Compact lithography simulation system based on physical model

Abstract

The application provides a compact photolithography simulation system based on a physical model, relates to the fields of photolithography simulation tools and physical modeling technology, 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 construction module constructs a three-dimensional photolithography model according to the light source wavelength, the coherence factor, the numerical aperture of a projection objective lens, the aberration coefficient of a photolithography machine in a photolithography device, and the photoresist type, the photolithography resolution requirement and the photolithographic feature size of a photolithography process; the model construction module in the application is based on wave optics theory and quantum mechanics theory, comprehensively considers the diffraction of light in the projection objective lens, the interference of light on the surface and inside of the photoresist, and the physical and chemical processes of the photoresist molecules at the microscopic level, and can accurately simulate the complex propagation path of light in the photolithography device compared with a basic physical model.

Description

Technical Field

[0001] This invention relates to the field of photolithography simulation tools and physical modeling technology, specifically a compact photolithography simulation system based on a physical model. Background Technology

[0002] As the semiconductor industry continues to evolve towards smaller process sizes and higher integration, photolithography, as a crucial step in the chip manufacturing process, directly impacts chip performance, yield, and production efficiency with its precision and stability. Even the slightest deviation in any photolithography step can damage chip performance or even render it unusable, leading to significant economic and time costs. Therefore, accurately simulating the photolithography process and deeply optimizing photolithography techniques have become core challenges that urgently need to be overcome in the semiconductor manufacturing field.

[0003] Currently, the following techniques are mainly used in photolithography simulation and process optimization:

[0004] Basic physics model simulation: Early lithography simulations often relied on models that only covered some of the basic physics principles.

[0005] Single-algorithm simulation: Some studies use a single simulation algorithm, employing the finite element time-domain method to simulate light propagation.

[0006] General-purpose computing devices are still used in lithography simulations: many companies still rely on general-purpose computing devices, such as ordinary office computers or simple servers. These devices lack the computing power and memory capacity to handle complex lithography simulation algorithms, making it difficult to meet the demands of large-scale data processing.

[0007] Traditional model management method: In the past, the management of lithography models was mostly done using a simple file storage method, which mixed lithography models from different processes and equipment.

[0008] Despite the progress made in lithography simulation and process optimization, existing technologies still have many drawbacks:

[0009] Problem 1: Due to oversimplification, the basic physical model cannot accurately simulate the complex propagation path of light in the lithography equipment and the physicochemical processes at the microscopic level within the photoresist. This results in low simulation accuracy, which fails to provide strong support for the development of advanced process technologies and seriously hinders the advancement of chip manufacturing towards smaller sizes and higher precision.

[0010] The second problem is that single-algorithm simulation cannot fully leverage the advantages of different algorithms, making it difficult to ensure both computational efficiency and simulation accuracy. In particular, when dealing with multi-parameter and complex lithography scenarios, it is impossible to achieve comprehensive and efficient simulation and optimization, and cannot meet the increasingly complex lithography process requirements.

[0011] Thirdly, general-purpose computing devices lack the hardware performance to meet the complex computational requirements of lithography simulation, resulting in lengthy simulation processes that severely impact the cycle and efficiency of process development. Furthermore, insufficient hardware performance prevents some high-precision simulation algorithms from running effectively, further reducing the accuracy of simulation results.

[0012] Question 4: Traditional model management methods lack systematicity and intelligence, resulting in chaotic model storage and untimely updates. This makes it difficult to quickly call up suitable models during the adjustment and optimization of lithography processes, and also makes it impossible to update models in a timely manner according to changes in processes and equipment, thus hindering the coordinated development of lithography simulation and process optimization.

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

[0014] Technical problems to be solved

[0015] To address the shortcomings of existing technologies, this invention provides a compact photolithography simulation system based on a physical model, which solves the problems mentioned in the background.

[0016] Technical solution

[0017] To achieve the above objectives, the present invention provides the following technical solution: a compact lithography simulation system based on a physical model, comprising a model building 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 building module constructs a three-dimensional lithography model based on the wavelength of the lithography machine's light source, coherence factor, numerical aperture of the projection lens, aberration coefficient, as well as the type of photoresist, lithography resolution requirements, and lithography pattern feature size of the lithography process.

[0019] The kernel visualization module uses 3D 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 within the photoresist during the photolithography process.

[0021] The result evaluation and optimization module evaluates the linewidth uniformity, edge roughness, and overlay accuracy of the lithographic 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, manages, and stores lithography models for stepper projection lithography, scanning projection lithography, ArF lithography machines, and KrF lithography machines, making it convenient for users to quickly access and update them.

