Modeling method of photoresist model and related product

Abstract

The invention relates to a modeling method of a photoresist model and a related product. The modeling method comprises the following steps: acquiring a to-be-optimized process parameter set of a developing pattern influencing photoresist in a photoresist model; dividing the set into a plurality of subsets based on the working procedure to which each parameter belongs; taking a minimum deviation index between a simulation result obtained by the photoresist model and an actual measurement result as an optimization target, taking each subset as an iteration unit, and optimizing each parameter of the set; wherein in the iterative optimization process of the parameters of the subset in the downstream process, the parameters of the subset in the upstream process are not changed. According to the scheme, under the condition that the model precision is not greatly reduced, the calculation complexity of an optimization algorithm is reduced, the calculation efficiency is improved, and calculation resources are saved.

Description

Technical Field

[0001] The present invention relates to the field of semiconductor technology, and in particular to a method for modeling a photoresist model, a computer-readable storage medium, a computer program product, and a computer device. Background Art

[0002] With the rapid development of semiconductor technology, a rigorous photoresist model based on lithography simulation technology has gradually become one of the key tools for optimizing the lithography process. The rigorous photoresist model uses precise physical and chemical models to describe the processes of exposure, post-baking, development, and measurement in lithography, and can effectively simulate and predict the graphic features, process windows, etc. of the developed pattern, thereby providing a basis for the design and debugging of lithography process parameters. However, the rigorous photoresist model also has the following key problems: accurate modeling requires a large number of photoresist process parameters, but most of the process parameters are difficult to obtain directly due to experimental conditions.

[0003] In related technologies, usually, information such as graphic features is used as the verification object, and an optimization algorithm is used to synchronously optimize all unknown process parameters in the photoresist model, so that the deviation between the graphic features obtained by the photoresist model and the measured graphic features meets the relevant requirements. Since the number of unknown process parameters is large and exists in multiple process stages, the process parameters in the upstream process will affect the adjustment of the process parameters in the downstream process, resulting in a large computational complexity of the optimization algorithm and a low optimization efficiency. To improve the optimization efficiency of the optimization algorithm, it is often necessary to significantly reduce the accuracy of the photoresist model, which in turn leads to poor accuracy of the photoresist model and is difficult to effectively guide actual production. Summary of the Invention

[0004] In view of the above problems, the present invention is proposed to provide a method for modeling a photoresist model, a computer-readable storage medium, a computer program product, and a computer device that overcome the above problems.

[0005] An object of the present invention is to provide a method for modeling a photoresist model, which can improve the modeling efficiency of the photoresist model without significantly reducing the accuracy of the photoresist model.

[0006] Specifically, according to an aspect of the present invention, the present invention provides a method for modeling a photoresist model, including:

[0007] Obtaining a set of process parameters to be optimized that affect the developed pattern of the photoresist in the photoresist model;

[0008] Dividing the set into multiple subsets based on the processes to which the parameters belong;

[0009] Taking the minimum deviation index between the simulation result obtained from the photoresist model and the measured result as the optimization objective, and using each of the subsets as an iteration unit, optimize each parameter of the set; wherein, during the iterative optimization process of each parameter of the subset in the downstream process, the parameters of the subset in the upstream process remain unchanged.

[0010] Optionally, the optimization of each parameter of the set with the minimum deviation index between the simulation result obtained from the photoresist model and the measured result as the optimization objective and each of the subsets as an iteration unit includes:

[0011] Obtain the preset threshold range of each parameter in the set;

[0012] Input the initial values of each parameter in the set into the photoresist model to obtain an initial simulation result;

[0013] Taking the deviation index between the initial simulation result and the measured result as the initial optimization objective, perform iterative simulation based on a gradient-free optimization algorithm with each of the subsets as an iteration unit, and determine each target parameter within the preset threshold range of each parameter in the set to minimize the deviation index.

[0014] Optionally, the gradient-free optimization algorithm includes a first preset algorithm and a second preset algorithm, and the second preset algorithm is nested within the first preset algorithm;

[0015] The second preset optimization algorithm is configured to, with the parameters of each subset in the upstream process remaining unchanged, take the minimum deviation index as the optimization objective, determine each candidate parameter within the preset threshold range of each parameter of the subset in the downstream process, and use the deviation index corresponding to each candidate parameter as the candidate deviation index;

[0016] The first preset optimization algorithm is configured to take the minimum candidate deviation index obtained from the second preset optimization algorithm as the optimization objective, determine each target parameter within the preset threshold range of each parameter of the subset in the upstream process, and use each candidate parameter corresponding to the minimum candidate deviation index as the target parameter.

[0017] Optionally, at least one of the first preset algorithm and the second preset algorithm is a global gradient-free optimization algorithm.

[0018] Optionally, the processes to which each parameter belongs include any two or more of the following processes:

[0019] Exposure process, post-baking process, development process, or measurement process for measuring the developed pattern to obtain pattern features.

