Parameter optimization methods for photolithography models and related products

Abstract

This invention provides a method for optimizing the parameters of a lithography model and related products. The parameter optimization method includes: acquiring a lithography model containing multiple process parameters to be optimized; acquiring measured lithography images obtained from actual lithography results using a metrology device; based on the lithography model, using the minimum deviation between the simulated and measured lithography images as the optimization objective, iterating through multiple rounds of solutions using a global optimization algorithm to solve for the values ​​of each of the multiple process parameters to be optimized, until the generation trend of the deviation obtained from multiple iterations meets the set trend conditions, at which point the iteration stops, resulting in the target parameter set. This method achieves early stopping of iteration based on obtaining good optimization results, avoiding unnecessary waste of computational resources and time, and improving the efficiency of lithography model establishment.

Description

Technical Field

[0001] This invention relates to the field of integrated circuit technology, and in particular to a method for optimizing the parameters of a photolithography model, a computer-readable storage medium, a computer program product, and a computer device. Background Technology

[0002] As chip manufacturing technology advances, the feature size of lithographic target patterns is becoming increasingly smaller. This leads to more significant effects from optical diffraction during exposure and from the physicochemical effects occurring in the photoresist, thus substantially altering the shape of the pattern on the substrate after lithography. Therefore, optical proximity correction (OPC) plays an increasingly important role in the manufacturing process of very large-scale integrated circuits.

[0003] At advanced photolithography technology nodes, OPC (Optical Photolithography Design) often relies on simulation results based on photolithography models. A photolithography model is an empirical model derived from actual physicochemical effects and fitted to measured data from the actual process. It can simulate the process from exposure to development, and simulate the specific morphology of the photoresist after development. OPC can then be used to design and refine patterns based on the specific morphology of the photoresist simulated by the photolithography model.

[0004] However, existing methods for constructing and optimizing lithography models are often inefficient and require a lot of time and computing resources. How to efficiently obtain an accurate lithography model remains a problem that the industry urgently needs to solve. Summary of the Invention

[0005] One object of the present invention is to provide a method for optimizing the parameters of a lithography model, a computer-readable storage medium, a computer program product, and a computer device to improve the efficiency of lithography model building.

[0006] Specifically, according to one aspect of the present invention, the present invention provides a method for optimizing the parameters of a photolithography model, comprising: A lithography model containing multiple process parameters to be optimized is obtained. The lithography model is used to simulate the lithography process and obtain a simulated lithography image. Acquire measured lithography images obtained by measurement equipment from actual lithography results; Based on the lithography model, with the minimum deviation between the simulated lithography image and the measured lithography image as the optimization target, a global optimization algorithm is used to iteratively solve the values ​​of the multiple process parameters to be optimized in multiple rounds until the generation trend of the deviation obtained from multiple iterations meets the set trend condition, and then the iteration stops, resulting in a target parameter set, which includes the target values ​​of the multiple process parameters to be optimized.

[0007] Optionally, the setting of the global optimization algorithm includes setting a genetic algorithm; and the step of using the setting of the global optimization algorithm to iteratively solve for the values ​​of the plurality of process parameters to be optimized in multiple rounds includes: Multiple candidate parameter sets are initialized, and each candidate parameter set is treated as a separate individual to form an initial population. The candidate parameter set includes the candidate values ​​of each of the multiple process parameters to be optimized. The process parameters of the lithography model are determined using each individual in the current population, and candidate simulated lithography images are obtained. The fitness of each individual is calculated based on each candidate simulated lithography image and the measured lithography image, wherein the fitness reflects the deviation between the candidate simulated lithography image and the measured lithography image; Based on the fitness of each individual, selection, crossover, and mutation operations are performed on the individuals in the current population to obtain the next population. The process of repeatedly executing the process of obtaining candidate simulated lithographic images using the lithography model and calculating the fitness of each individual in the current population is repeated until the generation trend of the fitness obtained from multiple iterations meets the set trend condition, at which point the iteration stops and the target population is obtained. Select the target individual with the lowest fitness from the target population, and use the candidate parameter set corresponding to the target individual as the target parameter set.

[0008] Optionally, the step of determining whether the generation trend of the fitness obtained through multiple iterations satisfies the set trend condition includes: In a series of consecutive rounds, the minimum fitness of each individual in the current round of the population is obtained and used as the candidate fitness for that round. Based on the distribution trends of the acquired candidate fitness values, it is determined whether the generation trend of the candidate fitness values ​​satisfies the set trend condition.

[0009] Optionally, the step of setting multiple consecutive rounds of acquisition includes: Get the iteration number N of the current round, where N is an integer greater than 1; In response to N being greater than or equal to a set number of iterations, the most recent M rounds with the current round as the cutoff round are obtained as the set consecutive rounds, where M is an integer greater than 1 and less than N.