[0023] The real-time simulation module has a real-time simulation function, which can be connected to the actual lithography equipment to obtain the exposure time, exposure intensity, ambient temperature and humidity of the equipment in real time and perform simulation, and provide timely feedback on the simulation results;

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

[0025] 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 lithographic pattern and the actual dissolution rate data of the photoresist in the actual lithography experiment, and continuously optimizes the simulation model.

[0026] Preferably, the multimodal information integration module employs a modeling method based on wave optics theory, considering the diffraction of light in the projection lens of the lithography equipment and the interference of light on the surface and inside the photoresist to construct an accurate lithography model. Simultaneously, it combines quantum mechanics theory to accurately simulate the physical and chemical processes at the microscopic level of electronic transitions and chemical bond breaking of photoresist molecules during the lithography process. Furthermore, this module introduces machine learning algorithms to adaptively adjust the model using the light source parameters, photoresist characteristic parameters, lithography pattern size deviation data, and corresponding simulation results from historical lithography experiments, thereby improving the model's adaptability to lithography scenarios with different photoresist types and different lithography pattern complexities.

[0027] Preferably, the kernel visualization module utilizes virtual reality or augmented reality technology, allowing users to immerse themselves in observing the propagation path of light within the photoresist and the dynamic chemical reactions of photoresist molecules by wearing VR headsets or using AR devices. This provides an intuitive understanding of the photolithography process. The module supports multiple users simultaneously entering the virtual or augmented reality environment via network connection to mark, discuss, and collaboratively analyze the model kernel. Furthermore, the module features dynamic annotation capabilities, providing real-time annotation of the light intensity distribution, photoresist reaction rate, and key physical quantities, including their values, units, and trends, to facilitate 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 a three-dimensional mesh and solves the propagation of light in the time domain iteratively using 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 task to multiple computing cores or computing nodes for simultaneous calculation, accelerating the simulation process. This module adopts adaptive mesh generation technology, which automatically adjusts the mesh density according to the intensity of changes in physical quantities such as light intensity and photoresist reaction rate during the lithography process. Fine mesh is used in areas with drastic changes in physical quantities, and coarse mesh is used in areas with gradual changes, ensuring simulation accuracy and reducing the amount of computation.

[0029] Preferably, the result evaluation and optimization module employs a non-dominated sorting genetic algorithm multi-objective optimization algorithm to simultaneously optimize the exposure dose, focus depth, and photoresist development time parameters of the lithography process, improving the resolution, linewidth uniformity, and edge roughness of the lithography pattern. Combined with sensitivity analysis, by changing the value of a single parameter, the module observes the changes in the linewidth and overlay accuracy of the lithography pattern to determine the degree of influence of key parameters on the lithography effect. Furthermore, this module introduces a deep learning model, trained with a large amount of simulation data of lithography effects under different combinations of process parameters and measurement data of lithography patterns from actual lithography experiments. Using lithography process parameters as input and predicted lithography effect indicators as output, it quickly predicts the lithography effect under different combinations of process parameters, providing guidance for the optimization process.

[0030] Preferably, the model library management module classifies, manages, and stores lithography models for stepper projection lithography, scanning projection lithography, ArF lithography, and KrF lithography. It has an automatic model update function. When the system detects changes in the photoresist formulation, exposure mode adjustment, or changes in the wavelength of the lithography machine's light source or the image aberration correction parameters, it automatically retrieves the latest model template with the corresponding changed parameters from the cloud and adjusts and updates the model based on local historical data and actual conditions to ensure the timeliness and accuracy of the model.

[0031] Preferably, the real-time simulation module is connected to the actual lithography equipment, and acquires the exposure time, exposure intensity, ambient temperature, and humidity of the equipment in real time for simulation and timely feedback of the results. At the same time, it provides intelligent early warning to the actual lithography equipment based on the real-time simulation results. When the simulation results show that the linewidth deviation or edge roughness index of the lithography pattern exceeds 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 focus depth parameters.

[0032] Preferably, the error compensation module introduces an error compensation algorithm to analyze and compensate for the light intensity calculation error and photoresist reaction rate calculation error caused by model simplification and numerical calculation methods in the simulation results. It establishes an error prediction model and uses machine learning and statistical analysis methods to predict the systematic error, random error type and magnitude under different combinations of photolithography process parameters and equipment conditions. During the simulation process, it adjusts relevant parameters according to the prediction results to perform 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 lithographic 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 and establishes a model accuracy evaluation index system including average error rate, root mean square error, and correlation coefficient. It comprehensively and objectively evaluates the performance of the simulation model under different lithography process parameters and different lithography pattern characteristics. Based on the evaluation results, it optimizes the material refractive index of the wave optics model and the reaction constant model parameters of the quantum mechanical model.

