Photoetching process optimization method and device, terminal and medium

Abstract

The invention provides a photoetching process optimization method and device, a terminal and a medium, and the photoetching process optimization method comprises the steps: constructing a mask pattern data set, a light source data set and a photoresist data set, and randomly generating a parent population based on the mask pattern data set, the light source data set and the photoresist data set; the parent population comprises a plurality of parent individuals; and based on a preset number of iterations, circularly executing optimization operation on the parent population. According to the method, iterative optimization is carried out by constructing the mask pattern data set, the light source data set and the photoresist data set, so that the optimization range of the photoetching process is expanded to mask pattern data, light source data and photoresist data, better photoetching process data is obtained, and a better photoetching effect is achieved.

Description

Technical Field

[0001] This application belongs to the field of photolithography technology and relates to a photolithography process optimization technology, and in particular to a photolithography process optimization method, apparatus, terminal and medium. Background Technology

[0002] Photolithography is a core process in chip manufacturing. It uses optical exposure and chemical development to transfer patterns from a photomask onto a silicon wafer coated with photoresist, facilitating subsequent processes such as etching, ion implantation, or thin-film deposition to form integrated circuit structures. To improve the resolution of photolithography and obtain integrated circuits with finer linewidths, higher precision, and more complex patterns, process optimization is usually required before photolithography production of chips.

[0003] Currently, optimization methods for photolithography processes typically include two types: OPC (Optical Proximity Correction) and SMO (Source-Mask Optimization). OPC pre-corrects the mask pattern to compensate for optical proximity effects during photolithography, resulting in a pattern on the wafer that more closely resembles the design target. SMO, on the other hand, optimizes the light source configuration and mask pattern as a whole, seeking the optimal combination to improve image quality. However, these optimization methods primarily focus on adjusting the optical components—namely, the light source configuration and mask pattern—while the chemical composition and thickness of the photoresist are usually fixed. This means that the photolithography process often cannot be optimized to its best state, impacting chip manufacturing quality. Summary of the Invention

[0004] The purpose of this application is to provide a method, apparatus, terminal and medium for optimizing photolithography processes, in order to solve the problem that the chemical composition and thickness of the photoresist are usually fixed during the optimization process of existing photolithography processes, which leads to the inability to optimize the photolithography process to the best state.

[0005] In a first aspect, this application provides a photolithography process optimization method, comprising: constructing a mask pattern data group, a light source data group, and a photoresist data group, and randomly generating a parent population based on the mask pattern data group, the light source data group, and the photoresist data group; the parent population includes multiple parent individuals; and performing optimization operations cyclically on the parent population based on a preset number of iterations, wherein, in a single optimization operation, the method includes: S100, obtaining the current optimization number and the current parent population; if the current optimization number is equal to the preset number of iterations, stopping the optimization and outputting the current best individual in the current parent population as the optimal photolithography process data group; otherwise, executing step S200; S200, obtaining the current... The fitness of each parent individual in the parent population is determined, and a preset number of individuals to be optimized are selected based on the fitness. Each offspring individual is obtained based on the individuals to be optimized, and the fitness of each offspring individual is also obtained. The method for obtaining each offspring individual includes a crossover operation. S300: The current parent population is updated based on each parent individual and each offspring individual to obtain a new current parent population, and the individual with the highest fitness among the parent individuals and offspring individuals is taken as the current optimal individual. S400: The current optimization count is incremented to obtain a new current optimization count, and steps S100 to S400 are re-executed based on the current optimal individual and the new current parent population.

[0006] In one embodiment of this application, the method of obtaining each of the offspring individuals further includes mutation operations.

[0007] In one embodiment of this application, the step of selecting a preset number of individuals to be optimized based on fitness includes: obtaining the selection probability of each parent individual based on the fitness of each parent individual; and selecting a preset number of parent individuals as each individual to be optimized based on the selection probability of each parent individual.

[0008] In one embodiment of this application, the step of selecting a preset number of individuals to be optimized based on fitness includes a selection process that repeats a preset number of times. In a single selection process, the steps include: randomly extracting a number of parent individuals and selecting the one with the highest fitness among these parent individuals as the individual to be optimized; wherein, the numerical value of the preset number of times is the same as the numerical value of the preset number.