[0020] Optionally, the parameters of the set include any one or more of the following parameters:

[0021] The exposure parameters of the photoresist and the concentration of the quencher in the exposure process;

[0022] The diffusion coefficient of the photoacid, the diffusion coefficient of the quencher, the reaction constant of the deprotection reaction, the reaction order of the deprotection reaction, the non-linear diffusion term, and the surface correction term in the post-baking process;

[0023] The development rate, development threshold concentration, and development sensitivity of the photoresist in the development process;

[0024] The line width measurement deviation term and the sidewall angle measurement correction term in the measurement process.

[0025] Optionally, the simulation results include multiple graphic features obtained by performing simulation measurement on the simulated development pattern obtained from the photoresist model. The measured results include multiple graphic features obtained by measuring the actual development pattern.

[0026] The deviation index is the root mean square deviation or the mean absolute deviation between each graphic feature in the simulation results and the corresponding graphic feature in the measured results.

[0027] Wherein, the graphic features include the critical dimension of the pattern and / or the groove shape parameter.

[0028] According to another aspect of the present invention, there is also provided a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the steps of the above-mentioned method for modeling a photoresist model are implemented.

[0029] According to still another aspect of the present invention, there is also provided a computer program product, including a computer program, and when the computer program is executed by a processor, the steps of the above-mentioned method for modeling a photoresist model are implemented.

[0030] According to yet another aspect of the present invention, there is also provided a computer device, including a memory, a processor, and a computer program stored on the memory, and the processor executes the computer program to implement the steps of the above-mentioned method for modeling a photoresist model.

[0031] The method for modeling a photoresist model, computer-readable storage medium, computer program product, and computer device of the present invention divide each parameter into multiple subsets based on the process to which each process parameter to be optimized belongs, and perform automatic iterative optimization with the subsets as the iteration unit, greatly reducing the number of times of calling the simulation module for simulation and iterative optimization. It realizes reducing the computational complexity of the optimization algorithm, improving the computational efficiency and saving computational resources without significantly reducing the model accuracy, and achieves the purpose of using the photoresist model obtained by modeling as an effective tool to guide actual production.

[0032] Those skilled in the art will better understand the above and other objects, advantages and features of the present invention from the following detailed description of the specific embodiments of the present invention in conjunction with the accompanying drawings. Description of the Drawings

[0033] Some specific embodiments of the present invention will be described in detail hereinafter with reference to the accompanying drawings in an exemplary but not restrictive manner. The same reference numerals in the drawings denote the same or similar components or parts. Those skilled in the art should understand that these drawings are not necessarily drawn to scale. In the drawings:

[0034] Figure 1 is a flowchart of a modeling method according to an embodiment of the present invention;

[0035] Figure 2 is a flowchart of optimizing each parameter in a set for the modeling method according to an embodiment of the present invention;

[0036] Figure 3 is a flowchart of a second preset algorithm for the modeling method according to an embodiment of the present invention;

[0037] Figure 4 is a flowchart of a first preset algorithm for the modeling method according to an embodiment of the present invention;

[0038] Figure 5 is a schematic diagram of a computer program product according to an embodiment of the present invention;

[0039] Figure 6 is a schematic diagram of a computer-readable storage medium according to an embodiment of the present invention; and

[0040] Figure 7 is a schematic diagram of a computer device according to an embodiment of the present invention. Detailed Embodiments

[0041] With the continuous development of semiconductor technology, lithography, as a core process in semiconductor manufacturing, faces increasing challenges in improving resolution and reducing feature sizes. Chemically amplified photoresists are widely used in current deep sub-micron processes, which utilize photolithographic exposure and subsequent chemical amplification reactions to achieve photoresist development. However, in the manufacturing process of deep sub-micron processes, due to the increasing resolution requirements, the traditional chemically amplified photoresist lithography process has been greatly restricted. These restrictions mainly come from the difficulty in designing or debugging high-precision process parameters for each process stage in the manufacturing process.

[0042] The photoresist model based on lithography simulation technology can describe the lithography processes such as exposure, post-baking, development, and measurement in lithography through accurate physical and chemical models, thereby providing a basis for the parameter design and debugging of the lithography process. However, when constructing the relevant physical and chemical models of the photoresist model, there are still a large number of unknown process parameters whose exact values are difficult to directly obtain during the actual manufacturing or experimental manufacturing process. Due to the large number of unknown process parameters and their existence in multiple process stages, the process parameters in the upstream process will affect the adjustment of the process parameters in the downstream process, resulting in a large computational complexity of the optimization algorithm and a low optimization efficiency. Existing technical solutions often compromise between model accuracy and computational complexity, resulting in the final result being unable to be effectively applied to guide actual production.

[0043] The purpose of the modeling method of the photoresist model in this embodiment is to improve the modeling efficiency of the photoresist model without significantly reducing the accuracy of the photoresist model.

[0044] Figure 1 It is a schematic flowchart of the modeling method according to an embodiment of the present invention. Generally, the method may include:

[0045] S100, obtaining a set of process parameters to be optimized that affect the developed pattern of the photoresist in the photoresist model;

[0046] S200, dividing the set into multiple subsets based on the processes to which the parameters belong;

[0047] S300, taking the minimum deviation index between the simulation result obtained from the photoresist model and the measured result as the optimization target, and taking each subset as an iteration unit to optimize each parameter in the set; wherein, during the iterative optimization process of each parameter in the subset in the downstream process, the parameters in the subset in the upstream process remain unchanged.