[0034] Preferably, the model building module takes into account lithography equipment parameters and process requirements, and outputs a 3D lithography model; the kernel visualization module takes into account model kernel data and outputs a visualization display interface. Users can select and combine modules according to their needs, and add special simulation modules for new photoresists to adapt to changes in lithography simulation requirements. It has a data security management mechanism, which uses AES symmetric encryption algorithm to encrypt and store enterprise-specific lithography process parameters, experimental data, sensitive data, and model data during the simulation process, and uses SSL / TLS network protocol to encrypt and transmit them. It sets access permissions for different roles such as ordinary users and administrators. Ordinary users perform simulation operations and view results, while administrators perform system settings and data management operations to ensure data security and privacy. It interacts and integrates with lithography process design software, lithography equipment control software, and lithography-related software through standardized data interfaces such as XML and JSON.

[0035] Beneficial effects

[0036] This invention provides a compact photolithography simulation system based on a physical model. It offers the following advantages:

[0037] 1. The model building module in this invention is based on wave optics and quantum mechanics, comprehensively considering light diffraction in the projection lens, light interference on and within the photoresist surface, and the physicochemical processes at the microscopic level of photoresist molecules. Compared to fundamental physical models, it can accurately simulate the complex propagation path of light in photolithography equipment, greatly improving simulation accuracy and providing a solid basis for the development of advanced process technologies. This powerfully promotes the development of chip manufacturing towards smaller sizes and higher precision. The simulation calculation module comprehensively utilizes the finite element time-domain method and the fast Fourier transform method, and introduces parallel computing technology and adaptive mesh generation technology. This not only fully leverages the advantages of different algorithms, ensuring both computational efficiency and simulation accuracy, but also automatically adjusts the mesh density according to changes in physical quantities during the photolithography process. It comprehensively and efficiently simulates complex processes such as light propagation and chemical reactions within the photoresist during photolithography, meeting the increasingly complex demands of photolithography processes.

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

[0039] 3. In this invention, the error compensation module introduces an error compensation algorithm to analyze and compensate for errors in the simulation results caused by model simplification and numerical calculation methods. An error prediction model is established, and relevant parameters are adjusted based on the prediction results to improve simulation accuracy. The simulation result verification module compares the simulation results with actual lithography experimental data, uses statistical analysis methods to comprehensively and objectively evaluate the performance of the simulation model, optimizes model parameters based on the evaluation results, continuously improves the simulation model, and enhances the overall reliability of the system. Attached Figure Description

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

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

[0042] Figure 3 This is a simulation diagram of the photolithography intensity distribution of the present invention;

[0043] Figure 4 This is a simulation diagram of the electric field distribution of the present invention. Detailed Implementation

[0044] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention. Specific Implementation Example 1:

[0046] like Figure 1-4 As shown, the overall working principle of the compact photolithography simulation system based on the physical model is as follows:

[0047] The system begins with a model building module, which receives parameters of the lithography equipment (light source wavelength, coherence factor, numerical aperture of the projection lens, aberration coefficient) and lithography process requirements (photoresist type, lithography resolution requirements, and lithographic pattern feature size). It utilizes wave optics theory to consider the diffraction of light in the projection lens and its interference on and within the photoresist surface. Quantum mechanics theory is combined to simulate the electronic transitions and chemical bond breaking of photoresist molecules. Simultaneously, machine learning algorithms are used to adaptively adjust the model based on historical data, constructing a three-dimensional lithography model. This model serves as the foundational data, supporting subsequent modules.

[0048] The simulation module acquires a three-dimensional photolithography model and uses the finite element method (FEM) to divide the photolithography area into a three-dimensional mesh. It iteratively solves for light propagation in the time domain using Maxwell's equations, or calculates light diffraction and interference in the frequency domain using the fast Fourier transform method. Parallel computing technology is introduced to accelerate the calculation. An adaptive mesh generation technique is used to adjust the mesh density according to changes in physical quantities such as light intensity and photoresist reaction rate. This simulates the light propagation and chemical reactions within the photoresist during the photolithography process, outputting simulation results.

[0049] The kernel visualization module obtains 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 headset or using AR devices. This includes things like the propagation path of light in the photoresist and the dynamic chemical reaction of photoresist molecules. It supports multi-user collaboration and has dynamic annotation capabilities, generating a visual display interface that allows users to intuitively understand the photolithography process and assists them in analyzing the model.

[0050] The results evaluation and optimization module receives the results from the simulation calculation module and uses a non-dominated sorting genetic algorithm to simultaneously optimize lithography process parameters such as exposure dose, focus depth, and photoresist development time. Combined with sensitivity analysis, it determines the degree of influence of key parameters on indicators such as linewidth uniformity, edge roughness, and overlay accuracy of the lithography pattern. Through deep learning models trained with a large amount of simulation and experimental data, it quickly predicts the lithography effect under different combinations of process parameters, providing guidance for optimizing the lithography process, and finally outputs the optimized process parameters.

