Compact photoetching simulation system based on physical model

Abstract

The invention provides a compact photoetching simulation system based on a physical model, which relates to the technical field of photoetching simulation tools and physical modeling and comprises a model construction module, a kernel visualization module, a simulation calculation module, a result evaluation and optimization module, a model library management module, a real-time simulation module, an error compensation module and a simulation result verification module. The model building module is used for building a three-dimensional photoetching model according to the light source wavelength, the coherence factor, the numerical aperture and the aberration coefficient of a photoetching machine in the photoetching equipment as well as the photoresist type, the photoetching resolution requirement and the photoetching pattern feature size of the photoetching process. Diffraction of light in a projection objective, interference of light on the surface and inside of photoresist and physical and chemical processes of a photoresist molecule microscopic level are comprehensively considered, and compared with a basic physical model, the complex propagation path of light in photoetching equipment can be accurately simulated.

Description

Technical Field

[0001] The present invention relates to the technical fields of lithography simulation tools and physical modeling, and specifically to a compact lithography simulation system based on a physical model. Background Art

[0002] As the semiconductor industry continues to develop towards smaller process nodes and higher integration levels, lithography technology, as a crucial link in the chip manufacturing process, its accuracy and stability directly affect the performance, yield, and production efficiency of chips. Any slight deviation in the lithography process may cause damage or even scrapping of the chip performance, resulting in high economic costs and time costs. Therefore, accurately simulating the lithography process and deeply optimizing the lithography process have become the core problems that need to be solved urgently in the semiconductor manufacturing field.

[0003] Currently, in lithography simulation and process optimization, the following technical means are mainly used:

[0004] Basic physical model simulation: Early lithography simulations mostly relied on models that only covered some basic physical principles.

[0005] Single algorithm simulation: Some studies used a single simulation algorithm and simulated light propagation using the finite element time domain method.

[0006] General computing device assistance: When performing lithography simulation, many enterprises still rely on general computing devices, such as ordinary office computers or simple servers. These devices have insufficient computing power and memory capacity to meet the requirements of large-scale data processing when facing complex lithography simulation algorithms.

[0007] Traditional model management method: For the management of lithography models, in the past, a simple file storage method was mostly used, and the lithography models of different processes and devices were stored in a mixed manner.

[0008] Although the existing technology has made certain progress in lithography simulation and process optimization, there are still many drawbacks:

[0009] Problem 1: Due to excessive simplification, the basic physical model cannot accurately simulate the complex propagation path of light in the lithography equipment and the physical and chemical processes at the microscopic level in the photoresist, resulting in low accuracy of the simulation results and being unable to provide strong support for the development of advanced process technologies, seriously hindering the progress of chip manufacturing towards smaller sizes and higher precision.

[0010] Problem 2: Single algorithm simulation cannot fully utilize the advantages of different algorithms, and it is difficult to balance simulation accuracy while ensuring computational efficiency. Especially when dealing with multi-parameter and complex lithography scenarios, it cannot achieve comprehensive and efficient simulation and optimization, and cannot meet the requirements of increasingly complex lithography processes.

[0011] Problem 3: General computing devices are difficult to match the complex computing requirements of lithography simulation in terms of hardware performance, resulting in a long simulation process and seriously affecting the cycle and efficiency of process development. At the same time, due to insufficient hardware performance, some high-precision simulation algorithms cannot run effectively, further reducing the accuracy of simulation results.

[0012] Problem 4: Traditional model management methods lack systematicness and intelligence, with chaotic model storage and untimely updates. This makes it difficult to quickly call appropriate models during the adjustment and optimization of lithography processes and unable to update models in a timely manner according to process and equipment changes, restricting the coordinated development of lithography simulation and process optimization.

[0013] Therefore, a compact lithography simulation system based on physical models is needed to solve the above problems. Summary of the Invention

[0014] Technical Problems to be Solved

[0015] In view of the deficiencies of the prior art, the present invention provides a compact lithography simulation system based on physical models, which solves the problems in the above background technology.

[0016] Technical Solutions

[0017] To achieve the above objectives, the present invention is realized through the following technical solutions: A compact lithography simulation system based on physical models, including a model construction module, a kernel visualization module, a simulation calculation module, a result evaluation and optimization module, a model library management module, a real-time simulation module, an error compensation module, and a simulation result verification module;

[0018] The model construction module constructs a three-dimensional lithography model based on the light source wavelength, coherence factor, numerical aperture of the projection objective, aberration coefficient of the lithography equipment, and the type of photoresist, lithography resolution requirements, and lithography pattern feature size of the lithography process.

[0019] The kernel visualization module uses three-dimensional visualization technology to display the model kernel.

[0020] The simulation calculation module uses physical simulation algorithms to simulate and calculate the light propagation and chemical reactions in the photoresist during the lithography process.

[0021] The result evaluation and optimization module evaluates the line width uniformity, edge roughness, and overlay accuracy of the lithography pattern based on the simulation results, and optimizes the exposure dose and focus depth of the lithography process.

[0022] The model library management module classifies and manages the lithography models of step-and-scan projection lithography processes and ArF and KrF lithography machines, facilitating quick user calls and updates.

[0023] The real-time simulation module has the function of real-time simulation. It can be connected to the actual lithography equipment to obtain the exposure time, exposure intensity, temperature and humidity of the environment of the equipment in real time and perform simulation, and timely feedback the simulation results.

[0024] The error compensation module introduces an error compensation algorithm to analyze and compensate the errors in the simulation results, improving the accuracy of the simulation.

[0025] The simulation result verification module verifies the accuracy and reliability of the simulation model by comparing with the actual size measurement data of the lithography pattern and the actual dissolution rate data of the photoresist in the actual lithography experiment, and continuously optimizes the simulation model.

[0026] Preferably, in the multimodal information integration module, a modeling method based on wave optics theory is adopted. Considering the diffraction of light in the projection objective of the lithography equipment and the interference of light on the surface and inside of the photoresist, an accurate lithography model is constructed. At the same time, combined with quantum mechanics theory, the physical and chemical processes at the microscopic level of electron transition and chemical bond breaking of photoresist molecules during the lithography process are accurately simulated. And this module introduces a machine learning algorithm to adaptively adjust the model by using the light source parameters, photoresist characteristic parameters, lithography pattern size deviation data of historical lithography experiments, and the corresponding simulation results, improving the adaptability of the model to different photoresist types and different lithography scene complexities of lithography patterns.