[0048] During actual manufacturing or experimental manufacturing, a photoresist (such as a chemically amplified photoresist) generally needs to go through manufacturing processes such as exposure, post-baking, and development to obtain the developed pattern of the photoresist. The developed pattern usually presents as a three-dimensional contour, which includes multiple trenches. The line width (critical dimension, CD) of the trenches, three-dimensional shape, etc. will directly affect the subsequent etching of the wafer, and thus affect the quality and performance of the chip. After obtaining the developed pattern, it is usually necessary to measure the developed pattern to obtain the pattern feature data such as the critical dimension of the pattern and the three-dimensional shape of the trenches in the developed pattern. These measured data can be used to compare with the simulation result of the photoresist model, and the accuracy of the photoresist model can be judged through the deviation index.

[0049] A complete photoresist model generally includes an exposure module, a post-baking module, a development module, and a measurement module, etc. Among them, the exposure module, the post-baking module, and the development module are used to simulate the physical, chemical and other processes of the exposure, post-baking, and development processes of the photoresist respectively. The measurement module is used to simulate the measurement of the simulated development pattern obtained by the photoresist model to obtain the graphic feature data of the simulated development pattern.

[0050] It can be understood that the sizes of the graphic features of the development patterns of photoresists in deep sub-micron and nano-scale processes are relatively small (usually from a few nanometers to hundreds of nanometers), and generally need to be measured by measurement tools such as a scanning electron microscope (for example, Critical Dimension Scanning Electron Microscopy, abbreviated as CD-SEM). Limited by the measurement accuracy, the measurement results of these measurement tools are not always accurate. The main influencing factors of the measurement error include the three-dimensional profile of the development pattern, the relevant parameters in the measurement stage (which can be called measurement process parameters), such as the height of the line width plane in the trench, the edge detection error, the electron beam-sample interaction effect, etc. That is to say, after the photoresist model obtains the simulated development pattern, the graphic feature data of each pattern cannot be directly read, but the final simulation result needs to be obtained through the simulation of the measurement process by the measurement model. Finally, by comparing the measured data with the final simulation result, the accuracy of the photoresist model can be judged.

[0051] In this embodiment, the photoresist model can be a complete simulation model to simulate the complete process of the photoresist, such as including an exposure module, a post-baking module, a development module, and a measurement module, or including an exposure module, a post-baking and development module, and a measurement module. The photoresist model can be a simplified simulation model that only simulates some processes of the photoresist, such as an exposure module, an exposure prediction module (directly predicting the simulated development pattern through the simulation result of the exposure module) and a measurement module, or including an exposure module, a post-baking module, a post-baking prediction module (directly predicting the simulated development pattern through the simulation result of the post-baking module) and a measurement module.

[0052] Each module in the photoresist model may include one or more process parameters, among which some process parameters can obtain exact values during actual manufacturing or trial manufacturing and can be called known process parameters. Most process parameters are difficult to obtain exact values and can be called unknown process parameters or process parameters to be optimized. Exemplarily, the known process parameters may include the wavelength of exposure, the numerical aperture (NA) of the optical system, the exposure dose, polarization, depth of focus, the absorption coefficients of the photoresist (Dill A parameter, Dill B parameter), the post-exposure bake time, the post-exposure bake temperature, the development time, etc. Exemplarily, the process parameters to be optimized may include the exposure parameters of the photoresist (Dill C parameter), the concentration of the quencher, the diffusion coefficient of the photoacid, the diffusion coefficient of the quencher, the reaction constant of the deprotection reaction, the reaction order of the deprotection reaction, the non-linear diffusion term, the surface correction term, the development rate of the photoresist, the development threshold concentration, the development sensitivity, the line width measurement deviation term, the sidewall angle measurement correction term, etc. In addition, when the photoresist model is a simplified simulation model, there are usually other unknown process parameters. For example, the light intensity threshold parameter of the optical aerial image required for directly predicting the simulated developed pattern in the exposure prediction module, or the concentration threshold parameter of the photoresist post-exposure bake substance required for directly predicting the simulated developed pattern in the post-exposure bake prediction module, etc.

[0053] Each process parameter in the photoresist model may affect the simulation result. Input the known process parameters into the photoresist model. By adjusting the values of the process parameters to be optimized, when the deviation index between the simulated developed pattern and the measured developed pattern of the photoresist model meets the relevant requirements, it can be considered that the values of the process parameters to be optimized can represent the actual parameter values in actual manufacturing or trial manufacturing. The smaller the deviation index, the higher the accuracy of the photoresist model.