[0051] The model library management module categorizes, manages, and stores different types of lithography models, including stepper projection lithography, scanning projection lithography, and ArF and KrF lithography machines. When the system detects changes in lithography process parameters (changes in photoresist formulation, adjustments in exposure modes) or equipment parameters (fine-tuning of lithography machine light source wavelength, correction of image aberration of projected objects), it automatically retrieves the latest model template from the cloud and updates the model by combining it with local data. This ensures the timeliness and accuracy of the models, allowing users to quickly access and update them, and providing model support for the model building module.

[0052] The real-time simulation module connects to the actual lithography equipment, acquiring parameters such as exposure time, exposure intensity, and ambient temperature and humidity in real time. It performs real-time simulations and provides timely feedback. If the simulation results show that the linewidth deviation of the lithographic pattern, edge roughness, or photoresist reaction does not meet requirements, it issues an alarm to the equipment operator via audible alarm and pop-up prompts, providing suggestions for adjusting parameters such as exposure dose and focus depth. Simultaneously, it feeds real-time data 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, analyzing and compensating for errors in light intensity calculation and photoresist reaction rate calculation caused by model simplification and numerical calculation methods. It establishes an error prediction model using machine learning and statistical analysis methods to predict the type and magnitude of errors under different combinations of lithography process parameters and equipment conditions. During the simulation, relevant parameters are adjusted based on the prediction results to improve simulation accuracy. The compensated results are then 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 lithographic pattern and the actual dissolution rate data of the photoresist in actual photolithography experiments. It uses statistical analysis methods to process a large amount of verification data and establishes a model accuracy evaluation index system that includes indicators such as average error rate, root mean square error, and correlation coefficient. This comprehensively and objectively evaluates the performance of the simulation model under different lithography process parameters and different lithographic pattern characteristics. Based on the evaluation results, the model parameters, such as the material refractive index of the wave optics model and the reaction constant of the quantum mechanics model, are optimized. The optimized model is then fed back to the model building module and other related modules to continuously improve the entire simulation system.

[0055] The system's modules are closely interconnected, forming a closed-loop workflow through data input and output. It also has a data security management mechanism, employing AES symmetric encryption algorithm for encrypted storage and SSL / TLS network protocol for encrypted transmission of sensitive data. Different role access permissions are set to ensure data security. Furthermore, it interacts and integrates with related software such as lithography process design software and lithography equipment control software through standardized XML and JSON data interfaces to adapt to different lithography simulation needs. Specific Implementation Example 2:

[0057] like Figure 1-4 As shown, the key algorithm mentioned in Example 1 will be analyzed in detail below, including its core mathematical formulas and explanations:

[0058] Model building module:

[0059] Wave optics theoretical modeling - Maxwell's equations:

[0060] The differential form of Maxwell's equations:

[0061]

[0062] in It is the curl operator, used to describe the rotational properties of a vector field. In photolithography simulations, it helps us analyze the propagation direction and mode changes of light. It is a divergence operator used to measure the degree of divergence or convergence of a vector field at a point. In light propagation, it can be used to analyze the sources and sinks of electric and magnetic fields. E is the electric field strength, measured in volts per meter (V / m). In photolithography, the electric field strength determines the strength of the interaction between light and photoresist molecules, affecting the exposure and reaction process of the photoresist. B is the magnetic flux density, measured in tesla (T). As an electromagnetic wave, light's magnetic field component is interrelated with its electric field component, jointly influencing light propagation and the photolithography process. H is the magnetic field strength, measured in 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, measured in coulombs per square meter (C / m). 2 The polarization effect of the medium is considered, which is important for simulating light propagation in photolithography for different photoresists and other media. J is the current density. Although the current effect is usually relatively small in photolithography, it still needs to be considered in some special cases (such as those involving charge transfer). ρ is the charge density, which describes the distribution of charge in space. It is the partial derivative with respect to time, used to describe the rate of change of a physical quantity with time, and in photolithography simulation, it reflects the dynamic characteristics of light propagation and the photolithography process.

[0063] Maxwell's equations describe the fundamental properties of electric and magnetic fields and their interrelationships, forming the basis of wave optics theory. In photolithography modeling, solving these equations allows for the simulation of light propagation within photolithography equipment, including diffraction and interference phenomena.

[0064] Accurate simulation of light propagation in the projection lens, taking into account light diffraction and interference effects, allows for the construction of a more accurate lithography model and improves the simulation accuracy of lithography patterns.

[0065] Method of use: In the Finite Element Time Domain (FDTD) method, the lithographic region is divided into a three-dimensional mesh, and Maxwell's equations are discretized at each mesh point. The propagation of light in the time domain is simulated by iteratively solving these discrete equations.