[0010] Optionally, the set trend conditions include: Among the M candidate fitness values, the J nearest candidate fitness values ​​are obtained, and the difference between any two adjacent candidate fitness values ​​among the J nearest candidate fitness values ​​is less than a preset difference value, where J is an integer greater than 2.

[0011] Optionally, the set trend conditions include: Using a sliding window, K groups of candidate fitnesss are obtained from the M candidate fitnesss. Each group of candidate fitnesss includes Q candidate fitnesss. A linear relationship between the candidate fitnesss of each group of candidate fitnesss is fitted, and the slopes of the obtained K linear relationships are all less than a preset slope. K and Q are both integers greater than 2.

[0012] Optionally, the set trend conditions further include: Among the M candidate fitness values, the most recent L candidate fitness values ​​are obtained, and the candidate fitness value of the current round is the maximum value among the most recent L candidate fitness values, where L is an integer greater than 2.

[0013] Optionally, the set trend conditions include: Among the M candidate fitness values, the J nearest candidate fitness values ​​are obtained, and the difference between any two adjacent candidate fitness values ​​among the J nearest candidate fitness values ​​is less than a preset difference, where J is an integer greater than 2; and From the M candidate fitness values, the K nearest candidate fitness values ​​are obtained, and a linear relationship is fitted. The slope of any one of the K nearest candidate fitness values ​​in the linear relationship is less than a preset slope, where K is an integer greater than 2. Among the M candidate fitness values, the most recent L candidate fitness values ​​are obtained, and the candidate fitness value of the current round is the maximum value among the most recent L candidate fitness values, where L is an integer greater than 2.

[0014] According to another aspect of the present invention, a computer-readable storage medium is also provided, on which a computer program is stored, wherein the computer program, when executed by a processor, implements the steps of the parameter optimization method for any of the above-described photolithography models.

[0015] According to another aspect of the present invention, a computer program product is also provided, comprising a computer program that, when executed by a processor, implements the steps of the parameter optimization method for any of the above-described photolithography models.

[0016] According to another aspect of the present invention, a computer device is also provided, including a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the parameter optimization method for any of the above-described photolithography models.

[0017] The parameter optimization method for the lithography model of this invention effectively avoids getting trapped in local optima and obtains an accurate global optimal solution by using a global optimization algorithm to optimize multiple parameters of the lithography model. By improving the iteration stopping condition of the global optimization algorithm, the generation trend of the deviation obtained from multiple iterations is obtained. The iteration stops when the generation trend meets the set trend condition. This achieves early stopping of iteration while ensuring that the global optimization algorithm can obtain good results, avoiding unnecessary waste of computational resources and time, and improving the efficiency of lithography model building.

[0018] The above and other objects, advantages and features of the present invention will become more apparent to those skilled in the art from the following detailed description of specific embodiments of the invention in conjunction with the accompanying drawings. Attached Figure Description

[0019] The following sections will describe some specific embodiments of the invention in a detailed manner by way of example and not limitation, with reference to the accompanying drawings. The same reference numerals in the drawings denote the same or similar parts or portions. Those skilled in the art should understand that these drawings are not necessarily drawn to scale. In the drawings: Figure 1 This is a flowchart illustrating a parameter optimization method for a photolithography model according to an embodiment of the present invention; Figure 2 This is a flowchart illustrating the iterative optimization process using a genetic algorithm in a parameter optimization method according to an embodiment of the present invention. Figure 3 This is a flowchart illustrating the use of iteration stopping conditions in a parameter optimization method according to an embodiment of the present invention; Figure 4 This is a flowchart illustrating the process of obtaining and setting data for multiple consecutive rounds in a parameter optimization method according to an embodiment of the present invention. Figure 5 This is a flowchart illustrating the parameter optimization method using iterative stopping conditions according to another embodiment of the present invention; Figure 6 This is an iterative schematic diagram illustrating the parameter optimization method according to an embodiment of the present invention, which uses a first iteration stopping condition to optimize the parameters of a photolithography model with relatively simple process conditions. Figure 7 The parameter optimization method according to an embodiment of the present invention uses a third iteration stopping condition. Figure 6 An iterative diagram illustrating parameter optimization of the lithography model in the image; Figure 8 This is an iterative schematic diagram illustrating the parameter optimization method according to an embodiment of the present invention, which uses a first iteration stopping condition to optimize the parameters of a photolithography model with relatively complex process conditions. Figure 9The parameter optimization method according to an embodiment of the present invention uses a third iteration stopping condition. Figure 8 An iterative diagram illustrating parameter optimization of the lithography model in the image; Figure 10 This is a schematic diagram of a computer program product according to an embodiment of the present invention; Figure 11 This is a schematic diagram of a computer-readable storage medium according to an embodiment of the present invention; and Figure 12 This is a schematic diagram of a computer device according to an embodiment of the present invention. Detailed Implementation

[0020] The purpose of the parameter optimization method for the lithography model in this embodiment is to improve the efficiency of lithography model establishment.

