to embodiments of this application, a storage medium is also provided, on which program instructions are stored. When the program instructions are run by a computer or processor, they are used to execute the corresponding steps of the light source mask collaborative optimization method 100 of this application. The storage medium may, for example, include a memory card of a smartphone, a storage component of a tablet computer, a hard disk of a personal computer, a read-only memory (ROM), an erasable programmable read-only memory (EPROM), a portable compact disc read-only memory (CD-ROM), a USB memory, or any combination of the above storage media. A computer-readable storage medium may be any combination of one or more computer-readable storage media.

[0049] Furthermore, according to embodiments of this application, a computer program is also provided, which can be stored on a cloud or local storage medium. When this computer program is run by a computer or processor, it is used to perform the corresponding steps of the light source mask collaborative optimization method 100 of this application.

[0050] Based on the above description, the light source mask collaborative optimization method and apparatus according to the embodiments of this application can, while expanding the photolithography process window, improve the dynamic change stability of ILS to predict and stabilize the PR undercut cross section, ensure the linearity of the etching deviation compensation rule table during the etching process development, and thereby improve the concentration and accuracy of the final etched AEI CD.

[0051] Although exemplary embodiments have been described herein with reference to the accompanying drawings, it should be understood that the above exemplary embodiments are merely illustrative and are not intended to limit the scope of the invention. Various changes and modifications can be made therein by those skilled in the art without departing from the scope and spirit of the invention. All such changes and modifications are intended to be included within the scope of the invention as claimed in the appended claims.

[0052] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0053] In the several embodiments provided by this invention, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another device, or some features may be ignored or not executed.

[0054] Numerous specific details are set forth in the specification provided herein. However, it will be understood that embodiments of the invention may be practiced without these specific details. In some instances, well-known methods, structures, and techniques have not been shown in detail so as not to obscure the understanding of this specification.

[0055] Similarly, it should be understood that, in order to streamline the invention and aid in understanding one or more of the various aspects of the invention, features of the invention are sometimes grouped together in a single embodiment, figure, or description thereof in the description of exemplary embodiments of the invention. However, the method of the invention should not be construed as reflecting an intention that the claimed invention requires more features than are expressly recited in each claim. Rather, as reflected in the corresponding claims, its inventive point lies in solving the corresponding technical problem with fewer features than all of those in a single disclosed embodiment. Therefore, the claims following the detailed description are hereby expressly incorporated into that detailed description, wherein each claim itself is a separate embodiment of the invention.

[0056] Those skilled in the art will understand that, apart from the mutual exclusion of features, all features disclosed in this specification (including the accompanying claims, abstract, and drawings) and all processes or units of any method or apparatus so disclosed can be combined in any combination. Unless otherwise expressly stated, each feature disclosed in this specification (including the accompanying claims, abstract, and drawings) may be replaced by an alternative feature that serves the same, equivalent, or similar purpose.

[0057] Furthermore, those skilled in the art will understand that although some embodiments herein include certain features included in other embodiments but not others, combinations of features from different embodiments are meant to be within the scope of the invention and form different embodiments. For example, in the claims, any of the claimed embodiments can be used in any combination.

[0058] The various component embodiments of the present invention can be implemented in hardware, or as software modules running on one or more processors, or a combination thereof. Those skilled in the art will understand that microprocessors or digital signal processors (DSPs) can be used in practice to implement some or all of the functions of some modules in the article analysis device according to embodiments of the present invention. The present invention can also be implemented as an apparatus program (e.g., a computer program and computer program product) for performing part or all of the methods described herein. Such programs implementing the present invention can be stored on a computer-readable medium, or can be in the form of one or more signals. Such signals can be downloaded from an Internet website, provided on a carrier signal, or provided in any other form.

[0059] It should be noted that the above embodiments are illustrative of the invention and not restrictive, and that those skilled in the art can devise alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs placed between parentheses should not be construed as limiting the claims. The word "comprising" does not exclude the presence of elements or steps not listed in the claims. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The invention can be implemented by means of hardware comprising several different elements and by means of a suitably programmed computer. In the unit claims enumerating several means, several of these means may be embodied by the same item of hardware. The use of the words first, second, and third, etc., does not indicate any order. These words can be interpreted as names.