[0066] Quantum mechanical theoretical modeling - Schrödinger equation:

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

[0068] in ψ is the Hamiltonian operator, representing the total energy of the system, including kinetic and potential energy. In the quantum simulation of photoresist molecules, it includes the kinetic energy of electrons and the potential energy of interactions between electrons and atomic nuclei, and between electrons themselves. ψ is the wave function, which describes the quantum state of a microscopic particle. The square of the modulus of the wave function is |ψ|. 2 The wave function represents the probability density of a particle appearing at a certain point in space. In photolithography, the wave function can be used to analyze the distribution and state of electrons in photoresist molecules. E is the energy of the particle, measured in joules (j) or electron volts (eV). For electrons in photoresist molecules, their energy state determines the stability of chemical bonds and the probability of chemical reactions.

[0069] The Schrödinger equation describes the evolution of the quantum state of microscopic particles over time. In photolithography, it is used to simulate microscopic processes such as electron transitions and chemical bond breaking within photoresist molecules.

[0070] More accurate simulations of the physical and chemical processes at the microscopic level during photolithography can help us understand the reaction mechanism of photoresist and improve the accuracy of photolithography models at the microscopic scale.

[0071] By determining the potential energy function of photoresist molecules, a Hamiltonian operator is constructed, and then the Schrödinger equation is solved to obtain the wave function and energy eigenvalues, thereby analyzing the state of electrons and the possibility of chemical reactions.

[0072] Simulation calculation module:

[0073] Finite Element 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 H represents the component of the electric field in the x-direction, with units of volts per meter (V / m). In photolithography simulations, it describes the distribution and variation of the photoelectric field in the x-direction. zThe magnetic field component in the z-direction is represented by amperes per meter (A / m), describing the distribution and variation of the optical magnetic field in the z-direction. `n` represents the time step, an integer used to mark the simulation's time progression. As `n` increases, the simulation time advances. `i` and `j` represent the coordinates of spatial grid points, used to determine their position in the 2D Yee grid. `Δt` is the time step size in seconds (s), determining the temporal interval of the simulation. A smaller `Δt` can improve simulation accuracy but increase computation time. `ε` is the dielectric constant in farads per meter (F / m), reflecting the polarization characteristics of the medium under an electric field. Different photoresists and optical media have different dielectric constants. `μ` is the permeability in henleys per meter (H / m), describing the magnetic properties of the medium under a magnetic field. `Δy` is the spatial step size in meters (m), determining the grid spacing of the Yee grid in the y-direction. A smaller `Δy` can improve spatial resolution but increase computational cost.

[0078] The FDTD method simulates the propagation of light in space by iteratively updating the values ​​of 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 the numerical calculation.

[0079] It can efficiently and accurately simulate the propagation of light in the photolithography area, taking into account complex phenomena such as light diffraction and interference, and provides a foundation for the simulation of the photolithography process.

[0080] Usage: First, divide the lithographic region into a three-dimensional mesh, initialize the values ​​of electric and magnetic fields, and then update the electric and magnetic fields sequentially at each time step according to the above update equation until the required simulation time is reached.

[0081] Fast Fourier Transform (FFT) method:

[0082] Discrete Fourier Transform (DFT):

[0083] Where X[k] is the discrete Fourier transform result of the discrete signal x[n], a complex sequence representing the frequency domain component of the signal; x[n] is the time-domain discrete signal, which in photolithography simulation can be the sampled value of the light field in the time domain; n is the index of the time-domain discrete signal, ranging from 0 to N-1; k is the index of the frequency-domain discrete signal, also ranging from 0 to N-1; N is the length of the signal, i.e., the number of sampling points; and j is the imaginary unit. e is a natural constant, approximately 2.71828.

[0084] The FFT method transforms the signal from the time domain to the frequency domain, utilizing the characteristics of the frequency domain to quickly calculate the diffraction and interference of light. In photolithography simulations, it can be used to handle problems related to light propagation and imaging.

[0085] The problem to be solved: to improve the efficiency of light propagation and imaging calculations and reduce computation time, especially for large-scale photolithography simulation problems.

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

[0087] Results Evaluation and Optimization Module:

[0088] Non-dominated sorting genetic algorithm (NSGA-11):

[0089] Non-dominated ranking: For two individuals p and q, if individual p is not inferior to individual q on all objective functions, and is superior to individual q on at least one objective function, then individual p is said to dominate individual q. Non-dominated ranking is used to divide individuals in a population into different ranks.