[0027] Preferably, in the kernel visualization module, virtual reality or augmented reality technology is used to enable users to immerse themselves in observing the propagation path of light in the photoresist and the dynamic chemical reactions of photoresist molecules in the model kernel through wearing a VR helmet or using an AR device, intuitively understanding the lithography process. It supports multiple people to enter the virtual or augmented reality environment simultaneously through network connection to mark, discuss and collaboratively analyze the model kernel. In addition, this module has a dynamic annotation function to real-time annotate the numerical values, units and change trends of key physical quantities such as the light intensity distribution and the photoresist reaction rate in the model kernel, facilitating user analysis.

[0028] Preferably, in the simulation calculation module, the physical simulation algorithm adopts the finite element time domain method and the fast Fourier transform method. The finite element time domain method divides the lithography area into three-dimensional grids and iteratively solves the propagation of light in the time domain through Maxwell's equations; the fast Fourier transform method uses frequency domain transformation to calculate the diffraction and interference of light, improving the efficiency and accuracy of the simulation calculation. At the same time, parallel computing technology is introduced to distribute the simulation tasks to multiple computing cores or computing nodes for simultaneous calculation, accelerating the simulation process. This module adopts an adaptive grid division technology to automatically adjust the grid density according to the severity of the change of physical quantities such as light intensity and photoresist reaction rate during the lithography process, using fine grids in areas where physical quantities change violently and coarse grids in areas where the change is gentle, ensuring the simulation accuracy and reducing the calculation amount.

[0029] Preferably, in the result evaluation and optimization module, a non-dominated sorting genetic algorithm multi-objective optimization algorithm is adopted to simultaneously optimize the exposure dose, depth of focus, and photoresist development time parameters of the lithography process, improve the resolution, line width uniformity, and edge roughness indicators of the lithography pattern. Combining with the sensitivity analysis method, by changing the value of a single parameter, observing the change range of the line width and overlay accuracy indicators of the lithography pattern, the influence degree of the key parameters on the lithography effect is determined. In addition, a deep learning model is introduced into this module, which is trained with a large amount of lithography effect simulation data under different process parameter combinations and lithography pattern measurement data from actual lithography experiments. Taking the lithography process parameters as the input and the predicted lithography effect indicators as the output, it can quickly predict the lithography effect under different process parameter combinations and provide guidance for the optimization process.

[0030] Preferably, the model library management module classifies and stores the lithography models of step-and-repeat projection lithography process, scanning projection lithography process, ArF lithography machine, and KrF lithography machine, and has an automatic model update function. When the system detects changes in the photoresist formulation of the lithography process, adjustment of the exposure mode, and fine-tuning of the light source wavelength of the lithography machine or changes in the projection objective aberration correction parameters of the equipment, it automatically obtains the latest model template of the corresponding change parameters from the cloud, and adjusts and updates the model according to the local historical data and actual situation to ensure the timeliness and accuracy of the model.

[0031] Preferably, the real-time simulation module is connected to the actual lithography equipment, obtains the exposure time, exposure intensity, temperature, and humidity of the environment of the equipment in real time for simulation and promptly feedbacks the results. At the same time, it conducts intelligent early warning on the actual lithography equipment according to the real-time simulation results. When the simulation results show that the line width deviation and edge roughness indicators of the lithography pattern exceed the allowable range, or the reaction degree of the photoresist does not meet the requirements, it issues an alarm to the equipment operator through sound alarm and pop-up prompt, and provides suggestions for adjusting the exposure dose and depth of focus parameters.

[0032] Preferably, the error compensation module analyzes and compensates the light intensity calculation error and photoresist reaction rate calculation error in the simulation results caused by model simplification and numerical calculation methods by introducing an error compensation algorithm, establishes an error prediction model, and uses machine learning and statistical analysis methods to predict the types and magnitudes of systematic errors and random errors under different lithography process parameter combinations and equipment conditions, and adjusts relevant parameters according to the prediction results during the simulation process for error control.

[0033] Preferably, the simulation result verification module verifies the accuracy and reliability of the simulation model by comparing it with the actual size measurement data of the lithography pattern and the actual dissolution rate data of the photoresist in the actual lithography experiment, uses statistical analysis methods to process a large amount of verification data, and establishes a model accuracy evaluation index system including average error rate, root mean square error, and correlation coefficient indicators, so as to comprehensively and objectively evaluate the performance of the simulation model under different lithography process parameters and different lithography pattern characteristics, and optimizes the material refractive index of the wave optics model and the reaction constant model parameters of the quantum mechanics model according to the evaluation results.

[0034] Preferably, the model building module inputs lithography equipment parameters and process requirements and outputs a three-dimensional lithography model, the kernel visualization module inputs model kernel data and outputs a visualization display interface, the user selects and combines modules according to needs, adds special simulation module function modules for new photoresists, adapts to changes in lithography simulation needs, and has a data security management mechanism. The enterprise-specific lithography process parameters, experimental data sensitive data and model data of the simulation process are encrypted and stored using the AES symmetric encryption algorithm and encrypted and transmitted through the SSL / TLS network protocol. Access rights of ordinary users and administrators of different roles are set. Ordinary users perform simulation operations and view results, and administrators perform system settings and data management operations to ensure data security and privacy. Data interaction and integration are performed with lithography process design software, lithography equipment control software and lithography-related software through XML and JSON standardized data interfaces.

[0035] Beneficial Effects

[0036] The present invention provides a compact lithography simulation system based on a physical model, which has the following beneficial effects:

[0037] 1. The model building module in the present invention is based on the theory of wave optics and quantum mechanics, and comprehensively considers the diffraction of light in the projection objective, the interference of light on the surface and inside of the photoresist, and the physical and chemical processes at the microscopic level of the photoresist molecules. Compared with the basic physical model, it can accurately simulate the complex propagation path of light in the lithography equipment, greatly improve the simulation accuracy, provide a solid basis for the development of advanced process technology, and effectively promote the development of chip manufacturing towards smaller size and higher precision. The simulation calculation module comprehensively uses the finite element time domain method and the fast Fourier transform method, and introduces parallel computing technology and adaptive meshing technology. This can not only give full play to the advantages of different algorithms, while ensuring the calculation efficiency and taking into account the simulation accuracy, but also automatically adjust the grid density according to the changes in the physical quantities of the lithography process, and comprehensively and efficiently simulate the complex processes such as light propagation and chemical reactions in the photoresist during the lithography process, so as to meet the increasingly complex lithography process requirements.