[0054] In this embodiment, before automatically iteratively optimizing each process parameter to be optimized through an optimization algorithm, the parameters are first divided into multiple subsets according to the processes to which the process parameters to be optimized belong. For example, the process parameters to be optimized can be divided into an exposure subset, a post-exposure bake subset, a development subset, a measurement subset, etc. For another example, the process parameters to be optimized can be divided into an exposure subset, a post-exposure bake and development subset, a measurement subset, etc. When using the optimization algorithm for optimization, iterative simulation is performed with each subset as the iteration unit. Among them, during the iterative optimization process of the parameters in the subset in the downstream process, the parameters in the subset in the upstream process remain unchanged. Each subset corresponds to a process module in the photoresist model. For example, the exposure subset corresponds to the exposure module, the post-exposure bake subset corresponds to the post-exposure bake module, the development subset corresponds to the development module, the measurement subset corresponds to the measurement module, the post-exposure bake and development subset corresponds to the post-exposure bake and development module, the exposure prediction subset corresponds to the exposure prediction module, etc., and the post-exposure bake prediction subset corresponds to the post-exposure bake prediction module, etc.

[0055] In this embodiment, during iterative optimization, the optimization algorithm iterates in stages with the process module as the unit. For example, the post-bake development subset and all previous parameters can be kept unchanged (recorded in the cache) first, the measurement module is called, the simulation and iterative optimization are performed on each process parameter to be optimized in the measurement subset, and the iterative optimization results are recorded in the cache. After the iteration is completed, the measurement module is exited, and the post-bake development module is called to adjust each process parameter to be optimized in the post-bake development subset according to the iterative optimization results of the measurement module. Each time a module is called, the simulation and optimization are performed on multiple parameters in the corresponding subset simultaneously. Compared with directly performing simulation and optimization on each process parameter to be optimized one by one, this solution can significantly reduce the number of times of calling the simulation module for simulation and iterative optimization, thereby reducing the computational complexity of the optimization algorithm, improving the computational efficiency, saving computational resources, and achieving the purpose of using the photoresist model obtained by modeling as an effective tool to guide actual production without significantly reducing the model accuracy.

[0056] In some embodiments of the modeling method of the present invention, as Figure 2 shown, taking the minimum deviation index between the simulated development pattern and the measured development pattern obtained from the photoresist model as the optimization target, and taking each subset as the iterative unit to optimize each parameter of the set, including:

[0057] S311, obtaining the preset threshold range of each parameter in the set;

[0058] S313, inputting the initial values of each parameter in the set into the photoresist model to obtain the initial simulation result;

[0059] S315, taking the deviation index between the initial simulation result and the measured result as the initial optimization target, and performing iterative simulation based on the gradient-free optimization algorithm with each subset as the iterative unit to determine each target parameter within the preset threshold range of each parameter in the set, so as to minimize the deviation index.

[0060] In this embodiment, the optimization algorithm is a derivative-free optimization algorithm (DFO for short). The derivative-free optimization algorithm is a type of algorithm that can perform optimization without gradient information. It is mainly used for optimization problems where the objective function is non-differentiable, the gradient is difficult to calculate, or the calculation cost is high. When applied to optimize unknown parameters in models such as optimization engineering and simulation, it has high calculation efficiency and good accuracy. The derivative-free optimization algorithm can include local derivative-free optimization algorithms and global derivative-free optimization algorithms. The local derivative-free optimization algorithm can include the Nelder-Mead algorithm (Downhill Simplex Method), the Subplex algorithm (Subplex Algorithm), etc. The global derivative-free optimization algorithm can include the genetic algorithm (GA for short), the covariance matrix adaptation evolution strategy algorithm (CMAES for short), etc.

[0061] In this embodiment, the preset threshold ranges of the parameters are used to constrain the search boundaries of the derivative-free optimization algorithm, thereby further improving the calculation efficiency and accuracy of the optimization algorithm. The preset threshold range can be a prior numerical range preset in the algorithm. The preset threshold range can also be set according to actual parameters. For example, if the photoresist manufacturer provides the range of the concentration of the quencher, then this concentration range can be used as the preset threshold range of the concentration of the quencher.

[0062] In some embodiments of the modeling method of the present invention, the derivative-free optimization algorithm includes a first preset algorithm and a second preset algorithm, and the second preset algorithm is nested within the first preset algorithm.

[0063] The second preset optimization algorithm is configured to, under the condition that the parameters of each subset in the upstream process remain unchanged, take the minimum deviation index as the optimization target, determine each candidate parameter within the preset threshold range of the parameters of the subset in the downstream process, and use the deviation index corresponding to each candidate parameter as the candidate deviation index.

[0064] The first preset optimization algorithm is configured to take the minimum candidate deviation index obtained by the second preset optimization algorithm as the optimization target, determine each target parameter within the preset threshold range of the parameters of the subset in the upstream process, and use each candidate parameter corresponding to the minimum candidate deviation index as the target parameter.

[0065] The following takes the photoresist model including the post-baking and development module and the measurement module, and the parameters to be optimized are the parameters in the post-baking and development subset and the measurement subset as an example to illustrate the solution of this embodiment.