[0090] Crowded distance calculation:

[0091]

[0092] Where d i is the crowding distance of individual i, used to measure the sparsity of an individual in the population. A larger crowding distance indicates that the individuals around that individual are more sparse. M is the number of objective functions, which in photolithography optimization may include multiple objectives such as the resolution of the photolithographic pattern, linewidth uniformity, and edge roughness. It is the value of individual i on the m-th objective function. For example, when m=1 represents the resolution objective function, That is, the resolution index value corresponding to individual i. and These are the maximum and minimum values ​​of the m-th objective function in the current population, respectively, used to normalize the objective function values.

[0093] NSGA-II is a multi-objective optimization algorithm that selects superior individuals through non-dominated sorting and crowding distance calculation, gradually evolving 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, photoresist development time, etc.) to improve lithography results (such as resolution of lithographic patterns, linewidth uniformity, and edge roughness).

[0095] Usage: First, initialize a population, with each individual representing a set of lithography process parameters. Perform non-dominated sorting and crowding distance calculations on the individuals in the population, select superior individuals for crossover and mutation operations, and generate a new population. Repeat this process until a termination condition is met (e.g., reaching the maximum number of iterations), ultimately obtaining an optimal combination of process parameters.

[0096] Sensitivity analysis methods:

[0097]

[0098] Where S is sensitivity, which measures the degree of influence of the change in parameter x on the dependent variable y; Δx is the amount of change in parameter x, such as the change in exposure dose, where x is the original value of the parameter, such as the initial exposure dose; Δy is the amount of change in dependent variable y (such as the linewidth of the lithographic pattern, overlay accuracy, etc.), where y is the original value of the dependent variable, such as the initial linewidth of the lithographic pattern.

[0099] Sensitivity analysis is used to measure the degree of influence of a change in a parameter on the dependent variable. By calculating sensitivity, it is possible to determine which parameters have a greater impact on the lithography effect, i.e., the critical parameters.

[0100] Problem to be solved: Determine the impact of key parameters on photolithography results, providing a basis for optimizing process parameters.

[0101] Instructions for use: Keep other parameters fixed, change the value of one parameter, record the change in the dependent variable, and then calculate the sensitivity using the formula above. Repeat this process for all parameters of interest to obtain the sensitivity value for each parameter. Compare the sensitivity values ​​to determine the key parameters. Specific Implementation Example 3:

[0103] like Figure 1-4 As shown below, the specific application logic steps of each module and algorithm in the compact photolithography simulation system based on the physical model are explained:

[0104] 1. Model building module:

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

[0106] Wave optics modeling: Based on wave optics theory, considering the diffraction of light in the projection lens and the interference of light on and inside the photoresist surface, a light propagation model is constructed using Maxwell's equations.

[0107] Quantum mechanical modeling: Combining quantum mechanical theory, this study constructs physical and chemical process models at the microscopic level for microscopic processes such as electronic transitions and chemical bond breaking in photoresist molecules using theories such as the Schrödinger equation.

[0108] Machine learning optimization: Utilizing historical lithography experimental data on light source parameters, photoresist characteristic parameters, lithographic pattern size deviations, and corresponding simulation results, machine learning algorithms are employed to adaptively adjust the constructed model, thereby improving its adaptability to lithography scenarios with different photoresist types and varying lithographic pattern complexities. The final output is a 3D lithography model.

[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 dynamics of chemical reactions of photoresist molecules.

[0111] Technology Application: Using virtual reality (VR) or augmented reality (AR) technologies, model kernel data is transformed into a visualized scene. Users enter this scene by wearing VR headsets or using AR devices, achieving immersive observation.

[0112] Interactive functionality: Supports multiple users simultaneously entering virtual or augmented reality environments via network connection to annotate, discuss, and collaboratively analyze the model kernel. Furthermore, utilizing dynamic annotation, it provides real-time annotation of key physical quantities such as light intensity distribution and photoresist reaction rate within the model kernel, including their values, units, and trends, facilitating user analysis and understanding. The final output is a visual display interface.

[0113] 3. Simulation Calculation Module:

[0114] Model receiving: Receives the 3D lithography model generated by the model building module.

[0115] Algorithm selection and execution:

[0116] Finite Element Time Domain (FDTD) method: The lithographic region is divided into a three-dimensional mesh, Maxwell's equations are discretized at each mesh point, and the propagation process of light is simulated by iteratively solving these discrete equations in the time domain.

[0117] Fast Fourier Transform (FFT) method: Transforms the time-domain distribution of the light field to the frequency domain, and uses the frequency domain transformation to quickly calculate the diffraction and interference of light.