[0038] 2. The model library management module of the present invention classifies and stores various lithography models and has an automatic update function. When lithography process or equipment parameters change, it can automatically obtain the latest model template from the cloud and update the model in combination with local data. Compared with the traditional model management method, it not only facilitates users to quickly retrieve and call models, but also ensures the timeliness and accuracy of models, promotes the coordinated development of lithography simulation and process optimization. The real-time simulation module can be connected to the actual lithography equipment to obtain the equipment operation parameters and environmental data in real time for simulation and timely feedback the results. When the simulation results show that the line width deviation and edge roughness index of the lithography pattern exceed the allowable range, or the reaction degree of the photoresist does not meet the requirements, it can issue an alarm to the equipment operator through sound alarm and pop-up prompt, and provide suggestions for adjusting parameters, which helps to timely discover and solve problems in the lithography process and ensure the stable progress of the lithography process.

[0039] 3. In the present invention, the error compensation module introduces an error compensation algorithm to analyze and compensate for the errors in the simulation results caused by model simplification and numerical calculation methods, establish an error prediction model, adjust relevant parameters according to the prediction results, and improve the simulation accuracy. The simulation result verification module comprehensively and objectively evaluates the performance of the simulation model by comparing with the actual lithography experiment data using statistical analysis methods, and optimizes the model parameters according to the evaluation results to continuously improve the simulation model and enhance the overall reliability of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 is the overall framework diagram of the present invention;

[0041] Figure 2 is the system flow schematic diagram of the present invention;

[0042] Figure 3 is the simulation diagram of the lithography light intensity distribution of the present invention;

[0043] Figure 4 is the simulation diagram of the electric field distribution of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0044] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention. Specific Embodiment 1:

[0046] As Figures 1-4 shown, for the compact lithography simulation system based on the physical model, the overall working principle is as follows:

[0047] The system starts with a model construction module, which receives lithography equipment parameters (wavelength of the lithography machine light source, coherence factor, numerical aperture of the projection objective lens, aberration coefficient) and lithography process requirements (type of photoresist, lithography resolution requirement, feature size of the lithography pattern). By applying the theory of wave optics to consider the diffraction of light in the projection objective lens and the interference on and inside the photoresist surface, combining with the theory of quantum mechanics to simulate the electronic transition and chemical bond breakage of photoresist molecules, and at the same time using machine learning algorithms to adaptively adjust the model with historical data, a three-dimensional lithography model is constructed. This model serves as basic data to support subsequent modules.

[0048] The simulation calculation module obtains the three-dimensional lithography model, divides the lithography area into three-dimensional grids using the finite element time domain method, and iteratively solves the propagation of light in the time domain through Maxwell's equations, or calculates the diffraction and interference of light in the frequency domain using the fast Fourier transform method. At the same time, parallel computing technology is introduced to accelerate the calculation, and the adaptive grid division technology is used to adjust the grid density according to the changes of physical quantities such as light intensity and photoresist reaction rate, and simulates and calculates the light propagation and chemical reactions inside the photoresist during the lithography process, and outputs the simulation result data.

[0049] The kernel visualization module obtains the model kernel data from the simulation calculation module, and uses virtual reality or augmented reality technology to allow users to immerse themselves in observing the model kernel by wearing a VR helmet or using an AR device, such as the propagation path of light in the photoresist and the dynamic chemical reactions of photoresist molecules. It supports multi-person collaboration and has a dynamic annotation function, generating a visual display interface to facilitate users to intuitively understand the lithography process and assist users in analyzing the model.

[0050] The result evaluation and optimization module receives the results from the simulation calculation module, uses the non-dominated sorting genetic algorithm to optimize lithography process parameters such as exposure dose, focus depth, and photoresist development time simultaneously, combines the sensitivity analysis method to determine the influence degree of key parameters on indicators such as line width uniformity, edge roughness, and overlay accuracy of the lithography pattern, and uses a large amount of simulation and experimental data to train through a deep learning model to quickly predict the lithography effect under different process parameter combinations, providing guidance for optimizing the lithography process, and then outputs the optimized process parameters.

[0051] The model library management module classifies, manages, and stores different types of lithography models such as step-and-repeat projection lithography process, scanning projection lithography process, and ArF lithography machines, KrF lithography machines, etc. When the system detects changes in lithography process (change in photoresist formulation, adjustment of exposure mode) or equipment (fine adjustment of the wavelength of the lithography machine light source, aberration correction of the projection objective lens) parameters, it automatically obtains the latest model template from the cloud and updates the model in combination with local data to ensure the timeliness and accuracy of the model, facilitating users to quickly call and update the model, and providing model support for the model construction module.

[0052] The real-time simulation module is connected to the actual lithography equipment to obtain in real time parameters such as the exposure time, exposure intensity, temperature, and humidity of the environment of the equipment, perform real-time simulation, and promptly feedback the results. If the simulation results show that the line width deviation and edge roughness index of the lithography pattern exceed the allowable range, or the reaction degree of the photoresist does not meet the requirements, an alarm is sent to the equipment operator through a sound alarm and a pop-up window prompt, and suggestions for adjusting parameters such as the exposure dose and focus depth are provided. At the same time, the real-time data is fed back to the simulation calculation module for real-time correction of the simulation model.

[0053] The error compensation module processes the results output by the simulation calculation module, analyzes and compensates for the calculation errors of light intensity and photoresist reaction rate caused by model simplification and numerical calculation methods. An error prediction model is established through machine learning and statistical analysis methods to predict the types and magnitudes of errors under different combinations of lithography process parameters and equipment conditions. During the simulation process, relevant parameters are adjusted according to the prediction results to improve the simulation accuracy, and the compensated results are fed back to the result evaluation and optimization module and other relevant modules.

[0054] The simulation result verification module compares the simulation results with the actual size measurement data of the lithography pattern and the actual dissolution rate data of the photoresist in the actual lithography experiment, processes a large amount of verification data using statistical analysis methods, and establishes a model accuracy evaluation index system including indicators such as the average error rate, root mean square error, and correlation coefficient to comprehensively and objectively evaluate the performance of the simulation model under different lithography process parameters and different lithography pattern characteristics. According to the evaluation results, model parameters such as the material refractive index of the wave optics model and the reaction constant of the quantum mechanics model are optimized, and the optimized model is fed back to relevant modules such as the model construction module to continuously improve the entire simulation system.