[0066] Please refer to Figures 3-4, the steps of the second preset algorithm may include:

[0067] S331, obtain the initial development result output by the post-baking development module;

[0068] S332, search and update the initial values of the parameters in the measurement subset according to the historical deviation index;

[0069] S333, input the initial development result and the parameters in the measurement subset into the measurement module to obtain the initial simulation result;

[0070] S334, determine whether the deviation index between the initial simulation result and the measured result meets the first preset condition; if so, execute S335, if not, then return to execute S332;

[0071] S335, the second preset algorithm ends the iteration and outputs the candidate deviation index.

[0072] The steps of the first preset algorithm may include:

[0073] S351, obtain the exposure result output by the exposure module;

[0074] S352, search and update the initial values of the parameters in the post-baking development subset according to the historical candidate deviation index;

[0075] S353, input the exposure result and the parameters in the post-baking development subset into the post-baking development module to obtain the initial development result;

[0076] S354, execute the second preset algorithm to obtain the candidate deviation index;

[0077] S355, determine whether the candidate deviation index meets the second preset condition; if so, execute S356, if not, then return to execute S352;

[0078] S356, the first preset algorithm ends the iteration and outputs the target deviation index.

[0079] Specifically, during optimization, the first preset algorithm may first determine the initial values of the parameters in the post-baking development subset, and then call the post-baking development module to obtain the initial development result. At this time, the first preset algorithm can be paused, and the initial development result can be recorded in the cache, and then the second preset algorithm is started.

[0080] The second preset algorithm first determines the initial values of the parameters in the measurement subset, then calls the measurement module, inputs the initial development result in the buffer, and obtains the initial simulation result. Then, it compares the deviation index between the initial simulation result and the measured result, and based on the deviation index, searches and updates the initial values of the parameters in the measurement subset, and updates the initial simulation result of the measurement module. The second preset algorithm is iteratively optimized multiple times until the deviation index is minimized. The minimum deviation indication is used as the candidate deviation index, and the parameters in the corresponding measurement subset are used as the candidate parameters.

[0081] Next, the second preset algorithm can be paused, and the first preset algorithm can be returned. Based on the candidate deviation index, the initial values of the parameters in the post-baking development subset are searched and updated, and the initial development result of the post-baking development module is updated.

[0082] Next, the first preset algorithm can be paused again, and the updated initial development result is recorded in the buffer. Then, the second preset algorithm is started again, and through multiple iterative optimizations, the candidate deviation index and the candidate parameters are updated. Through multiple nested iterative optimizations, the first preset algorithm can determine the smallest one among multiple candidate deviation indexes as the target deviation indication, and use the parameters in the corresponding post-baking development subset and measurement subset as the target parameters to end the optimization training.

[0083] On the one hand, whether it is the first preset algorithm or the second preset algorithm, when searching and updating the parameters of the corresponding subset, they are both guided by the historical deviation index and search and update in the general direction of approaching the target parameters, thereby improving the optimization efficiency. On the other hand, each time the first preset algorithm searches and updates the parameters and updates the initial simulation result, they are all recorded in the buffer, which can be called by the second preset algorithm and can also be called in the subsequent iterations of the first preset algorithm, thereby reducing unnecessary simulation times and saving computing resources.

[0084] In particular, the computational amount of the simulation of the post-baking development module is much larger than that of the measurement module, and the number of parameters in the post-baking development subset is also much more than that in the measurement subset. Since the second preset algorithm is nested in the first preset algorithm, the number of iterations of the first preset algorithm is much less than that of the first preset algorithm. That is to say, the number of simulation times of the post-baking development module is much less than that of the measurement module. In this way, the optimization efficiency can be further improved, and computing resources are saved.

[0085] In some embodiments of the modeling method of the present invention, the gradient-free optimization algorithm includes a first preset algorithm, a second preset algorithm, and a third preset algorithm. The second preset algorithm is nested in the first preset algorithm, and the third preset algorithm is nested in the second preset algorithm.

[0086] Exemplarily, the photoresist model may include a post-baking module, a post-baking prediction module, and a measurement module, and the parameters to be optimized are the parameters in the post-baking subset, the post-baking prediction subset, and the measurement subset.

[0087] During optimization, the first preset algorithm may first determine the initial values of the parameters in the post-baking subset, and then call the post-baking module to obtain the initial post-baking result. At this time, the first preset algorithm may be paused, and the initial post-baking result may be recorded in the cache. Then, the second preset algorithm is started. First, the initial values of the parameters in the post-baking prediction subset are determined, and then the post-baking prediction module is called, and the initial post-baking result in the cache is input to obtain the initial development result.

[0088] Next, the second preset algorithm may be paused, and the initial development result may be recorded in the cache. Then, the third preset algorithm is started. First, the initial values of the parameters in the measurement subset are determined, and then the measurement module is called, and the initial development result in the cache is input to obtain the initial simulation result. Then, the deviation index between the initial simulation result and the measured result is compared. According to the deviation index, the initial values of the parameters in the measurement subset are searched and updated, and the initial simulation result of the measurement module is updated. The third preset algorithm is iteratively optimized multiple times until the deviation index is minimized. The minimum deviation indication is used as the first candidate deviation index, and the parameters in the corresponding measurement subset are used as the first candidate parameters.