[0021] Figure 1 This is a flowchart illustrating a parameter optimization method for a photolithography model according to an embodiment of the present invention. The method generally includes: S100: Obtain a lithography model containing multiple process parameters to be optimized. The lithography model is used to simulate the lithography process and obtain a simulated lithography image. S200: Acquire the measured lithography image obtained by the measurement equipment from the actual lithography results; S300, based on a lithography model, takes the minimum deviation between simulated and measured lithography images as the optimization objective. It uses a global optimization algorithm to iteratively solve multiple process parameters to be optimized for multiple rounds until the generation trend of the deviation obtained from multiple iterations meets the set trend conditions, and then stops iterating to obtain the target parameter set, which includes the target values ​​of multiple process parameters to be optimized.

[0022] The photolithography process for wafers mainly includes substrate preparation, resist coating, pre-baking, alignment, exposure, post-baking, development, and etching. A photolithography model is used to simulate one or more of these processes. For example, a photolithography model can be an exposure simulation model, which calculates the aerial image (AI) and / or the latent image of the photoresist based on the mask layout and exposure-related process parameters. A photolithography model can also be an exposure-post-baking-development simulation model, which calculates the contour image of the photoresist after development based on the mask layout and exposure-related, post-baking-related, and development-related process parameters. A photolithography model can also be an exposure-post-baking-development-etching simulation model, which calculates the contour image of the wafer after etching based on the mask layout and exposure-related, post-baking-related, development-related, and etching-related process parameters. Photolithography models can also be simulation models of other photolithography processes or combinations of photolithography processes, which will not be elaborated here.

[0023] The measurement device can be an imaging device based on optics or charged particle beams (e.g., electron beams). For example, when the photolithography model is used to simulate optical spatial images, the measurement device can be a charge-coupled device (CCD) for acquiring measured optical spatial images of the photomask. For example, when the photolithography model is an exposure-bake-develop simulation model, the measurement device can be a scanning electron microscope (SEM) for acquiring measured contour images of the photoresist. For example, when the photolithography model is an exposure-bake-develop-etch simulation model, the measurement device can be a scanning electron microscope (SEM) for acquiring measured contour images of the wafer.

[0024] In constructing any of the aforementioned lithography models, a large number of process parameters need to be set to accurately and realistically reproduce the physical and chemical reactions during the process and obtain precise simulation results. Some of these process parameters can be measured directly or indirectly in actual production, while others cannot be measured. A crucial step in constructing a lithography model is optimizing the values ​​of the unknown parameters. Only by obtaining the accurate values ​​of each parameter in the set of process parameters to be optimized can a high-precision lithography model be obtained. To a certain extent, the main process of constructing a lithography model is the process of solving for the optimal values ​​of each process parameter to be optimized.

[0025] Since photolithography typically processes a large number of feature patterns simultaneously and usually involves multiple process steps, it is necessary to focus on the global optimum when solving for the optimized process parameters to avoid getting trapped in local optima. Therefore, global optimization algorithms are the preferred algorithms for parameter optimization of photolithography models. Global optimization algorithms refer to methods for finding the global optimum in optimization problems. These problems often have multiple local optima, thus requiring specialized algorithms to avoid getting trapped in local optima and find the true global optimum.

[0026] In this embodiment, the global optimization algorithm can be set as a genetic algorithm, simulated annealing algorithm, particle swarm optimization algorithm, etc. Among them, the genetic algorithm is a computational model established by simulating the natural selection and genetic mechanisms of Darwin's theory of biological evolution. It is a method to search for the optimal solution by simulating the natural evolutionary process. This method represents unknown parameters using chromosomes and achieves the global optimization process through techniques such as evaluation, replication, combination, and mutation. The simulated annealing algorithm simulates the principle of solid annealing, corresponding concepts such as particle states, the lowest energy state, melting process, isothermal process, cooling, and energy in the solid annealing process with the solution, optimal solution, initial temperature, Metropolis sampling process, decrease of control parameters, and objective function in combinatorial optimization problems, thus achieving the global optimization process. The particle swarm optimization algorithm is similar to the genetic algorithm, also searching for the optimal solution through iteration in a random manner. However, its rules are simpler than those of the genetic algorithm. It does not require the crossover and mutation operations of the genetic algorithm, but instead finds the global optimal solution by following the currently searched optimal value.