[0009] In one embodiment of this application, the photoresist data group includes several photoresist data, each of which includes resin type, photosensitive material type, solvent type, additive type, resin content, photosensitive material content, solvent content, additive content, and photoresist thickness.

[0010] In one embodiment of this application, the method for obtaining the fitness of each offspring individual is the same as the method for obtaining the fitness of each parent individual. The method for obtaining the fitness of each parent individual includes: performing photolithographic simulation on each parent individual to obtain a corresponding simulated photolithographic pattern; and obtaining the pattern quality corresponding to each simulated photolithographic pattern based on a preset photolithographic pattern evaluation index, so as to use it as the fitness of each parent individual.

[0011] In one embodiment of this application, the photolithographic pattern evaluation metrics include pattern critical dimension accuracy, edge roughness, and pattern integrity.

[0012] Secondly, this application provides a photolithography process optimization device, including a parent population generation module and an iterative optimization module;

[0013] The parent population generation module is used to construct a mask pattern data group, a light source data group, and a photoresist data group, and randomly generate a parent population based on the mask pattern data group, the light source data group, and the photoresist data group; the parent population includes multiple parent individuals;

[0014] The iterative optimization module is used to perform optimization operations on the parent population cyclically based on a preset number of iterations. Each optimization operation includes: obtaining the current optimization count and the current parent population; if the current optimization count equals the preset number of iterations, stopping optimization and outputting the current best individual in the current parent population as the optimal lithography process data set; otherwise, simulating each parent individual in the current parent population to obtain fitness, and selecting a preset number of individuals to be optimized based on fitness; obtaining each offspring individual based on each offspring individual to be optimized, and simulating each offspring individual to obtain fitness; wherein the acquisition method for each offspring individual includes a crossover operation; updating the current parent population based on each parent individual and each offspring individual to obtain a new current parent population, and selecting the individual with the highest fitness among the parent individuals and offspring individuals as the current best individual; incrementing the current optimization count to obtain a new current optimization count, and re-executing the optimization operation based on the current best individual and the new current parent population.

[0015] Thirdly, this application provides a terminal, including: a processor and a memory, wherein the memory and the processor are communicatively connected;

[0016] The memory is used to store computer programs, and the processor is used to execute the computer programs stored in the memory to enable the terminal to perform the photolithography process optimization method as described above.

[0017] Fourthly, this application provides a computer storage medium storing a computer program, which, when executed by a processor, implements the photolithography process optimization method as described above.

[0018] As described above, this application provides a photolithography process optimization method, apparatus, terminal, and medium. By constructing a mask pattern data group, a light source data group, and a photoresist data group for iterative optimization, it avoids the poor optimization effect caused by optimizing only the mask pattern and the light source, thereby obtaining better photolithography process data, further improving the image quality of the photolithography pattern, achieving better photolithography effect, and having high industrial application value. Attached Figure Description

[0019] Figure 1 The diagram shown is a flowchart illustrating a photolithography process optimization method according to an embodiment of this application.

[0020] Figure 2 The diagram shown is a flowchart illustrating a single optimization operation process as described in an embodiment of this application.

[0021] Figure 3 The diagram shown is a flowchart illustrating another single optimization operation process described in an embodiment of this application.

[0022] Figure 4 The diagram shown is a flowchart illustrating a method for obtaining the fitness of each parent individual as described in an embodiment of this application.

[0023] Figure 5 The diagram shown is a flowchart illustrating a method for selecting individuals to be optimized, as described in an embodiment of this application.

[0024] Figure 6 The diagram shown is a structural schematic of a photolithography process optimization apparatus according to an embodiment of this application.

[0025] Figure 7 The diagram shown is a structural schematic of a terminal as described in an embodiment of this application.