[0089] Next, the third preset algorithm may be paused, and the second preset algorithm is returned. According to the first candidate deviation index, the initial values of the parameters in the post-baking prediction subset are searched and updated, and the initial development result of the post-baking prediction module is updated. At this time, the second preset algorithm may be paused again, and the updated initial development result may be recorded in the cache. Then, the third preset algorithm is started again. Through multiple iterative optimizations, the first candidate deviation index and the first candidate parameters are updated. Through multiple nested iterative optimizations of the third preset algorithm and the second preset algorithm, the second preset algorithm may determine the smallest one among multiple first candidate deviation indexes as the second candidate deviation indication, and the parameters in the corresponding post-baking prediction subset and measurement subset are used as the second candidate parameters.

[0090] Next, the second preset algorithm can be paused, and the first preset algorithm can be returned. According to the second candidate deviation index, the initial values of the parameters in the post-baking subset are searched and updated, and the initial post-baking result of the post-baking module is updated. At this time, the first preset algorithm can be paused again, and the updated initial development result is recorded in the cache. Then the second preset algorithm and the third preset algorithm are started again. Through multiple iterative optimizations, the second candidate deviation index and the second candidate parameters are updated. Through the multiple nested iterative optimizations of the third preset algorithm, the second preset algorithm, and the first preset algorithm, the first preset algorithm can determine the smallest one among multiple second candidate deviation indexes as the minimum deviation indication, and use the parameters in the corresponding post-baking subset, post-baking prediction subset, and measurement subset as the target parameters to end the optimization training.

[0091] In some embodiments of the modeling method of the present invention, at least one of the first preset algorithm and the second preset algorithm is a global gradient-free optimization algorithm.

[0092] The local gradient-free optimization algorithm has a low computational complexity and few computational times, but the optimization result is poor and it is easy to fall into a local optimal solution. The global gradient-free optimization algorithm is not easy to fall into a local optimal solution and has a good optimization result, but has a high computational complexity and many computational times. In this embodiment, when the set is divided into multiple subsets based on the processes to which the parameters belong, and each subset is an iteration unit, the computational complexity has been greatly reduced. Therefore, the parameters can be optimized by the global gradient-free optimization algorithm without significantly reducing the model accuracy, thereby improving the accuracy of the model.

[0093] In actual use, it can be determined whether to adopt the global gradient-free optimization algorithm according to the module characteristics corresponding to the first preset algorithm and the second preset algorithm. Exemplarily, for modules that are not easy to fall into local optimal solutions, such as the exposure module, exposure prediction module, post-baking prediction module, measurement module, etc., a local gradient-free optimization algorithm can be adopted to improve the computational efficiency. For modules that are easy to fall into local optimal solutions, such as the post-baking module, development module, post-baking and development module, etc., the global gradient-free optimization algorithm can be adopted to improve the optimization result.

[0094] In some embodiments of the modeling method of the present invention, the gradient-free optimization algorithm includes a first preset algorithm, a second preset algorithm, and a fourth preset algorithm, where the second preset algorithm is nested in the first preset algorithm, and the fourth preset algorithm is used to optimize each process parameter to be optimized in the subset related to the exposure process, and the first preset algorithm and the second preset algorithm are used to optimize each process parameter to be optimized in the processes downstream of the exposure process.

[0095] In this embodiment, the photoresist model includes an exposure module, an exposure prediction module, and at least two other simulation modules downstream of the exposure process.

[0096] The fourth preset algorithm may include a fifth preset algorithm and a sixth preset algorithm, where the sixth preset algorithm is nested within the fifth preset algorithm. The fifth preset algorithm is used to optimize the parameters in the exposure subset, and the sixth preset algorithm is used to optimize the parameters in the exposure prediction subset (such as the light intensity threshold parameter of the optical space image). The light intensity threshold parameter of the optical space image can be used to map the optical space image obtained by simulating the exposure module into a developed pattern. As an approximation, the developed pattern obtained from the light intensity threshold parameter of the optical space image can be directly used as the simulation result without simulation measurement by taking the graphic feature data of the read developed pattern, and the deviation index can be obtained by comparing it with the measured result. That is to say, the fourth preset algorithm can directly optimize the parameters in the exposure subset and the exposure prediction subset without relying on processes such as post-baking, development, and measurement.

[0097] Specifically, the fifth preset algorithm can search and update within the preset threshold range of the parameters in the exposure subset according to the deviation index and update the exposure simulation result, while the sixth preset algorithm searches and updates within the preset threshold range of the parameters in the exposure prediction subset and updates the deviation index. Through continuous nested iteration optimization until the minimum deviation index that meets the relevant conditions is obtained, the parameters in the corresponding exposure subset are used as the target parameters. At this time, the fourth preset algorithm can be terminated.

[0098] After obtaining the target parameters in the exposure subset, the target parameters can be input into the exposure module to output the target exposure result. In the subsequent iterative calculations of the first preset algorithm and the second preset algorithm, the target exposure result is always used as the input.