[0027] Taking a genetic algorithm as an example of setting the global optimization algorithm, when solving for the set of process parameters to be optimized in a lithography model, each process parameter can be treated as a chromosome, and the set of chromosomes corresponding to the process parameters can be grouped into a single individual. Multiple different individuals can then be grouped into a population. The population is then iteratively updated based on a population evolution strategy, thereby updating each process parameter to be optimized. After each population update, the values ​​of all process parameters to be optimized are input into the lithography model to obtain the corresponding simulated lithography image. By comparing the simulated lithography image with the measured lithography image, the deviation of each individual is obtained (which can be represented by fitness in a genetic algorithm). An iteration stopping condition is set; iteration stops when the deviation result obtained from the iteration meets the stopping condition, thus obtaining the target parameter set.

[0028] Parameter optimization methods based on global optimization algorithms can effectively avoid getting trapped in local optima and obtain accurate global optimal solutions. However, in practical applications, global optimization algorithms typically require a large number of iterative calculations, resulting in low optimization efficiency. This is especially true when the design layout of the lithography model contains numerous complex patterns, consuming enormous computational resources and time, significantly reducing optimization efficiency. The inventors discovered that the main reason for this low efficiency lies in the excessive number of times the lithography model is used to calculate and simulate the lithography image during the iteration process. For design layouts with numerous complex patterns, each simulation of the lithography model requires a significant amount of computational resources and time.

[0029] The inventors further discovered that the setting of the iteration stopping condition directly affects the total number of iterations, which is directly proportional to the number of simulations of the photolithography model. Specifically, the iteration stopping condition typically includes two schemes. The first is to preset a maximum number of iterations, stopping iteration when the preset maximum number of iterations is reached. This scheme relies on prior experience to set a reasonable value for the preset maximum number of iterations. However, the uncertainty of prior experience is relatively high. Therefore, in practical applications, a conservative strategy is usually used, setting a larger preset maximum number of iterations to ensure high-precision optimization results. This results in a significant waste of computational resources and time.

[0030] Another approach is to preset the deviation threshold, stopping the iteration when the deviation obtained during the iteration process meets the preset deviation requirement. This method can dynamically adjust the total number of iterations to some extent, but it still relies on prior experience to set a reasonable deviation threshold. When the deviation threshold is set too strictly, it may take many iterations to reach the preset deviation threshold, or even never reach it (for example, when the boundary conditions of the process parameters to be optimized are set unreasonably), which will also cause a lot of unnecessary waste of computational resources and time.

[0031] To address the above issues, the inventors, through further research, creatively proposed a third iteration stopping condition. Specifically, this third iteration stopping condition does not require setting a maximum number of iterations or a preset deviation; instead, it sets a trend condition based on the generation trend of the deviation obtained from multiple iterations. This trend condition can include the fluctuation characteristics of the deviation obtained from multiple iterations, the overall upward and downward trend, etc. On one hand, the magnitude of the deviation fluctuation in each round of the global optimization algorithm is related to the convergence degree of the process parameters to be optimized. When the process parameters to be optimized have converged during the iteration process, the oscillation amplitude of the obtained deviation will significantly decrease. Continuing iteration after the overall fluctuation reaches a certain level usually will not yield a better optimization result for the deviation. On the other hand, when the global optimization algorithm is close to convergence, the overall upward and downward trend of the deviation will tend to level off, until it approaches a horizontal level. Continuing iteration after the overall upward and downward trend reaches a certain level will significantly reduce the return on investment, and the deviation may even show an upward trend. By obtaining the generation trend of the deviation obtained from multiple iterations, and stopping the iteration after the generation trend meets the set trend conditions, it is possible to stop the iteration as early as possible while ensuring that the set global optimization algorithm can obtain good results, thus avoiding unnecessary waste of computing resources and time.

[0032] The parameter optimization method for the lithography model in this embodiment effectively avoids getting trapped in local optima and obtains a precise global optimal solution by using a global optimization algorithm to optimize multiple parameters of the lithography model. By improving the iteration stopping condition of the global optimization algorithm, the generation trend of the deviation obtained from multiple iterations is obtained. The iteration stops when the generation trend meets the set trend condition. This achieves early stopping of iteration while ensuring that the global optimization algorithm can obtain good results, avoiding unnecessary waste of computational resources and time, and improving the efficiency of lithography model building.