[0026] Explanation of reference numerals in the attached figures

[0027] 31: Parent population generation module; 32: Iterative optimization module; 40: Terminal; 41: Processor; 42: Memory; 421: Operating system; 422: Application program; 43: User interface; 44: Network interface; 45: Bus system. Detailed Implementation

[0028] The following specific examples illustrate the implementation of this application. Those skilled in the art can easily understand other advantages and effects of this application from the content disclosed in this specification. This application can also be implemented or applied through other different specific embodiments, and various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of this application. It should be noted that, unless otherwise specified, the following embodiments and features in the embodiments can be combined with each other.

[0029] It should be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of this application. Therefore, the drawings only show the components related to this application and are not drawn according to the actual number, shape and size of the components in the actual implementation. In the actual implementation, the form, quantity and proportion of each component can be arbitrarily changed, and the layout of the components may also be more complex.

[0030] Existing photolithography optimization methods typically focus on optimizing mask pattern data and light source data, while pre-configured photoresist is often used. However, the composition and thickness of the photoresist affect the development time and effect of the lithographic pattern, thus impacting its resolution, clarity, and overall pattern quality. Therefore, optimizing only the mask pattern and light source data is insufficient to optimize the photolithography process to its optimal state, ultimately hindering further improvements in chip quality.

[0031] To address the technical problems existing in the prior art, the following embodiments of this application provide a photolithography process optimization method, apparatus, terminal, and medium. By constructing a mask pattern data group, a light source data group, and a photoresist data group for iterative optimization, the optimization scope of the photolithography process is expanded to include mask pattern data, light source data, and photoresist data, thereby obtaining better photolithography process data to further improve the image quality of the photolithography pattern and achieve better photolithography results.

[0032] The following embodiments of this application provide a photolithography process optimization method, apparatus, terminal, and medium, including but not limited to applications in the manufacture of semiconductor chips or optical devices. The following description will take the transfer of circuit patterns to silicon wafers for the manufacture of semiconductor chips as an example.

[0033] The technical solutions in the embodiments of this application will be described in detail below with reference to the accompanying drawings.

[0034] This embodiment provides a photolithography process optimization method for optimizing photolithography process settings before mass production to obtain an optimal photolithography process data set, thereby improving chip manufacturing quality. Specifically, the photolithography process data set includes, but is not limited to, settings for mask shape, size compensation, phase shift layer, light source wavelength, light source intensity, exposure dose, photoresist composition and content, and photoresist thickness.

[0035] The following will explain the specific steps and principles of the photolithography process optimization method, such as... Figure 1 As shown, it includes:

[0036] S10: Construct mask pattern data set, light source data set, and photoresist data set, and randomly generate parent population based on the mask pattern data set, light source data set, and photoresist data set.

[0037] The mask pattern data group is a collection of all mask pattern data. Each mask pattern data is used to characterize a specific setting of the mask pattern in the photolithography process. For example, each mask pattern data is used to characterize a specific setting of the shape, size compensation amount, and phase shift layer of the mask pattern in the photolithography process.

[0038] The light source data group is a collection of all light source data. Each light source data is used to characterize a specific setting of the light source in the photolithography process. For example, each light source data is used to characterize a specific setting of the light source wavelength, light source intensity, and exposure dose in the photolithography process.

[0039] The photoresist data set is a collection of all photoresist data. Each photoresist data set is used to characterize a specific setting of the photoresist in the photolithography process. For example, each photoresist data set is used to characterize a specific setting of the composition, content, and thickness of the photoresist in the photolithography process.

[0040] It should be noted that, compared with the existing technology that uses a pre-configured fixed photoresist for simulation optimization, this application constructs a mask pattern data group, a light source data group, and a photoresist data group for iterative optimization. This optimizes the mask pattern data and light source data while also considering the optimization of the photoresist data, thereby facilitating the acquisition of a better photolithography process data group and achieving better photolithography results.

[0041] It should be noted that those skilled in the art can make specific settings for each data in the mask pattern data group, light source data group, and photoresist data group based on actual optimization needs, and this application does not impose specific restrictions here.

[0042] To facilitate understanding, the following explanation will use the specific settings of each data in the photoresist data set as an example.