[0055] Each module of the system is closely connected, forming a closed-loop workflow through data input and output. At the same time, it has a data security management mechanism, encrypts and stores sensitive data using the AES symmetric encryption algorithm and encrypts and transmits it through the SSL / TLS network protocol, sets different role access permissions to ensure data security, and also conducts data interaction and integration with related software such as lithography process design software and lithography equipment control software through XML and JSON standardized data interfaces to meet different lithography simulation requirements. Specific Embodiment 2:

[0057] As Figures 1-4 shown, the key algorithms mentioned in Embodiment 1 are analyzed in detail below, including their core mathematical formulas and explanations:

[0058] Model Construction Module:

[0059] Wave Optics Theoretical Modeling - Maxwell's Equations:

[0060] Differential Form of Maxwell's Equations:

[0061]

[0062] wherein is the curl operator, which is used to describe the rotation characteristics of a vector field. In lithography simulation, it helps us analyze the propagation direction and mode changes of light. is the divergence operator, which is used to measure the divergence or convergence degree of a vector field at a certain point. In light propagation, it can be used to analyze the sources and sinks of the electric field and magnetic field. E is the electric field strength, with the unit of volts per meter (V / m). In lithography, the electric field strength determines the interaction strength between light and photoresist molecules and affects the exposure and reaction processes of the photoresist. B is the magnetic flux density, with the unit of tesla (T). As an electromagnetic wave, the magnetic field component of light is correlated with the electric field component and jointly affects the light propagation and lithography process. H is the magnetic field strength, with the unit of amperes per meter (A / m), which is related to the magnetic flux density and describes the characteristics of the magnetic field in different media. D is the electric displacement vector, with the unit of coulombs per square meter (C / m 2 ), which takes into account the polarization effect of the medium and is important for simulating the light propagation of different media such as photoresists in lithography. J is the current density. In the lithography process, although the current effect is usually relatively small, it also needs to be considered in some special cases (such as those involving charge transfer, etc.). ρ is the charge density, which describes the distribution of charges in space. is the partial derivative with respect to time, which is used to describe the rate of change of a physical quantity with time and reflects the dynamic characteristics of light propagation and the lithography process in lithography simulation.

[0063] Maxwell's equations describe the basic properties of the electric field and magnetic field and their mutual relationships, and are the basis of the wave optics theory. In lithography modeling, by solving these equations, the propagation of light in lithography equipment can be simulated, including diffraction and interference phenomena.

[0064] Precisely simulate the propagation of light in the projection objective lens, considering the diffraction and interference effects of light, so as to construct a more accurate lithography model and improve the simulation accuracy of lithography patterns.

[0065] Usage method: In the finite-difference time-domain (FDTD) method, the lithography area is divided into a three-dimensional grid, and Maxwell's equations are discretized at each grid point. By iteratively solving these discrete equations, the propagation of light in the time domain can be simulated.

[0066] Quantum mechanics theory modeling - Schrödinger equation:

[0067] For a microscopic particle (such as an electron in a photoresist molecule), its time-independent Schrödinger equation is:

[0068] wherein is the Hamiltonian operator, which represents the total energy of the system, including kinetic energy and potential energy. In the quantum simulation of photoresist molecules, it includes the kinetic energy of electrons and the potential energy of the interactions between electrons and nuclei, as well as between electrons. ψ is the wave function, which describes the quantum state of microscopic particles. The square of the modulus of the wave function, |ψ| 2 represents the probability density of the particle appearing at a certain point in space. In lithography, the wave function can be used to analyze the distribution and state of electrons in photoresist molecules. E is the energy of the particle, with the unit of joule (J) or electron volt (eV). For the electrons in photoresist molecules, their energy states determine the stability of chemical bonds and the possibility of chemical reactions.

[0069] The Schrödinger equation describes the evolution of the quantum state of microscopic particles over time. In the lithography process, it is used to simulate microscopic processes such as the transition of electrons and the breaking of chemical bonds within photoresist molecules.

[0070] More accurate simulation of the physical and chemical processes at the microscopic level during the lithography process helps to understand the reaction mechanism of photoresist and improve the accuracy of the lithography model at the microscopic scale.

[0071] By determining the potential energy function of photoresist molecules, constructing the Hamiltonian operator, and then solving the Schrödinger equation to obtain the wave function and energy eigenvalues, the state of electrons and the possibility of chemical reactions can be further analyzed.

[0072] Simulation calculation module:

[0073] Finite-difference time-domain (FDTD) method:

[0074] In the Yee grid, the update equations for the electric and magnetic fields (taking the two-dimensional case as an example):

[0075]

[0076]

[0077] where E x represents the x-component of the electric field, with the unit of volt per meter (V / m). In lithography simulation, it describes the distribution and variation of the photoelectric field in the x direction. H zDenotes the z - component of the magnetic field, with the unit of amperes per meter (A / m), which describes the distribution and variation of the optical magnetic field in the z - direction. n represents the time step, which is an integer used to mark the time progress of the simulation. As n increases, the simulation time advances. i and j represent the coordinates of the spatial grid points, which are used to determine the position in the two - dimensional Yee grid. Δt is the time step size, with the unit of seconds (s), which determines the advancement interval of the simulation in time. A smaller Δt can improve the accuracy of the simulation but will increase the calculation time. ∈ is the permittivity, with the unit of farads per meter (F / m), which reflects the polarization characteristics of the medium under the action of an electric field. Different photoresists and optical media have different permittivities. μ is the magnetic permeability, with the unit of henries per meter (H / m), which describes the magnetic characteristics of the medium under the action of a magnetic field. Δy is the spatial step size, with the unit of meters (m), which determines the grid spacing of the Yee grid in the y - direction. A smaller Δy can improve the spatial resolution but will increase the computational amount.

[0078] The FDTD method simulates the propagation of light in space by iteratively updating the values of the electric and magnetic fields in the time domain. The Yee grid is a staggered grid that places the electric and magnetic field components at different grid points to ensure the stability of numerical calculations.

[0079] Efficiently and accurately simulate the propagation of light in the lithography region, considering complex phenomena such as light diffraction and interference, providing a basis for the simulation of the lithography process.

[0080] Usage method: First, divide the lithography region into a three - dimensional grid, initialize the values of the electric and magnetic fields, and then update the electric and magnetic fields sequentially at each time step according to the above update equations until the desired simulation time is reached.