[0033] In some embodiments of the parameter optimization method for the photolithography model of the present invention, such as Figure 2 As shown, setting a global optimization algorithm includes setting a genetic algorithm; and the steps of using the global optimization algorithm to iteratively solve for the values ​​of multiple process parameters to be optimized in multiple rounds include: S311, initialize multiple candidate parameter sets, and treat each candidate parameter set as a separate individual to form an initial population. The candidate parameter set includes the candidate values ​​of each of the multiple process parameters to be optimized. S312, the process parameters of the lithography model are determined by each individual in the current population, and candidate simulated lithography images are obtained; S313, calculate the fitness of each individual based on each candidate simulated lithography image and the measured lithography image respectively. The fitness reflects the deviation between the candidate simulated lithography image and the measured lithography image. S314, determine whether the generation trend of fitness obtained from multiple iterations meets the set trend conditions. If not, execute S315; if yes, execute S316. S315, based on each fitness, perform selection, crossover and mutation operations on the individuals in the current population to obtain the next population, and return to execute S312; S316, obtain the target population, select the target individual with the lowest fitness from the target population, and use the candidate parameter set corresponding to the target individual as the target parameter set.

[0034] Genetic Algorithm (GA) is a global optimization algorithm particularly suitable for searching high-dimensional and massive spaces, making it ideal for establishing lithography models that require optimization of numerous physical and chemical parameters. In this embodiment, the genetic algorithm treats each process parameter in the set of process parameters to be optimized as a chromosome, the set of chromosomes corresponding to the set of process parameters to be optimized as a single individual, and multiple different individuals as a population. Through iterative optimization of the set of process parameters to be optimized, the algorithm searches for the globally optimal solution in the solution space.

[0035] After each population update, candidate simulated lithographic images of the set of process parameters to be optimized for each population are calculated using a lithography model. Each population consists of multiple individuals, and each individual can generate one candidate simulated lithographic image. The fitness of the population can be defined as the overall deviation between the candidate simulated lithographic images and the measured lithographic images for all individuals, such as total deviation, average deviation, or root mean square deviation.

[0036] In this embodiment, the genetic algorithm uses an improved iteration stopping condition. By comparing the generation trend of the fitness of the population obtained from multiple iterations with a set trend condition, the iteration is stopped in a timely manner when the set trend condition is met, avoiding unnecessary waste of computational resources and time, thereby improving the efficiency of lithography model building.

[0037] In some embodiments of the parameter optimization method for the photolithography model of the present invention, such as Figure 3 As shown, the steps for determining whether the generation trend of fitness obtained through multiple iterations satisfies the set trend conditions include: S321, In setting multiple consecutive rounds, obtain the smallest fitness among the individuals in the current round of the population in each round, and use it as the candidate fitness for the round. S323, Based on the distribution trend of the acquired multiple candidate fitnesss, determine whether the generation trend of candidate fitnesss meets the set trend conditions.

[0038] In this embodiment, fitness is represented as the model's loss function; a smaller fitness value indicates a smaller bias in the lithography model. By using the individual with the lowest fitness among all individuals in each population as the candidate fitness for this round of population evolution, the population can be driven to evolve to the individual with the lowest fitness, reaching the set trend conditions as quickly as possible and improving optimization efficiency.

[0039] In some embodiments of the parameter optimization method for the photolithography model of the present invention, such as Figure 4 As shown, the acquisition steps for multiple consecutive rounds include: S331, Get the iteration number N of the current round, where N is an integer greater than 1; S333, determine if N is greater than or equal to the set number of iterations; if so, execute S335; S335, retrieve the M nearest rounds up to the current round, and use them as a set of consecutive rounds, where M is an integer greater than 1 and less than N.

[0040] The candidate fitness values for each round obtained at the initial stage of iteration usually exhibit significant fluctuations. If the improved iteration stopping condition is used at this time, the distribution trends of multiple candidate fitness values generally do not meet the set trend condition, and instead, additional computing resources are required for condition judgment. Therefore, the improved iteration stopping condition is not used at the initial stage of iteration. After multiple iterations, when the fluctuations of the candidate fitness values for each round are small, the improved iteration stopping condition is then used, which can reduce the consumption of computing resources and improve the optimization efficiency. In practical use, the value of the set iteration number can be reasonably set based on prior experience, which will not be elaborated here.

[0041] That the iteration number N of the current round is greater than or equal to the set iteration number can be used as a prerequisite for the set trend condition. In this embodiment, after the iteration number of the current round reaches N, a sliding observation window with a width of M is set, and the data of the candidate fitness values for multiple consecutive rounds within the observation window are obtained. Then, it is judged whether the generation trend of the candidate fitness values meets the set trend condition. If not, one more iteration is performed, and the observation window is slid, and the steps of obtaining the data of the candidate fitness values within the observation window and judging whether their generation trend meets the set trend condition are repeated until the generation trend of the data of the candidate fitness values for multiple consecutive rounds within the observation window meets the set trend condition.

[0042] In some embodiments of the parameter optimization method for the lithography model of the present invention, the set trend condition further includes: Trend condition 1: Obtain the latest J candidate fitness values among M candidate fitness values, and the difference between any two adjacent candidate fitness values among the latest J candidate fitness values is less than a preset difference, where J is an integer greater than 2.