[0043] Since photoresist is composed of resin, photosensitive material, solvent, and additives, in some optional embodiments, each photoresist data includes resin type, photosensitive material type, solvent type, additive type, resin content, photosensitive material content, solvent content, additive content, and photoresist thickness.

[0044] Resin is used to ensure that the photoresist film adheres to the silicon wafer and does not detach, and it also affects the photoresist's corrosion resistance. Types of resins include phenolic resins, long-chain resins, short-chain resins, polymethyl methacrylate resins, and cyclized polyisoprene rubber, among others. Furthermore, different resins have different development speeds and viscosities, and the resin content affects the solid content of the photoresist and the ease with which the developer penetrates. Excessive resin content hinders developer penetration and increases residual film rate; insufficient resin content leads to complete dissolution and prevents the formation of a complete pattern.

[0045] Photosensitive materials, based on photochemical reactions, transfer mask patterns onto the surface of a silicon wafer through exposure. Types of photosensitive materials include diazonoquinone compounds, photoacid generators, photosensitizers, and crosslinking agents. Different photosensitive materials absorb different wavelengths, therefore the selection of photosensitive materials must correspond to the light source. Furthermore, the selection of some photosensitive materials must correspond to the resin; for example, diazonoquinone compound photosensitive materials require phenolic resin. Moreover, excessively high photosensitive material content in the photoresist can lead to thinner or even broken lines after development (overdevelopment), while insufficient content results in inadequate solubility and more residue (underdevelopment).

[0046] Solvents are used to dissolve and disperse solid components such as resins and photosensitive materials to form a stable and uniform liquid. Excessive solvent content will result in the photoresist being too thin and having more pinholes after heating and softening into a solid film; conversely, insufficient solvent content will lead to excessively high liquid viscosity, making spin coating difficult and resulting in rough edges after development.

[0047] Photoresist also contains 1%-10% of other additives, such as: reactive diluents, used to reduce the viscosity of the photoresist, improve leveling, and facilitate uniform coating; tackifiers, used to enhance the adhesion between the photoresist and the silicon wafer, preventing peeling during development or etching; anti-reflective coating materials, used to reduce surface reflection effects and avoid linewidth deviations; stabilizers, used to inhibit monomer polymerization and improve process stability; and surfactants, used to reduce the surface tension of the photoresist solution, thereby reducing jagged defects at the edges of lines after development.

[0048] In summary, based on the materials and contents of each component in the photoresist, different photoresist formulations are constructed and combined with different photoresist thicknesses to serve as individual photoresist data. The collection of these photoresist data is then termed a photoresist data set. Further, a parent population is randomly generated based on the mask pattern data set, the light source data set, and the photoresist data set. The parent population includes multiple parent individuals, each containing one mask pattern data set, one light source data set, and one photoresist data set, ensuring that each parent individual can be simulated.

[0049] Specifically, the data in the mask pattern data group, the light source data group, and the photoresist data group are randomly combined to form several parent individuals, and the set of these parent individuals is taken as the parent population.

[0050] S20 performs optimization operations on the parent population based on a preset number of iterations.

[0051] Specifically, simulations are performed based on each parent individual in the parent population, and the evolution of each parent individual is carried out based on the simulation results, thereby optimizing the lithography process data set to ultimately obtain a better lithography process specific setting.

[0052] In some optional implementations, the optimization process for each parent individual in the parent population is a cyclically executed optimization operation, wherein, for example... Figure 2 and Figure 3 As shown, a single optimization operation includes:

[0053] S100: Obtain the current optimization count and the current parent population. If the current optimization count is equal to the preset iteration count, stop the optimization and output the current best individual in the current parent population as the optimal lithography process data set.

[0054] It should be noted that in this embodiment, the optimization operation is performed cyclically, and the number of iterations is a preset number of iterations. When the number of optimization operations reaches the preset number of iterations, the optimization stops, and the best individual in the current optimization process is output as the optimal lithography process data set.

[0055] Specifically, obtain the current optimization count. If the current optimization count is equal to the preset iteration count, stop the optimization and output the current best individual in the current parent population as the optimal lithography process data set; otherwise, execute step S200.