[0118] Efficiency improvement methods: Parallel computing technology is introduced, distributing simulation tasks across multiple computing cores or nodes for simultaneous computation to accelerate the simulation process. Simultaneously, adaptive mesh generation technology is employed, automatically adjusting the mesh density based on the drastic changes in physical quantities such as light intensity and photoresist reaction rate during the lithography process. A finer mesh is used in regions of rapid physical quantity changes, while a coarser mesh is used in regions of gradual change, reducing computational load while maintaining simulation accuracy. The final output is the simulation results of the lithography process.

[0119] 4. Results Evaluation and Optimization Module:

[0120] Result Input: Receives the result data from the simulation calculation module, including the simulation results of the lithography pattern, etc.

[0121] Multi-objective optimization: The non-dominated sorting genetic algorithm (NSGA-II) is used to optimize parameters such as exposure dose, focus depth, and photoresist development time in the lithography process to improve the resolution, linewidth uniformity, and edge roughness of the lithographic pattern.

[0122] Sensitivity analysis: By changing the value of a single parameter, the changes in indicators such as the linewidth of the lithographic pattern and the overlay accuracy are observed to determine the degree of influence of key parameters on the lithography effect.

[0123] Deep Learning Prediction: A deep learning model is introduced, trained using a large amount of simulation data of lithography effects under different combinations of process parameters and measurement data of lithography patterns from actual lithography experiments. Taking lithography process parameters as input and predicted lithography effect indicators as output, the model quickly predicts the lithography effect under different combinations of process parameters, providing guidance for the optimization process. The final output includes 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 projection lithography, scanning projection lithography, ArF lithography machine, and KrF lithography machine, and classify them according to process and equipment type.

[0126] Storage and Updates: Classified models are stored locally, with automatic model updates. When the system detects changes in parameters such as photoresist formulation changes, exposure mode adjustments, lithography machine light source wavelength fine-tuning, or projection image aberration correction, it automatically retrieves the latest model template with the corresponding changed parameters from the cloud and adjusts and updates the model based on local historical data and actual conditions, ensuring the model's timeliness and accuracy. This allows users to quickly access and update models when needed, such as in the model building module.

[0127] 6. Real-time simulation module:

[0128] Equipment connection and data acquisition: Connect to the actual lithography equipment to acquire parameters such as exposure time, exposure intensity, ambient temperature, and humidity in real time.

[0129] Real-time simulation: Real-time simulation calculations are performed using the acquired real-time parameters and the algorithms of the simulation calculation module.

[0130] Results Feedback and Early Warning: Simulation results are fed back promptly. If the simulation results show that the linewidth deviation or edge roughness of the lithographic pattern exceeds the allowable range, or the photoresist reactivity does not meet requirements, an alarm is issued to the equipment operator via sound and pop-up notifications, along with suggestions for adjusting parameters such as exposure dose and focus depth. Simultaneously, 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 identify errors in light intensity calculation and photoresist reaction rate calculation caused by model simplification, numerical calculation methods, etc.

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

[0134] Error compensation and control: During the simulation, relevant parameters are adjusted based on the results of the error prediction model to compensate for and control the error, thereby improving simulation accuracy. The compensated results are then fed back to the result evaluation and optimization module and other relevant modules.

[0135] 8. Simulation Result Verification Module:

[0136] Data Comparison: The simulation results are compared with the actual size measurement data of the lithographic pattern and the actual dissolution rate data of the photoresist in the actual lithography experiment.

[0137] Statistical analysis and index establishment: Statistical analysis methods are used to process a large amount of validation data to establish a model accuracy evaluation index system that includes indicators such as average error rate, root mean square error, and correlation coefficient. This system comprehensively and objectively evaluates the performance of the simulation model under different lithography process parameters and different lithography pattern characteristics.

[0138] Model optimization: Based on the evaluation results, the model parameters such as the material refractive index of the wave optics model and the reaction constant of the quantum mechanical model are optimized. The optimized model is fed back to the model building module and other related modules to continuously improve the entire simulation system. Specific Implementation Example 4:

[0140] like Figure 1-4 As shown, the following is a detailed description of the hardware composition and hardware specifications of each module in Embodiment 1:

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

[0142] Kernel Visualization Module: Utilizes high-resolution VR devices such as HTC Vive, Oculus Rift, or Microsoft HoloLens AR devices to present the kernel details of the model. It is driven by a graphics workstation equipped with an NVIDIA RTX A6000 GPU, high-speed memory, and a high-performance CPU. For multi-user collaboration, an enterprise-grade wireless router or gigabit Ethernet switch supporting the 802.11ac protocol or higher is required to ensure network security.

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

[0144] Results Evaluation and Optimization Module: Data is processed using a workstation host equipped with an Intel Core i9 series CPU and 32GB of memory. Data is stored through ordinary mechanical hard drives or solid-state drives, and results and suggestions are displayed using a high-resolution LCD screen.