[0081] Fast Fourier Transform (FFT) method:

[0082] Discrete Fourier Transform (DFT):

[0083] Where X[k] is the result of the discrete Fourier transform of the discrete signal x[n], which is a complex - valued sequence representing the components of the signal in the frequency domain. x[n] is the discrete signal in the time domain, which can be the sampled values of the optical field in the time domain in lithography simulation. n is the index of the discrete signal in the time domain, with a value range from 0 to N - 1. k is the index of the discrete signal in the frequency domain, also with a value range from 0 to N - 1. N is the length of the signal, that is, the number of sampling points, and j is the imaginary unit. e is the natural constant, approximately equal to 2.71828.

[0084] The FFT method calculates the diffraction and interference of light quickly by converting the signal from the time domain to the frequency domain and utilizing the characteristics of the frequency domain. In lithography simulation, it can be used to handle problems of light propagation and imaging.

[0085] Problem to be solved: Improve the efficiency of light propagation and imaging calculations, reduce the calculation time, especially for large-scale lithography simulation problems.

[0086] Usage method: Take the time-domain distribution of the light field as the input, convert it to the frequency domain through the FFT algorithm, perform diffraction and interference calculations in the frequency domain, and then convert it back to the time domain through the inverse FFT to obtain the final light field distribution.

[0087] Result evaluation and optimization module:

[0088] Non-dominated sorting genetic algorithm (NSGA-II):

[0089] Non-dominated sorting: For two individuals p and q, if individual p is not inferior to individual q in all objective functions and is superior to individual q in at least one objective function, then individual p is said to dominate individual q. The individuals in the population are divided into different ranks through non-dominated sorting.

[0090] Crowding distance calculation:

[0091]

[0092] where d i is the crowding distance of individual i, which is used to measure the sparseness of the individual in the population. The larger the crowding distance, the sparser the individuals around this individual. M is the number of objective functions. In lithography process optimization, it may include multiple objectives such as the resolution of the lithography pattern, line width uniformity, and edge roughness. is the value of individual i on the m-th objective function. For example, when m = 1 represents the resolution objective function, is the resolution index value corresponding to individual i. and are respectively the maximum and minimum values of the m-th objective function in the current population, which are used for normalizing the objective function values.

[0093] NSGA-II is a multi-objective optimization algorithm that selects excellent individuals through non-dominated sorting and crowding distance calculation, and gradually evolves the population to find a set of optimal non-dominated solutions (Pareto optimal solutions).

[0094] Problem to be solved: Simultaneously optimize multiple lithography process parameters (such as exposure dose, depth of focus, lithography resist development time, etc.) to improve the lithography effect (such as indicators such as the resolution of the lithography pattern, line width uniformity, and edge roughness).

[0095] Usage method: First, initialize a population, where each individual represents a set of lithography process parameters. Perform non-dominated sorting and crowding distance calculation on the individuals in the population, select excellent individuals for crossover and mutation operations to generate a new population. Repeat this process until the termination condition is met (such as reaching the maximum number of iterations), and finally obtain a set of optimal process parameter combinations.

[0096] Sensitivity analysis method:

[0097]

[0098] Where S is the sensitivity, used to measure the influence degree of the change of parameter x on the dependent variable y, Δx is the change amount of parameter x, such as the change value of the exposure dose, x is the original value of the parameter, such as the initial exposure dose, Δy is the change amount of the dependent variable y (such as the line width of the lithography pattern, overlay accuracy, etc.), and y is the original value of the dependent variable, such as the initial line width of the lithography pattern.

[0099] Sensitivity analysis is used to measure the influence degree of the change of a certain parameter on the dependent variable. By calculating the sensitivity, it can be determined which parameters have a greater impact on the lithography effect, that is, the key parameters.

[0100] Problem to be solved: Determine the influence degree of key parameters on the lithography effect, and provide a basis for the optimization of process parameters.

[0101] Usage method: Fix other parameters, change the value of a certain parameter, record the change of the dependent variable, and then calculate the sensitivity according to the above formula. Repeat this process for all parameters of interest to obtain the sensitivity values of each parameter, and compare the magnitudes of the sensitivity values to determine the key parameters. Specific embodiment three:

[0103] Such as Figures 1-4 shown, the following is a detailed description of the specific application logic steps of each module and algorithm in the compact lithography simulation system based on the physical model:

[0104] 1. Model construction module:

[0105] Input data: Obtain parameters such as the wavelength of the lithography machine light source, coherence factor, numerical aperture of the projection objective lens, aberration coefficient, as well as the type of photoresist, lithography resolution requirement, and feature size of the lithography pattern.

[0106] Fluctuation optical modeling: Based on the theory of fluctuation optics, considering the diffraction of light in the projection objective lens and the interference phenomena of light on the surface and inside of the photoresist, use Maxwell's equations to construct a light propagation model.

[0107] Quantum Mechanics Modeling: Combining quantum mechanics theory, for microscopic processes such as the electronic transition and chemical bond breakage of photoresist molecules, physical and chemical process models at the microscopic level are constructed through theories such as the Schrödinger equation.

[0108] Machine Learning Optimization: Using the light source parameters, photoresist characteristic parameters, photolithography pattern size deviation data of historical photolithography experiments and the corresponding simulation results, machine learning algorithms are used to adaptively adjust the constructed model to improve the adaptability of the model to different photoresist types and photolithography scenarios with different photolithography pattern complexities. Finally, a three-dimensional photolithography model is output.

[0109] 2. Kernel Visualization Module:

[0110] Data Acquisition: Obtain model kernel data from the simulation calculation module, including information such as the propagation path of light in the photoresist and the chemical reaction dynamics of photoresist molecules.

[0111] Technical Application: Using virtual reality (VR) or augmented reality (AR) technology, convert the model kernel data into a visual scene. Users enter this scene by wearing a VR headset or using an AR device to achieve immersive observation.

[0112] Interactive Function Implementation: Support multiple people to enter the virtual or augmented reality environment simultaneously through network connection to mark, discuss and collaboratively analyze the model kernel. In addition, using the dynamic annotation function, the numerical values, units, and change trends of key physical quantities such as the light intensity distribution and photoresist reaction rate of the model kernel are annotated in real time to facilitate user analysis and understanding. Finally, a visual display interface is output.

[0113] 3. Simulation Calculation Module:

[0114] Model Reception: Receive the three-dimensional photolithography model generated by the model construction module.