[0043] In this embodiment, the preset difference represents the tolerance for the fluctuation amplitude of the candidate fitness value. Exemplarily, a preset difference A can be set, and the difference fitness_diff between two candidate fitness values in two adjacent rounds can be expressed as: fitness_diff = abs (fitness (n) – fitness (n + 1)), where n represents any one of the latest J rounds in the observation window, that is, n is between N - J and N. By judging J consecutive rounds and calculating the difference fitness_diff between two candidate fitness values in each group of two adjacent rounds, when J consecutive rounds all satisfy fitness_diff < A, it is determined that trend condition 1 is met, indicating that the fluctuation amplitude of the candidate fitness value at the current stage is low and the candidate fitness value has tended to converge.

[0044] In some embodiments of the parameter optimization method for the lithography model of the present invention, the set trend condition includes: Trend condition 2: Use a sliding window to obtain K groups of candidate fitness from M candidate fitnesss. Each group of candidate fitness includes Q candidate fitnesss. Fit the linear relationship of the candidate fitness of each group of candidate fitnesss. The slope of the obtained K linear relationships is less than the preset slope. K and Q are both integers greater than 2.

[0045] In this embodiment, the preset slope represents the tolerance for the overall upward and downward trend of candidate fitness. The sliding window can use a fixed window width, which is Q candidate fitnesss. The step size of the sliding window can be set to 1, that is, sliding one candidate fitness at a time. In actual use, the sliding window can be used to obtain the candidate fitnesss of each round from NQ rounds to N rounds, as a group of candidate fitnesss. Then, after each iteration, the sliding window slides by one step size to obtain another group of candidate fitnesss. For example, a preset slope B can be set, and linear regression fitting can be performed on each of the Q discrete candidate fitnesss in the K groups of candidate fitnesss to obtain the slopes of K linear relationships. When all K slopes are less than B, it is determined that trend condition 2 is satisfied, indicating that the overall upward and downward trend of candidate fitness in the current stage tends to be gradual.

[0046] In some embodiments of the parameter optimization method for the photolithography model of the present invention, the setting of trend conditions further includes: Trend condition 3: Among the M candidate fitnesss, obtain the L nearest candidate fitnesss, and the candidate fitness of the current round is the maximum value among the L nearest candidate fitnesss, where L is an integer greater than 2.

[0047] Specifically, the candidate fitness value obtained in the current round is compared with the candidate fitness values ​​of the most recent L rounds. When the candidate fitness value of the current round is the maximum value, it is determined that trend condition 3 is met, indicating that the candidate fitness has converged in the current stage and shows an upward trend. It is unlikely that a better candidate fitness will be obtained by continuing the iteration.

[0048] In some embodiments of the parameter optimization method for the photolithography model of the present invention, such as Figure 5 As shown, the steps for determining whether the generation trend of candidate fitness meets the set trend conditions include: S341, determine whether trend condition 1 is satisfied. Trend condition 1 is to obtain the J nearest candidate fitnesss among M candidate fitnesss, and the difference between any two adjacent candidate fitnesss among the J nearest candidate fitnesss is less than a preset difference, where J is an integer greater than 2; if satisfied, execute S343. S343, determine whether trend condition 2 is satisfied. Trend condition 2 is to obtain the nearest K candidate fitnesss from M candidate fitnesss, fit a linear relationship, and the slope of any of the nearest K candidate fitnesss in the linear relationship is less than the preset slope, where K is an integer greater than 2; if satisfied, then execute S345. S345, determine whether trend condition 3 is satisfied. Trend condition 3 is to obtain the nearest L candidate fitnesss among M candidate fitnesss, and the candidate fitness of the current round is the maximum value among the nearest L candidate fitnesss, where L is an integer greater than 2. S347, if satisfied, then the generation trend of candidate fitness is determined to satisfy the set trend condition.

[0049] In this embodiment, by judging whether the generation trend of candidate fitness simultaneously satisfies trend condition 1, trend condition 2 and trend condition 3, the iteration can be stopped in advance when the fluctuation trend of candidate fitness, the overall rise and fall trend and other aspects reach a convergence trend, thereby reducing the risk of misjudgment.

[0050] Please see the appendix Figures 6 to 7 , attached Figure 6 This diagram illustrates the iterative process of parameter optimization for a relatively simple photolithography model using the first iteration stopping condition (with a preset maximum number of iterations of 100). Figure 7 This diagram illustrates the iterative process of parameter optimization for a relatively simple lithography model using the third iteration stopping condition. In the figure, the horizontal axis represents the iteration number, the vertical axis (cost) represents the loss function of the lithography model (i.e., candidate fitness), and the vertical axis (rms) represents the root mean square error between the linewidth of the simulated image of the lithography model and the actual measured data.