[0056] S200: Obtain the fitness of each parent individual in the current parent population, and select a preset number of individuals to be optimized based on the fitness; obtain each offspring individual based on each individual to be optimized, and obtain the fitness of each offspring individual.

[0057] Here, the current parent population refers to the parent population that needs to be optimized in the current optimization process. Each parent individual refers to the individual within the current parent population.

[0058] Fitness is used to characterize the quality of simulation results for corresponding parent or offspring individuals, serving as an evaluation criterion for subsequent optimization. Specifically, higher fitness results in better pattern quality obtained by the photolithography process.

[0059] The individuals to be optimized are those selected based on the fitness of their parent individuals for optimization and evolution. The number of individuals to be optimized is a preset number. It should be noted that those skilled in the art can set the preset number specifically based on actual needs, and this embodiment does not impose specific limitations here.

[0060] Each offspring individual is an individual evolved from each individual to be optimized.

[0061] For example, the evolution of each individual to be optimized includes a crossover operation. Specifically, two individuals to be optimized are randomly selected, and some of their photolithography process settings are randomly swapped to obtain two new individuals as offspring individuals. For example, the mask pattern size compensation, exposure dose, and photoresist composition of the two individuals to be optimized are swapped to obtain two offspring individuals.

[0062] Furthermore, to avoid the optimization process getting stuck in a local optimum too early, thus affecting the final lithography effect, this embodiment also includes a mutation operation in the evolution of each individual to be optimized. Specifically, an individual to be optimized is randomly selected, and some of its lithography process settings are randomly changed, so that the modified individual becomes the offspring individual.

[0063] Furthermore, based on the evolution of each offspring individual, its fitness is obtained to reflect the degree of optimization of each offspring individual after evolution, so as to perform further optimization based on each offspring individual after evolution, in order to achieve an iterative optimization process.

[0064] It should be noted that the fitness of each offspring is obtained in the same way as that of each parent. For ease of understanding, the following explanation will use the method of obtaining the fitness of each parent as an example.

[0065] In some alternative implementations, such as Figure 4 As shown, the methods for obtaining the fitness of each parent individual include:

[0066] S211, perform photolithography simulation on each parent individual to obtain the corresponding simulated photolithography pattern.

[0067] Specifically, the photolithography process settings corresponding to each parent individual are input into the simulation software to obtain simulation results, that is, to obtain the simulated photolithography pattern corresponding to each parent individual.

[0068] It should be noted that those skilled in the art can use simulation software to perform simulations according to actual circumstances, and this application does not impose specific limitations here. For example, the simulation software can be obtained based on machine learning training or other methods to acquire and analyze the simulated photolithography patterns of each parent individual. Specifically, those skilled in the art should be familiar with the specific training methods of the simulation software, and this embodiment does not impose specific limitations here.

[0069] S212, based on the preset photolithography pattern evaluation index, obtain the pattern quality corresponding to each simulated photolithography pattern, so as to serve as the fitness of each parent individual.

[0070] The photolithography pattern evaluation metrics include several photolithography pattern evaluation items, each used to evaluate the pattern quality of the photolithography pattern. For example, the photolithography pattern evaluation metrics include pattern critical dimension accuracy, edge roughness, and pattern integrity. Specifically, pattern critical dimension accuracy reflects the uniformity of the photolithography pattern lines, edge roughness reflects the smoothness of the edges of the photolithography pattern lines, and pattern integrity reflects the degree of overlap between the photolithography pattern and the mask pattern.

[0071] The pattern quality corresponding to each simulated lithographic pattern is obtained based on the lithographic pattern evaluation index. That is, the final lithographic pattern quality is obtained by combining the evaluation items of each lithographic pattern. For example, the sum of the critical dimension accuracy, edge roughness and pattern integrity is taken as the final lithographic pattern quality, and this pattern quality is used as the fitness of the parent individual corresponding to the simulated lithographic pattern.

[0072] Furthermore, a predetermined number of individuals to be optimized are selected based on fitness, and these individuals are then optimized. Since fitness reflects the quality of the simulation results of each individual, selecting individuals to be optimized based on fitness ensures that the optimization process of the lithography process data group shows a trend of improving pattern quality.