[0115] Algorithm Selection and Execution:

[0116] Finite Difference Time Domain (FDTD) Method: Divide the photolithography area into three-dimensional grids, discretize Maxwell's equations at each grid point, and simulate the light propagation process by iteratively solving these discrete equations in the time domain.

[0117] Fast Fourier Transform (FFT) Method: Convert the time domain distribution of the light field to the frequency domain and use the frequency domain transformation to quickly calculate the diffraction and interference of light.

[0118] Means for efficiency improvement: Introduce parallel computing technology, distribute simulation tasks to multiple computing cores or computing nodes for simultaneous calculation to accelerate the simulation process. At the same time, adopt adaptive mesh generation technology, automatically adjust the mesh density according to the severity of changes in physical quantities such as light intensity and photoresist reaction rate during the lithography process, use fine meshes in areas with drastic physical quantity changes, and use coarse meshes in areas with gentle changes, reducing the amount of calculation while ensuring simulation accuracy. Finally, output the simulation calculation results of the lithography process.

[0119] 4. Result evaluation and optimization module:

[0120] Result input: Receive the result data of the simulation calculation module, including the simulation results of lithography patterns, etc.

[0121] Multi-objective optimization: Adopt the non-dominated sorting genetic algorithm (NSGA-II), simultaneously optimize parameters such as exposure dose, depth of focus, and photoresist development time of the lithography process to improve indicators such as the resolution, line width uniformity, and edge roughness of the lithography pattern.

[0122] Sensitivity analysis: By changing the value of a single parameter, observe the change range of indicators such as the line width and overlay accuracy of the lithography pattern, and determine the influence degree of key parameters on the lithography effect.

[0123] Deep learning prediction: Introduce a deep learning model, train it using a large amount of lithography effect simulation data under different process parameter combinations and lithography pattern measurement data from actual lithography experiments, use lithography process parameters as input and predicted lithography effect indicators as output, quickly predict the lithography effect under different process parameter combinations, and provide guidance for the optimization process. Finally, output the evaluated lithography effect and the optimized lithography process parameters.

[0124] 5. Model library management module:

[0125] Model collection and classification: Collect different types of lithography models such as step-and-repeat projection lithography process, scanning projection lithography process, and ArF lithography machines, KrF lithography machines, etc., and classify and manage them according to process and equipment types.

[0126] Storage and update: Store the classified models locally, and at the same time have the function of automatic model update. When the system detects parameter changes such as the change of the photoresist formula of the lithography process, the adjustment of the exposure mode, or the fine-tuning of the light source wavelength of the lithography machine of the equipment, the correction of the projection objective aberration, etc., automatically obtain the latest model template of the corresponding changed parameters from the cloud, and adjust and update the model according to the local historical data and actual situation to ensure the timeliness and accuracy of the model. Facilitate users to quickly call and update the model when needed in the model construction module, etc.

[0127] 6. Real-time simulation module:

[0128] Device connection and data acquisition: Connect to the actual lithography device and obtain in real time parameters such as the exposure time, exposure intensity, temperature, and humidity of the environment of the device.

[0129] Real-time simulation: Use the obtained real-time parameters and combine with the algorithms of the simulation calculation module to perform real-time simulation calculations.

[0130] Result feedback and warning: Timely feedback the simulation results. If the simulation results show that the line width deviation and edge roughness index of the lithography pattern exceed the allowable range, or the reaction degree of the photoresist does not meet the requirements, an alarm is sent to the device operator through a sound alarm and a pop-up window prompt, and suggestions for adjusting parameters such as the exposure dose and focus depth are provided. At the same time, the real-time data is fed back to the simulation calculation module for real-time correction of the simulation model.

[0131] 7. Error compensation module:

[0132] Error analysis: Analyze the results output by the simulation calculation module to find out the calculation errors of light intensity, reaction rate of photoresist, etc. caused by reasons such as model simplification and numerical calculation methods.

[0133] Model establishment: Use machine learning and statistical analysis methods to establish an error prediction model based on historical simulation data and error data to predict the types and magnitudes of systematic errors and random errors under different combinations of lithography process parameters and equipment conditions.

[0134] Error compensation and control: During the simulation process, adjust relevant parameters according to the results of the error prediction model to compensate for and control the errors and improve the simulation accuracy. Feed the compensated results back to the result evaluation and optimization module and other relevant modules.

[0135] 8. Simulation result verification module:

[0136] Data comparison: Compare the simulation results with the actual size measurement data of the lithography pattern and the actual dissolution rate data of the photoresist in the actual lithography experiment.

[0137] Statistical analysis and index establishment: Use statistical analysis methods to process a large amount of verification data and establish a model accuracy evaluation index system including indicators such as average error rate, root mean square error, and correlation coefficient to comprehensively and objectively evaluate the performance of the simulation model under different lithography process parameters and different lithography pattern characteristics.

[0138] Model optimization: Optimize the model parameters such as the material refractive index of the wave optics model and the reaction constant of the quantum mechanics model according to the evaluation results, and feed the optimized model back to relevant modules such as the model construction module to continuously improve the entire simulation system. Specific embodiment four:

[0140] Such asFigures 1-4 As shown, the following is a detailed hardware composition and hardware description of each module in Example 1:

[0141] Model building module: A high-performance computer host equipped with an Intel Xeon multi-core, high-frequency CPU and more than 64GB of memory is used as the computing core, and an NVMe protocol high-speed solid-state drive with a sequential read speed of over 3000MB / s is used to store historical data, algorithm libraries and intermediate results. With the help of NVIDIA Quadro series professional graphics cards, CUDA technology is used to accelerate graphics rendering and matrix operations.

[0142] Kernel visualization module: Use high-resolution VR devices such as HTC Vive, Oculus Rift or Microsoft HoloLens AR devices to present model kernel details. It is driven by a graphics workstation equipped with NVIDIA RTX A6000 GPU, high-speed memory and high-performance CPU. For multi-person collaboration, an enterprise-level wireless router or Gigabit Ethernet switch that supports 802.11ac and above protocols is required to ensure the network.

[0143] Simulation computing module: A supercomputing cluster composed of multiple computing nodes equipped with AMD EPYC series CPUs and NVIDIA A100 GPUs. The nodes are interconnected through an InfiniBand network with a bandwidth of over 100 Gbps, and are equipped with Dell EMC PowerStore series PB-level large-capacity storage arrays, and temperature is controlled by air cooling or liquid cooling systems.