[0051] From the appendix Figure 7 It can be seen that, under relatively simple process conditions, the genetic algorithm shows a clear and stable decreasing trend in the RMS and cost values ​​of the lithography model during the iteration process. The third iteration stopping condition method successfully captured the changing trends of the RMS and cost of the lithography model during the iteration process, stopping the iteration when the curves reached near their minimum values. The entire optimization process only iterated for 35 rounds, far less than the previous method. Figure 6 100 rounds in the middle.

[0052] Please see the appendix Figures 8 to 9 , attached Figure 8 This diagram illustrates the iterative process of parameter optimization for a lithography model with relatively complex process conditions using the first iteration stopping condition (with a preset maximum number of iterations of 100). (Attached) Figure 9 This illustrates the iterative process of optimizing the parameters of the aforementioned lithography model with relatively complex process conditions using a third method of iterative stopping conditions.

[0053] From the appendix Figure 9 It can be seen that under relatively complex process conditions, the genetic algorithm generally shows a decreasing trend in the RMS and cost values ​​of the lithography model during the iteration process, but the oscillations are very violent in the early stages of the iteration. The third method of stopping the iteration successfully captured the changing trends of the RMS and cost of the lithography model during the iteration process, stopping the iteration when the curves reached near their minimum values. The entire optimization process only iterated for 85 rounds, which is still significantly lower than the previous method. Figure 8 100 rounds in the middle.

[0054] The flowchart provided in this embodiment is not intended to indicate that the operations of the method will be performed in any particular order, or that all operations of the method are included in every case. Furthermore, the method may include additional operations. Within the scope of the technical concept provided by the method in this embodiment, additional variations can be made to the above method.

[0055] It should be understood that in some embodiments, the components may be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods may be implemented using software or firmware stored in memory and executed by a suitable instruction execution system.

[0056] This invention also provides a computer program product 10, a computer-readable storage medium 20, and a computer device 30. Figure 10 This is a schematic diagram of a computer program product 10 according to an embodiment of the present invention. Figure 11 This is a schematic diagram of a computer-readable storage medium 20 according to an embodiment of the present invention. Figure 12 This is a schematic diagram of a computer device 30 according to an embodiment of the present invention. The computer program product 10 includes a computer program 11, which, when executed by the processor 32, implements the steps of the parameter optimization method for any of the above-described photolithography models. A computer-readable storage medium 20 stores the computer program 11 thereon, which, when executed by the processor 32, implements the steps of the parameter optimization method for any of the above-described photolithography models. The computer device 30 may include a memory 31, a processor 32, and the computer program 11 stored in the memory 31 and running on the processor 32.

[0057] The computer program 11 used to perform the operations of this invention may be assembly instructions, Instruction Set Architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, state setting data, integrated circuit configuration data, or source code or object code written in any combination of one or more programming languages ​​and procedural programming languages. The computer program 11 may execute entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the latter case, the remote computer may be connected to the user's computer via any type of network, including a Local Area Network (LAN) or Wide Area Network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider). In some embodiments, to perform aspects of this invention, electronic circuits, including, for example, programmable logic circuits, Field-Programmable Gate Arrays (FPGAs), or Programmable Logic Arrays (PLAs), may execute computer-readable program instructions to personalize the electronic circuits by utilizing state information from computer-readable program instructions.

[0058] For the purposes of this embodiment, computer program product 10 is a related product that includes computer program 11.

[0059] For the purposes of this embodiment, the computer-readable storage medium 20 is a tangible device capable of holding and storing a computer program 11. It can be any device capable of containing, storing, communicating, propagating, or transmitting the computer program 11 for use by or in conjunction with an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of the computer-readable storage medium 20 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 optical disc read-only memory (CD-ROM), digital versatile disc (DVD), memory stick, floppy disk, mechanical encoding device, and any suitable combination thereof.

[0060] Computer device 30 can be, for example, a server, desktop computer, laptop computer, tablet computer, or smartphone. In some examples, computer device 30 can be a cloud computing node. Computer device 30 can be described in the general context of computer system executable instructions (such as program modules) executed by a computer system. Typically, program modules can include routines, programs, object programs, components, logic, data structures, etc., that perform specific tasks or implement specific abstract data types. Computer device 30 can be implemented in a distributed cloud computing environment where tasks are performed by remote processing devices linked through a communication network. In a distributed cloud computing environment, program modules can reside on local or remote computing system storage media, including storage devices.

[0061] Computer device 30 may include a processor 32 adapted to execute stored instructions and a memory 31 that provides temporary storage space for the operation of said instructions during operation. The processor 32 may be a single-core processor, a multi-core processor, a computing cluster, or any other configuration. The memory 31 may include random access memory (RAM), read-only memory, flash memory, or any other suitable storage system.