[0073] In some alternative implementations, such as Figure 5 As shown, the selection methods for each individual to be optimized include:

[0074] S221, based on the fitness of each parent individual, obtain the selection probability of each parent individual.

[0075] Specifically, the higher the fitness of the parent individual, the greater the probability of selection.

[0076] S222, based on the selection probability of each parent individual, select a preset number of parent individuals as each individual to be optimized.

[0077] Since the parent individuals with higher fitness have a higher selection probability, they are more likely to be selected as individuals to be optimized. As a result, after evolution based on each individual to be optimized, the obtained individuals show a trend of better pattern quality. The specific settings of the photolithography process are ultimately obtained based on the optimization process of multiple iterations.

[0078] In other embodiments, the selection method for each individual to be optimized includes: randomly extracting a number of parent individuals, and selecting the one with the highest fitness among these parent individuals as the individual to be optimized, repeating this step until the number of selected individuals to be optimized is the number of optimizations, that is, repeating the aforementioned selection process a preset number of times, wherein the value of the preset number of times is the same as the value of the preset number of individuals.

[0079] Based on this, evolution can be carried out based on each selected individual to be optimized, thereby achieving the optimization of each individual.

[0080] S300 updates the current parent population based on each parent individual and each child individual to obtain a new current parent population, and selects the individual with the highest fitness among each parent individual and each child individual as the current optimal individual.

[0081] The updated current parent population is used as the current parent population in the next optimization process to achieve an iterative optimization process.

[0082] Furthermore, for the updated current parent population, the individual with the highest fitness is the individual with the best optimization effect after evolution in the current optimization process, and it is regarded as the optimal individual.

[0083] S400: Increment the current number of optimizations to obtain a new current number of optimizations, and re-execute steps S100 to S400 based on the current best individual and the new current parent population.

[0084] Based on the current best individual and the new current parent population, the optimization operation is re-executed to achieve an iterative optimization process. In this embodiment, the optimization operation is executed cyclically for a preset number of iterations. When the number of optimization operations reaches this number of iterations, the optimization stops, and the best individual in the current optimization process is output as the optimal lithography process data set.

[0085] It should be noted that, in order to stop optimization when the number of times the optimization operation is executed reaches the number of iterations, the current optimization count is incremented to obtain a new current optimization count, which is then used as the current optimization count in the next optimization process. That is, after executing the optimization process from step S100 to S400 once, the current optimization count is incremented by one to become the current optimization count in one optimization process.

[0086] Based on this, this embodiment optimizes the lithography process data set through a preset number of iterations, so that lithography based on the optimized lithography process data set can achieve better lithography results.

[0087] like Figure 6 As shown, this embodiment provides a photolithography process optimization device, including a parent population generation module 31 and an iterative optimization module 32.

[0088] Among them, the parent population generation module 31 is used to build a mask pattern data group, a light source data group, and a photoresist data group, and randomly generate a parent population based on the mask pattern data group, the light source data group, and the photoresist data group.

[0089] The iterative optimization module 32 is used to perform optimization operations on the parent population cyclically based on a preset number of iterations. For the specific steps and principles of performing optimization operations on the parent population cyclically, please refer to steps S210 to S230 above; this embodiment does not impose specific limitations here.

[0090] Based on the same technical concept, the photolithography process optimization method provided in the embodiments of the present invention can be implemented on the terminal side or the server side.

[0091] like Figure 7 The diagram shown illustrates an optional hardware structure of a terminal according to an embodiment of the present invention. The terminal 40 can be a mobile phone, computer device, tablet device, personal digital processing device, factory back-end processing device, etc. The terminal 40 includes at least one processor 41, a memory 42, at least one network interface 44, and a user interface 43. The various components in the device are coupled together via a bus system 45. It is understood that the bus system 45 is used to realize communication between these components. In addition to a data bus, the bus system 45 also includes a power bus, a control bus, and a status signal bus.