[0099] In this embodiment, by first optimizing the parameters to be optimized related to the exposure process, the optimization calculations of the parameters to be optimized in the exposure process and the subsequent processes are made independent of each other, which can reduce the number of exposure simulations and save computing resources.

[0100] In some embodiments of the modeling method of the present invention, the processes to which the parameters belong include any two or more of the following processes:

[0101] Exposure process, post-baking process, development process, or measurement process of measuring the developed pattern to obtain graphic features.

[0102] In some embodiments of the modeling method of the present invention, the set of parameters includes any one or more of the following parameters:

[0103] Exposure parameters of the photoresist and the concentration of the quencher in the exposure process;

[0104] Diffusion coefficient of the photoacid, diffusion coefficient of the quencher, reaction constant of the deprotection reaction, reaction order of the deprotection reaction, non-linear diffusion term, and surface correction term in the post-baking process;

[0105] The development rate, development threshold concentration, and development sensitivity of the photoresist in the development process;

[0106] The line width measurement deviation term and the sidewall angle measurement correction term in the measurement process.

[0107] In some embodiments of the modeling method of the present invention, the simulation results include a plurality of graphic features obtained by simulating and measuring the simulated development pattern obtained from the photoresist model. The measured results include a plurality of graphic features obtained by measuring the actual development pattern. The deviation index is the root mean square deviation or the mean absolute deviation between each graphic feature in the simulation result and the graphic feature in the corresponding measured result. Among them, the graphic features include the critical dimension of the pattern and / or the trench shape parameter.

[0108] For the chip layout of deep sub-micron processes, each chip layout may contain a huge amount of patterns. When judging the deviation between the simulation result and the measured result of the photoresist model, it is necessary to summarize the graphic feature data of as many patterns as possible to more accurately judge the difference between the simulated development pattern and the actual development pattern.

[0109] Exemplarily, sampling can be performed at multiple positions of the chip layout according to a preset rule, that is, measuring the patterns at multiple identical positions of the simulated development pattern and the actual development pattern to obtain a plurality of simulation results and measured results respectively. The simulation result and the measured result at the same position are used as a set of data for comparison, and then the comparison results of each set of data are statistically processed to obtain the root mean square deviation or the mean absolute deviation.

[0110] The root mean square deviation or the mean absolute deviation can generally judge the difference between the simulated development pattern and the actual development pattern. The smaller the value indicated by the deviation, the closer the simulated development pattern is to the actual development pattern, the closer the values of the optimized target parameters are to the actual values, and the better the accuracy of the photoresist model.

[0111] The flowchart provided in this embodiment is not intended to indicate that the operations of the method will be executed in any specific order, or that all operations of the method are included in every case. In addition, the method may include additional operations. Within the scope of the technical idea provided by the method in this embodiment, additional changes can be made to the above method.

[0112] It should be understood that in some embodiments, each part can be implemented by hardware, software, firmware, or a combination thereof. In the above embodiment, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system.

[0113] The embodiment of the present invention also provides a computer program product 10, a computer-readable storage medium 20, and a computer device 30. Figure 5Schematic diagram of a computer program product 10 according to an embodiment of the present invention. Figure 6 Schematic diagram of a computer-readable storage medium 20 according to an embodiment of the present invention. Figure 7 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, and when the computer program 11 is executed by a processor 32, the steps of any one of the above modeling methods are implemented. The computer-readable storage medium 20 stores the above computer program 11, and when the computer program 11 is executed by the processor 32, the steps of the modeling method of any one of the above embodiments are implemented. The computer device 30 may include a memory 31, a processor 32, and a computer program 11 stored on the memory 31 and running on the processor 32.

[0114] The computer program 11 for performing the operations of the present invention may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-related instructions, microcode, firmware instructions, status setting data, configuration data of an integrated circuit, 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 be executed entirely on the user's computer, partially on the user's computer, executed as a stand-alone 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 through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., using an Internet service provider through the Internet). In some embodiments, to perform aspects of the present invention, an electronic circuit, including, for example, a programmable logic circuit, a field-programmable gate array (FPGA), or a programmable logic array (PLA), may execute computer-readable program instructions by utilizing the status information of the computer-readable program instructions to personalize the electronic circuit.

[0115] For the description of this embodiment, the computer program product 10 is a related product including the computer program 11.

[0116] For the description of this embodiment, the computer-readable storage medium 20 is a tangible device capable of retaining and storing the computer program 11, which can be any device that can contain, store, communicate, propagate, or transmit the computer program 11 for use by an instruction execution system, apparatus, or device or in conjunction with these instruction execution systems, apparatuses, or devices. More specific examples (a non-exhaustive list) of the computer-readable storage medium 20 include the following: 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 versatile disc (DVD), memory stick, floppy disk, mechanically encoded devices, and any suitable combination of the above.