[0062] Computer device 30 may also include a network adapter / interface and an input / output (I / O) interface. The I / O interface allows external devices that can be connected to the computer device to input and output data. The network adapter / interface provides communication between the computer device and a network, typically represented as a communication network.

[0063] Therefore, those skilled in the art should recognize that although numerous exemplary embodiments of the present invention have been shown and described in detail herein, many other variations or modifications conforming to the principles of the present invention can be directly determined or derived from the disclosure of the present invention without departing from the spirit and scope of the invention. Thus, the scope of the present invention should be understood and construed as covering all such other variations or modifications.

Claims

1. A method for optimizing the parameters of a photolithography model, characterized in that, include: A lithography model containing multiple process parameters to be optimized is obtained. The lithography model is used to simulate the lithography process and obtain a simulated lithography image. Acquire measured lithography images obtained by measurement equipment from actual lithography results; Based on the lithography model, with the minimum deviation between the simulated lithography image and the measured lithography image as the optimization target, a global optimization algorithm is used to iteratively solve the values ​​of the multiple process parameters to be optimized in multiple rounds until the generation trend of the deviation obtained from multiple iterations meets the set trend condition, and then the iteration stops, resulting in a target parameter set, which includes the target values ​​of the multiple process parameters to be optimized.

2. The parameter optimization method according to claim 1, characterized in that, The set global optimization algorithm includes a set genetic algorithm; and the step of using the set global optimization algorithm to iteratively solve for the values ​​of the plurality of process parameters to be optimized in multiple rounds includes: Multiple candidate parameter sets are initialized, and each candidate parameter set is treated as a separate individual to form an initial population. The candidate parameter set includes the candidate values ​​of each of the multiple process parameters to be optimized. The process parameters of the lithography model are determined using each individual in the current population, and candidate simulated lithography images are obtained. The fitness of each individual is calculated based on each candidate simulated lithography image and the measured lithography image, wherein the fitness reflects the deviation between the candidate simulated lithography image and the measured lithography image; Based on the fitness of each individual, selection, crossover, and mutation operations are performed on the individuals in the current population to obtain the next population. The process of repeatedly executing the process of obtaining candidate simulated lithographic images using the lithography model and calculating the fitness of each individual in the current population is repeated until the generation trend of the fitness obtained from multiple iterations meets the set trend condition, at which point the iteration stops and the target population is obtained. Select the target individual with the lowest fitness from the target population, and use the candidate parameter set corresponding to the target individual as the target parameter set.

3. The parameter optimization method according to claim 2, characterized in that, The step of determining whether the generation trend of the fitness obtained from multiple iterations satisfies the set trend condition includes: In a series of consecutive rounds, the minimum fitness of each individual in the current round of the population is obtained and used as the candidate fitness for that round. Based on the distribution trends of the acquired candidate fitness values, it is determined whether the generation trend of the candidate fitness values ​​satisfies the set trend condition.

4. The parameter optimization method according to claim 3, characterized in that, The steps for setting multiple consecutive rounds of acquisition include: Get the iteration number N of the current round, where N is an integer greater than 1; In response to N being greater than or equal to a set number of iterations, the most recent M rounds with the current round as the cutoff round are obtained as the set consecutive rounds, where M is an integer greater than 1 and less than N.

5. The parameter optimization method according to claim 4, characterized in that, The set trend conditions include: Among the M candidate fitness values, the J nearest candidate fitness values ​​are obtained, and the difference between any two adjacent candidate fitness values ​​among the J nearest candidate fitness values ​​is less than a preset difference value, where J is an integer greater than 2.

6. The parameter optimization method according to claim 4, characterized in that, The set trend conditions include: Using a sliding window, K groups of candidate fitnesss are obtained from the M candidate fitnesss. Each group of candidate fitnesss includes Q candidate fitnesss. A linear relationship between the candidate fitnesss of each group of candidate fitnesss is fitted, and the slopes of the obtained K linear relationships are all less than a preset slope. K and Q are both integers greater than 2.

7. The parameter optimization method according to any one of claims 5 or 6, characterized in that, The set trend conditions also include: Among the M candidate fitness values, the most recent L candidate fitness values ​​are obtained, and the candidate fitness value of the current round is the maximum value among the most recent L candidate fitness values, where L is an integer greater than 2.

8. A computer-readable storage medium, characterized in that, It stores a computer program that, when executed by a processor, implements the steps of the parameter optimization method for the lithography model as described in any one of claims 1 to 7.

9. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the parameter optimization method for the lithography model as described in any one of claims 1 to 7.

10. A computer device, characterized in that, The method includes a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the parameter optimization method for the lithography model according to any one of claims 1 to 7.

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