[0092] The user interface 43 may include a monitor, keyboard, mouse, trackball, clicker, button, touchpad, or touch screen.

[0093] It is understood that memory 42 can be volatile memory or non-volatile memory, or both. Non-volatile memory can be read-only memory (ROM) or programmable read-only memory (PROM), which serves as an external cache. By way of example, but not limitation, many forms of RAM are available, such as static random access memory (SRAM) and synchronous static random access memory (SSRAM). The memory characterized in the embodiments of the present invention is intended to include, but is not limited to, these and any other suitable categories of memory.

[0094] In this embodiment of the invention, the memory 42 is used to store various types of data to support the operation of the terminal. Examples of this data include: any executable program for operation on the terminal 40, such as the operating system 421 and application programs 422; the operating system 421 contains various system programs, such as the framework layer, core library layer, driver layer, etc., for implementing various basic services and handling hardware-based tasks. The application program 422 may contain various applications, such as a media player, browser, etc., for implementing various application services. The lithography process optimization method provided in this embodiment of the invention can be included in the application program 422.

[0095] The methods disclosed in the above embodiments of the present invention can be applied to processor 41, or implemented by processor 41. Processor 41 may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by the integrated logic circuit of the hardware in processor 41 or by instructions in the form of software. The processor mentioned above may be a general-purpose processor, a digital signal processor (DSP), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. Processor 41 can implement or execute the methods, steps and logic block diagrams disclosed in the embodiments of the present invention. Processor 41 may be a microprocessor or any conventional processor, etc. The steps of the accessory optimization method provided in the embodiments of the present invention can be directly reflected as being executed by a hardware decoding processor, or being executed by a combination of hardware and software modules in the decoding processor. The software module may be located in a storage medium, which is located in a memory. The processor reads the information in the memory and combines it with its hardware to complete the steps of the aforementioned method.

[0096] In an exemplary embodiment, terminal 40 may be used by one or more application-specific integrated circuits (ASICs), DSPs, programmable logic devices (PLDs), or complex programmable logic devices (CPLDs) to execute the aforementioned method.

[0097] This invention also provides a computer-readable storage medium storing a computer program that, when invoked by a processor, implements the photolithography process optimization method provided by this invention.

[0098] A computer-readable storage medium can be a tangible device capable of holding and storing instructions used by an instruction execution device. Computer-readable storage media can be, for example, (but not limited to) electrical storage devices, magnetic storage devices, optical storage devices, electromagnetic storage devices, semiconductor storage devices, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of computer-readable storage media include: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), static random access memory (SRAM), portable compact disc read-only memory (CD-ROM), digital multifunction disc (DVD), memory sticks, floppy disks, and mechanical encoding devices.

[0099] The computer-readable program represented herein can be downloaded from a computer-readable storage medium to various computing / processing devices, or downloaded via a network, such as the Internet, a local area network, a wide area network, and / or a wireless network, to an external computer or external storage device. A network adapter card or network interface in each computing / processing device receives computer-readable program instructions from the network and forwards these instructions to the computer-readable storage medium in the respective computing / processing device.

[0100] In summary, this application constructs a mask pattern data group, a light source data group, and a photoresist data group for iterative optimization. This allows for simultaneous optimization of the photoresist data while optimizing the mask pattern data and light source data, thereby avoiding the influence of a fixed photoresist scheme on the final optimized scheme and achieving better lithography results.

[0101] The descriptions of the processes or structures corresponding to the above figures each have their own emphasis. For parts of a process or structure that are not described in detail, please refer to the relevant descriptions of other processes or structures.

[0102] The above embodiments are merely illustrative of the principles and effects of this application and are not intended to limit this application. Any person skilled in the art can modify or alter the above embodiments without departing from the spirit and scope of this application. Therefore, all equivalent modifications or alterations made by those skilled in the art without departing from the spirit and technical concept disclosed in this application should still be covered by the claims of this application.