[0117] The computer device 30 can be, for example, a server, a desktop computer, a laptop computer, a tablet computer, or a smartphone. In some examples, the computer device 30 can be a cloud computing node. The computer device 30 can be described in the general context of computer system-executable instructions, such as program modules, executed by a computer system. Generally, program modules can include routines, programs, object programs, components, logic, data structures, etc. that perform specific tasks or implement specific abstract data types. The computer device 30 can be implemented in a distributed cloud computing environment where tasks are executed by remote processing devices linked through a communication network. In the distributed cloud computing environment, program modules can be located on local or remote computing system storage media including storage devices.

[0118] The computer device 30 can include a processor 32 suitable for executing stored instructions and a memory 31 that provides temporary storage space for the operation of the instructions during operation. The processor 32 can be a single-core processor, a multi-core processor, a computing cluster, or any other number of other configurations. The memory 31 can include random access memory (RAM), read-only memory, flash memory, or any other suitable storage system.

[0119] The computer device 30 can also include a network adapter / interface and an input / output (I / O) interface. The I / O interface allows data to be input and output with external devices that can be connected to the computer device. The network adapter / interface can provide communication between the computer device and a network, which is typically shown as a communication network.

[0120] At this point, 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 that conform to the principles of the present invention can still be directly determined or derived from the content disclosed in the present invention without departing from the spirit and scope of the present invention. Therefore, the scope of the present invention should be understood and recognized as covering all these other variations or modifications.

Claims

1. A method for modeling a photoresist model, characterized in that, Including: Obtaining a set of process parameters to be optimized that affect the developed pattern of the photoresist in the photoresist model; Dividing the set into multiple subsets based on the processes to which the parameters belong; Taking the minimum deviation index between the simulation result obtained from the photoresist model and the measured result as the optimization goal, and taking each of the subsets as an iteration unit to optimize each parameter in the set; wherein, during the iterative optimization process of each parameter in the subset in the downstream process, the parameters in the subset in the upstream process remain unchanged.

2. The modeling method according to claim 1, wherein The taking the minimum deviation index between the simulation result obtained from the photoresist model and the measured result as the optimization goal, and taking each of the subsets as an iteration unit to optimize each parameter in the set includes: Obtaining the preset threshold range of each parameter in the set; Inputting the initial values of each parameter in the set into the photoresist model to obtain an initial simulation result; Taking the deviation index between the initial simulation result and the measured result as the initial optimization goal, and performing iterative simulation based on a gradient-free optimization algorithm with each of the subsets as an iteration unit, and determining each target parameter within the preset threshold range of each parameter in the set to minimize the deviation index.

3. The modeling method according to claim 2, characterized in that The gradient-free optimization algorithm includes a first preset algorithm and a second preset algorithm, and the second preset algorithm is nested within the first preset algorithm; The second preset optimization algorithm is configured to, under the condition that the parameters in each of the subsets in the upstream process remain unchanged, take the minimum deviation index as the optimization goal, determine each candidate parameter within the preset threshold range of the parameters in the subset in the downstream process, and use the deviation index corresponding to each candidate parameter as the candidate deviation index; The first preset optimization algorithm is configured to take the minimum candidate deviation index obtained from the second preset optimization algorithm as the optimization goal, determine each target parameter within the preset threshold range of the parameters in the subset in the upstream process, and use each candidate parameter corresponding to the minimum candidate deviation index as the target parameter.

4. The modeling method according to claim 3, wherein At least one of the first preset algorithm and the second preset algorithm is a global gradient-free optimization algorithm.

5. The modeling method according to claim 3, characterized in that The processes to which the parameters belong include any two or more of the following processes: Exposure process, post-baking process, development process, or measurement process for measuring the developed pattern to obtain pattern features.

6. The modeling method according to claim 5, wherein The parameters in the set include any one or more of the following parameters: Exposure parameters of the photoresist and concentration of the quencher in the exposure process; Diffusion coefficient of the photoacid, diffusion coefficient of the quencher, reaction constant of the deprotection reaction, reaction order of the deprotection reaction, non-linear diffusion term, and surface correction term in the post-baking process; Development rate, development threshold concentration, and development sensitivity of the photoresist in the development process; Line width measurement deviation term and sidewall angle measurement correction term in the measurement process.

7. The modeling method according to claim 1, wherein The simulation results include a plurality of graphic features obtained by performing simulation measurement on the simulated development pattern obtained from the photoresist model; the measured results include a plurality of graphic features obtained by measuring the actual development pattern; The deviation index is the root mean square deviation or the mean absolute deviation between each graphic feature in the simulation results and the corresponding graphic feature in the measured results; Wherein, the graphic features include graphic critical dimensions and / or trench shape parameters.

8. A computer-readable storage medium, characterized in that, A computer program is stored thereon, and when the computer program is executed by a processor, the steps of the modeling method of the photoresist model according to any one of claims 1 to 7 are implemented.

9. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, the steps of the modeling method of the photoresist model according to any one of claims 1 to 7 are implemented.

10. A computer device, characterized in that, It includes a memory, a processor, and a computer program stored on the memory, and the processor executes the computer program to implement the steps of the modeling method of the photoresist model according to any one of claims 1 to 7.

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