[0144] Result evaluation and optimization module: A workstation host equipped with an Intel Core i9 series CPU and 32GB of memory is used to process data, data is stored through ordinary mechanical hard drives or solid-state drives, and results and suggestions are displayed on a high-resolution LCD display.

[0145] Model library management module: It is managed by server hosts such as Lenovo ThinkSystem SR650 equipped with Intel Xeon Scalable processors and 64GB of memory. The distributed storage system is composed of Ceph distributed storage architecture and ordinary server hard disks, and interacts with the cloud through a 10Gbps Ethernet interface.

[0146] Real-time simulation module: Use an industrial control computer with multiple communication interfaces (such as RS-485 and Ethernet) to connect to the lithography equipment to obtain parameters, convert analog signals through data acquisition cards such as NI PCI-6259, and use Siemens SIMATIC WinCCOA real-time database to store data.

[0147] Error compensation module: The algorithm is run on a data analysis server equipped with Huawei Kunpeng processor and NVIDIA T4 GPU. Data is stored using a storage solution similar to the result evaluation and optimization module based on data characteristics and access frequency.

[0148] Simulation result verification module: The photolithography pattern size is obtained through an atomic force microscope, the photoresist dissolution rate is measured with a tester based on the optical interference principle, and the data is analyzed by a data processing workstation equipped with an Intel Xeon W processor and 128GB of memory, and the data is stored in a large-capacity disk array.

[0149] The following are the attached Figure 3 and attached Figure 4 Further analysis and expansion of the simulation diagram in this solution:

[0150] Analysis of photolithography light intensity distribution simulation diagram:

[0151] Overall shape and trend: The light intensity distribution in the area of interest (such as the area where the photolithography pattern is located) should meet the expectations of the photolithography process if the pattern is circular. For example, if the photolithography pattern is circular, the light intensity distribution may be relatively concentrated and uniform in the circular area and gradually weaken at the edge.

[0152] Intensity value range: Pay attention to the value range of light intensity, and determine whether the maximum and minimum values of light intensity are reasonable in combination with the initial light intensity and the calculation process of propagation and attenuation. If the actual photolithography process requires a specific light intensity threshold to trigger the photoresist reaction, it is necessary to confirm whether the light intensity in the simulation diagram can cover the threshold range and whether the light intensity in the key area meets the process standards.

[0153] Symmetry and uniformity: Check whether the light intensity distribution is symmetrical. Ideally, if there is no obvious asymmetry in the lithography equipment and process parameters, the light intensity distribution should have a certain degree of symmetry, such as symmetry about the central axis. In terms of uniformity, pay attention to the uniformity of light intensity in the area where the lithography pattern is located. Uniform light intensity helps to obtain more consistent lithography effects. If there is an obvious area of uneven light intensity, it may indicate a problem with the model or parameter setting, or reflect the characteristics of the lithography equipment itself (such as the unevenness of the light source).

[0154] Comparison with theoretical model: The light intensity distribution in the simulation is compared with the expected wave optics theory, taking into account the propagation and attenuation of light in the photoresist. If it is consistent with the theory, the light intensity should decay exponentially with the increase of propagation distance. If it is inconsistent with the theory, it may be that the model is over-simplified or there are errors in simulating phenomena such as light propagation and interference.

[0155] Electric field distribution simulation diagram analysis:

[0156] Distribution morphology: The electric field distribution presents a specific morphology related to the light propagation path. At the interface where light enters the photoresist, the electric field intensity may change suddenly due to the change in refractive index.

[0157] Electric field strength value: Analyze the numerical value of the electric field strength. Combine the initialization of the electric field and the parameters in the update equation (such as the dielectric constant epsilon, time step dt, spatial step dx, etc.) to determine whether the range of the electric field strength is reasonable. In actual lithography, the electric field strength affects processes such as the polarization of photoresist molecules. Specific lithography processes may have certain required ranges for the electric field strength, and it is necessary to confirm whether the simulation results meet the expectations.

[0158] Relationship with light intensity distribution: The electric field strength is closely related to the light intensity, and the light intensity is proportional to the square of the electric field strength. By observing the simulation diagrams of the electric field distribution and the light intensity distribution, the corresponding relationship between the two should be found. For example, in areas with stronger light intensity, the electric field strength is usually also larger, and the change trends should be similar. If the relationship between the two does not match, it may be due to errors in the calculation process or inaccurate understanding of the physical relationship when simulating the interaction between light and matter.

[0159] It should be noted that in this article, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device comprising a series of elements not only includes those elements, but also includes other elements not expressly listed, or also includes elements inherent to such process, method, article or device. Without further limitation, an element defined by the statement "comprising a reference structure" does not exclude the existence of additional identical elements in the process, method, article or device comprising the element.

[0160] Although the embodiments of the present invention have been shown and described, for those of ordinary skill in the art, it can be understood that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A compact lithography simulation system based on a physical model, characterized in that: It includes a model construction module, a kernel visualization module, a simulation calculation module, a result evaluation and optimization module, a model library management module, a real-time simulation module, an error compensation module, and a simulation result verification module; The model construction module constructs a three-dimensional lithography model based on the light source wavelength, coherence factor, numerical aperture of the projection objective, aberration coefficient in the lithography equipment, and the type of photoresist, lithography resolution requirement, and lithography pattern feature size in the lithography process; The kernel visualization module uses three-dimensional visualization technology to display the model kernel; The simulation calculation module uses physical simulation algorithms to simulate and calculate the light propagation in the lithography process and the chemical reactions in the photoresist; The result evaluation and optimization module evaluates the linewidth uniformity, edge roughness, and overlay accuracy of the lithography pattern according to the simulation results, and optimizes the exposure dose and focus depth of the lithography process; The model library management module classifies, manages, and stores the lithography models of step-and-scan lithography process, scanning projection lithography process, and ArF lithography machines, KrF lithography machines, facilitating quick user calls and updates; The real-time simulation module has a real-time simulation function, can be connected to the actual lithography equipment, and can obtain the exposure time, exposure intensity of the equipment, and the temperature and humidity of the environment in real time for simulation, and timely feedback the simulation results; The error compensation module introduces an error compensation algorithm to analyze and compensate the errors in the simulation results, improving the accuracy of the simulation; The simulation result verification module verifies the accuracy and reliability of the simulation model by comparing with the actual size measurement data of the lithography pattern and the actual dissolution rate data of the photoresist in the actual lithography experiment, and continuously optimizes the simulation model.