[0060] The above are merely specific embodiments or descriptions of the present invention, and the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. The scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for collaborative optimization of light source masks, characterized in that, The method includes: Obtain typical lithographic patterns from the target layout according to the design rules; Based on the typical photolithography pattern, perform collaborative optimization of the light source mask and output the collaborative optimization results of the light source mask. The evaluation metrics used in the collaborative optimization of the light source mask include edge placement error and the change in imaging logarithmic slope.

2. The method according to claim 1, characterized in that, The process of performing collaborative optimization of the light source mask based on the typical photolithography pattern, and outputting the collaborative optimization results of the light source mask, includes: Based on the typical lithography pattern and the first light source mask collaborative optimization evaluation function, the light source mask collaborative optimization is performed, and the light source mask collaborative optimization result is output. The first light source mask collaborative optimization evaluation function is constructed based on the edge placement error and the change in the imaging logarithmic slope.

3. The method according to claim 2, characterized in that, In the first light source mask collaborative optimization evaluation function, the optimization coefficient of the edge placement error is higher than the optimization coefficient of the change in the imaging logarithmic slope.

4. The method according to claim 3, characterized in that, In the first light source mask collaborative optimization evaluation function, the optimization coefficient of the edge placement error is N times the optimization coefficient of the imaging logarithmic slope change, where N is greater than 1 and less than or equal to 5.

5. The method according to claim 2, characterized in that, In the first light source mask collaborative optimization evaluation function, the weighting coefficients corresponding to the process conditions that participate in the calculation of the edge placement error and the change in the imaging logarithmic slope can be customized and adjusted independently according to different process requirements.

6. The method according to any one of claims 2-5, characterized in that, The collaborative optimization of the light source mask includes: Solve for the gradient of the collaborative optimization evaluation function of the first light source mask; The descent direction of the optimal solution is determined based on the gradient. Based on the optimal solution, the light source and mask are updated in the descent direction, and the value of the evaluation function is reduced until the edge placement error and the change in the imaging log slope are balanced in the process. Using the common lithography process window of all typical lithography patterns as the evaluation standard, the depth of focus, imaging log slope, change in imaging log slope, and mask error enhancement factor are evaluated to determine whether they meet the requirements. If they meet the requirements, the optimization results are used to verify patterns other than the typical lithography patterns in the target layout. If they can continuously pass the simulation and process evaluation, the light source mask co-optimization results are output. Otherwise, a second round of optimization iteration of light source mask co-optimization is performed to optimize the light source based on the problem pattern.

7. The method according to claim 1, characterized in that, The process of performing collaborative optimization of the light source mask based on the typical photolithography pattern, and outputting the collaborative optimization results of the light source mask, includes: Based on the typical lithography pattern and the second light source mask collaborative optimization evaluation function, the light source mask collaborative optimization is performed, and the initial optimization results are output, including the light source and the initial mask results. Based on the initial optimization results, using the light source and combining the change in the imaging log slope, a separate mask optimization is performed, and the mask optimization results are output. The second light source mask collaborative optimization evaluation function is constructed based on the edge placement error.

8. The method according to claim 7, characterized in that, Based on the initial optimization results, using the light source and combining the change in the imaging logarithmic slope, separate mask optimization is performed, and the mask optimization results are output, including: By integrating the initial optimization results, multiple rule tables for rule-based sub-resolution auxiliary graphics are obtained; Using the light source, multiple mask optimization results are obtained by performing individual mask optimization based on the multiple rule tables; Based on at least two of the following factors corresponding to the multiple mask optimization results: focal depth, imaging log slope, change in imaging log slope, and mask error enhancement factor, a final rule table that meets the process requirements is determined from the multiple rule tables. The initial optimization result is updated based on the mask optimization result corresponding to the final rule table to obtain the mask optimization result.

9. A light source mask collaborative optimization device, characterized in that, The light source mask collaborative optimization device includes a memory and a processor. The memory stores a computer program that is executed by the processor. When the computer program is executed, it causes the processor to perform the light source mask collaborative optimization method according to any one of claims 1-8.

10. A storage medium, characterized in that, The storage medium stores a computer program executed by a processor, which, when running, causes the processor to perform the light source mask collaborative optimization method according to any one of claims 1-8.

Images