Claims

1. A method for optimizing photolithography processes, comprising: Construct a mask pattern data group, a light source data group, and a photoresist data group, and randomly generate a parent population based on the mask pattern data group, the light source data group, and the photoresist data group; the parent population includes multiple parent individuals; Based on a preset number of iterations, optimization operations are performed cyclically on the parent population, wherein each optimization operation includes: S100: Obtain the current optimization count and the current parent population. If the current optimization count is equal to the preset iteration count, stop the optimization and output the current best individual in the current parent population as the optimal lithography process data set; otherwise, proceed to step S200. S200, obtain the fitness of each parent individual in the current parent population, and select a preset number of individuals to be optimized based on the fitness; obtain each offspring individual based on each offspring individual to be optimized, and obtain the fitness of each offspring individual; wherein, the method of obtaining each offspring individual includes crossover operation; S300, the current parent population is updated based on each parent individual and each child individual to obtain a new current parent population, and the individual with the highest fitness among each parent individual and each child individual is taken as the current optimal individual; S400, increment the current optimization count to obtain a new current optimization count, and re-execute steps S100 to S400 based on the current best individual and the new current parent population.

2. The method according to claim 1, characterized in that, The methods for obtaining offspring individuals also include mutation operations.

3. The method according to claim 1, characterized in that, The selection of a preset number of individuals to be optimized based on fitness includes: Based on the fitness of each parent individual, the selection probability of each parent individual is obtained; Based on the selection probability of each parent individual, a preset number of parent individuals are selected as each individual to be optimized.

4. The method according to claim 1, characterized in that, The selection of a preset number of individuals to be optimized based on fitness includes a selection process that iterates a preset number of times. Each selection process includes: Randomly extract several parent individuals, and select the one with the highest fitness among these parent individuals as the individual to be optimized; The value of the preset number of times is the same as the value of the preset number of items.

5. The method according to claim 1, characterized in that, The photoresist data set includes several photoresist data sets, each of which includes resin type, photosensitive material type, solvent type, additive type, resin content, photosensitive material content, solvent content, additive content, and photoresist thickness.

6. The method according to claim 1, characterized in that, The method for obtaining the fitness of each offspring individual is the same as the method for obtaining the fitness of each parent individual, wherein obtaining the fitness of each parent individual includes: Photolithographic simulation was performed on each of the parent individuals to obtain the corresponding simulated photolithographic pattern; Based on the preset photolithography pattern evaluation index, the pattern quality corresponding to each simulated photolithography pattern is obtained, which is used as the fitness of each parent individual.

7. The method according to claim 6, characterized in that, The evaluation metrics for the photolithographic pattern include the accuracy of the critical dimensions of the pattern, edge roughness, and pattern integrity.

8. A photolithography process optimization device, characterized in that, Includes a parent population generation module and an iterative optimization module; The parent population generation module is used to construct a mask pattern data group, a light source data group, and a photoresist data group, and randomly generate a parent population based on the mask pattern data group, the light source data group, and the photoresist data group; the parent population includes multiple parent individuals; The iterative optimization module is used to perform optimization operations on the parent population cyclically based on a preset number of iterations, wherein a single optimization operation includes: Obtain the current optimization count and the current parent population. If the current optimization count equals the preset iteration count, stop optimization and output the current best individual in the current parent population as the optimal lithography process data set; otherwise... The fitness of each parent individual in the current parent population is obtained through simulation, and a preset number of individuals to be optimized are selected based on the fitness; each offspring individual is obtained based on each offspring individual to be optimized, and the fitness of each offspring individual is obtained through simulation; wherein, the method of obtaining each offspring individual includes crossover operation. The current parent population is updated based on each parent individual and each child individual to obtain a new current parent population, and the individual with the highest fitness among each parent individual and each child individual is taken as the current optimal individual; The current optimization count is incremented to obtain a new current optimization count, and the optimization operation is re-executed based on the current best individual and the new current parent population.

9. A terminal, characterized in that, include: A processor and a memory, wherein the memory and the processor are communicatively connected; The memory is used to store a computer program, and the processor is used to execute the computer program stored in the memory to cause the terminal to perform the photolithography process optimization method as described in any one of claims 1 to 7.

10. A computer storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the photolithography process optimization method as described in any one of claims 1 to 7.

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