2. The compact lithography simulation system based on a physical model according to claim 1, wherein: In the multimodal information integration module, a modeling method based on wave optics theory is adopted, considering the diffraction of light in the projection objective of the lithography equipment and the interference of light on the surface and inside of the photoresist, to construct an accurate lithography model. At the same time, combined with quantum mechanics theory, the physical and chemical processes at the microscopic level of the electron transition and chemical bond breakage of photoresist molecules during the lithography process are accurately simulated. And this module introduces machine learning algorithms, using the light source parameters, photoresist characteristic parameters, lithography pattern size deviation data of historical lithography experiments, and the corresponding simulation results to adaptively adjust the model, improving the adaptability of the model to different photoresist types and different lithography pattern complexity lithography scenarios.

3. The compact lithography simulation system based on a physical model according to claim 2, wherein: In the kernel visualization module, virtual reality or augmented reality technology is used, enabling users to immerse themselves in observing the light propagation path in the photoresist and the dynamic chemical reactions of photoresist molecules in the model kernel by wearing a VR helmet or using an AR device, intuitively understanding the lithography process. It supports multiple people to enter the virtual or augmented reality environment simultaneously through network connection to mark, discuss, and collaboratively analyze the model kernel. In addition, this module has a dynamic annotation function, real-time annotating the numerical values, units, and change trends of key physical quantities such as the light intensity distribution and photoresist reaction rate in the model kernel, facilitating user analysis.

4. The compact lithography simulation system based on a physical model according to claim 3, wherein: In the simulation calculation module, the physical simulation algorithm adopts the finite element time domain method and the fast Fourier transform method. The finite element time domain method divides the lithography area into three-dimensional grids, and iteratively solves the propagation of light in the time domain through the Maxwell equations; the fast Fourier transform method uses frequency domain transformation to calculate the diffraction and interference of light, thereby improving the efficiency and accuracy of the simulation calculation. At the same time, parallel computing technology is introduced to distribute the simulation task to multiple computing cores or computing nodes for simultaneous calculation, thereby accelerating the simulation process. The module adopts adaptive grid division technology to automatically adjust the grid density according to the intensity of light and the intensity of changes in the physical quantities of the photoresist reaction rate during the lithography process. Fine grids are used in areas where the physical quantities change dramatically, and coarse grids are used in areas where the changes are gentle, thereby ensuring the simulation accuracy and reducing the amount of calculation.

5. The compact lithography simulation system based on a physical model according to claim 4, wherein: In the result evaluation and optimization module, a non-dominated sorting genetic algorithm multi-objective optimization algorithm is used to simultaneously optimize the exposure dose, focus depth, and photoresist development time parameters of the lithography process, improve the resolution, line width uniformity, and edge roughness indicators of the lithography pattern, and combine the sensitivity analysis method to observe the change range of the lithography pattern line width and overlay accuracy indicators by changing the value of a single parameter, and determine the influence of key parameters on the lithography effect. In addition, this module introduces a deep learning model, which is trained through a large amount of lithography effect simulation data under different process parameter combinations and lithography pattern measurement data of actual lithography experiments. The lithography process parameters are used as input and the predicted lithography effect indicators are used as output. The lithography effects under different process parameter combinations are quickly predicted to provide guidance for the optimization process.

6. The compact lithography simulation system based on a physical model according to claim 5, wherein: The model library management module classifies, manages and stores the lithography models of the stepping projection lithography process, scanning projection lithography process, ArF lithography machine and KrF lithography machine, and has the function of automatic model update. When the system detects changes in the photoresist formula of the lithography process, adjustments to the exposure mode, and fine-tuning of the equipment's lithography machine light source wavelength and changes in the projection object aberration correction parameters, it automatically obtains the latest model template of the corresponding changed parameters from the cloud, and adjusts and updates the model according to local historical data and actual conditions to ensure the timeliness and accuracy of the model.

7. The compact lithography simulation system based on a physical model according to claim 6, characterized in that: The real-time simulation module is connected to the actual lithography equipment, obtains the exposure time, exposure intensity, environmental temperature and humidity of the equipment in real time for simulation and timely feedback of the results, and at the same time performs intelligent early warning on the actual lithography equipment based on the real-time simulation results. When the simulation results show that the line width deviation and edge roughness indicators of the lithography graphics exceed the allowable range, or the reaction degree of the photoresist does not meet the requirements, an alarm is issued to the equipment operator through a sound alarm and a pop-up prompt, and suggestions for adjusting the exposure dose and focus depth parameters are provided.

8. The compact lithography simulation system based on a physical model according to claim 7, wherein: The error compensation algorithm introduced by the error compensation module analyzes and compensates for the light intensity calculation error and photoresist reaction rate calculation error in the simulation results caused by model simplification and numerical calculation methods, establishes an error prediction model, and uses machine learning and statistical analysis methods to predict the types and magnitudes of systematic errors and random errors under different combinations of lithography process parameters and equipment conditions. During the simulation process, relevant parameters are adjusted according to the prediction results to control errors.

9. The compact lithography simulation system based on a physical model according to claim 8, characterized in that: The simulation result verification module verifies the accuracy and reliability of the simulation model by comparing with the actual size measurement data of the lithography pattern and the actual dissolution rate data of the photoresist in the actual lithography experiment. It uses statistical analysis methods to process a large amount of verification data, establishes a model accuracy evaluation index system including average error rate, root mean square error, and correlation coefficient indicators, comprehensively and objectively evaluates the performance of the simulation model under different lithography process parameters and different lithography pattern characteristics, and optimizes the material refractive index of the wave optics model and the reaction constant model parameters of the quantum mechanics model according to the evaluation results.

10. The compact lithography simulation system based on a physical model according to claim 9, wherein: The model construction module inputs lithography equipment parameters and process requirements and outputs a three-dimensional lithography model. The kernel visualization module inputs model kernel data and outputs a visualization display interface. Users can select and combine modules according to their needs, add special simulation module functions for new photoresists to adapt to changes in lithography simulation requirements. It has a data security management mechanism, encrypts and stores the enterprise-specific lithography process parameters, experimental data, sensitive data, and model data during the simulation process using the AES symmetric encryption algorithm and encrypts the transmission through the SSL / TLS network protocol. It sets access permissions for different roles of ordinary users and administrators. Ordinary users can perform simulation operations and view results, while administrators can perform system settings and data management operations to ensure data security and privacy. It conducts data interaction and integration with lithography-related software such as lithography process design software and lithography equipment control software through XML and JSON standardized data interfaces.

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