[0145] Model Library Management Module: Manages server hosts such as Lenovo ThinkSystem SR650 equipped with Intel Xeon Scalable processors and 64GB of memory. It uses Ceph distributed storage architecture and ordinary server hard drives to form a distributed storage system, and interacts with the cloud via a 10Gbps Ethernet interface.

[0146] Real-time simulation module: An industrial control computer with multiple communication interfaces (such as RS-485, Ethernet) is connected to the lithography equipment to acquire parameters, analog signals are converted through data acquisition cards such as NI PCI-6259, and data is stored using the Siemens SIMATIC WinCCOA real-time database.

[0147] Error compensation module: The algorithm runs on a data analysis server equipped with a Huawei Kunpeng processor and NVIDIA T4 GPU. Based on the data characteristics and access frequency, the data is stored using a storage scheme similar to that of the result evaluation and optimization module.

[0148] Simulation result verification module: The size of the photolithographic pattern is obtained by atomic force microscopy, the photoresist dissolution rate is measured by a tester based on the principle of optical interference, 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 is an appendix Figure 3 and attached Figure 4 Further analysis and expansion of the simulation diagram in this scheme:

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

[0151] Overall shape and trend: The light intensity distribution exhibits a trend of being strongest at the center and gradually decreasing towards the edges. If the dimensions of the pattern features are set reasonably, the light intensity distribution in the region of interest (such as the area where the lithographic pattern is located) should meet the expectations of the lithography process. For example, if the lithographic pattern is circular, the light intensity distribution may be relatively concentrated and uniform within the circular area, gradually weakening at the edges.

[0152] Intensity range: Pay attention to the range of light intensity values. Combine the initial light intensity with calculations of propagation and attenuation to determine whether the maximum and minimum light intensity values ​​are reasonable. 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 critical areas meets the process standards.

[0153] Symmetry and Uniformity: Check if the light intensity distribution is symmetrical. Ideally, if the lithography equipment and process parameters have no obvious asymmetric factors, the light intensity distribution should have a certain degree of symmetry, such as symmetry about the central axis. Regarding uniformity, focus on the uniformity of light intensity in the area where the lithographic pattern is located. Uniform light intensity helps to obtain a more consistent lithography effect. If there are obvious areas of uneven light intensity, it may indicate a problem with the model or parameter settings, or reflect the characteristics of the lithography equipment itself (such as the non-uniformity of the light source).

[0154] Comparison with theoretical models: The light intensity distribution in the simulation is compared with the predictions of wave optics theory, taking into account the propagation and attenuation of light in the photoresist. If it conforms to the theory, the light intensity should decrease exponentially with increasing propagation distance. If it does not conform to the theory, it may be due to oversimplification of the model or errors in simulating phenomena such as light propagation and interference.

[0155] Analysis of electric field distribution simulation diagram:

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

[0157] Numerical analysis of electric field strength: Analyze the magnitude of the electric field strength, and in conjunction with the initialization and update of the electric field parameters (such as dielectric constant epsilon, time step dt, spatial step dx, etc.), determine whether the range of electric field strength is reasonable. In actual photolithography, the electric field strength affects processes such as the polarization of photoresist molecules. Specific photolithography processes may have certain requirements for the electric field strength within a certain range, and it is necessary to confirm whether the simulation results meet expectations.

[0158] Relationship with light intensity distribution: Electric field strength is closely related to light intensity; light intensity is proportional to the square of the electric field strength. Observing the simulated electric field distribution and the simulated light intensity distribution, a correspondence should be found between the two. For example, in regions with strong light intensity, the electric field strength is usually also large, and the trend of change should be similar. If the relationship does not match, it may be due to errors in the calculation process or an inaccurate understanding of the physical relationship when simulating the interaction between light and matter.

[0159] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising a reference structure" does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes the element.

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

Claims

1. A compact photolithography simulation system based on a physical model, characterized in that: The system includes a model building 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 constructs a three-dimensional lithography model based on the wavelength of the lithography machine's light source, coherence factor, numerical aperture of the projection lens, aberration coefficient, and the type of photoresist, resolution requirements, and feature size of the lithography pattern in the lithography process. When constructing the three-dimensional lithography model, wave optics theory is used to consider the diffraction of light in the projection lens and the interference on and inside the photoresist surface. Quantum mechanics theory is combined to simulate the electronic transitions and chemical bond breaking of photoresist molecules. Simultaneously, machine learning algorithms are used to adaptively adjust the model based on the light source parameters, photoresist characteristic parameters, lithography pattern size deviation data, and corresponding simulation results from historical lithography experiments, improving the model's adaptability to different photoresist types and lithography pattern complexities. The kernel visualization module uses 3D visualization technology to display the model kernel; The simulation calculation module uses physical simulation algorithms to simulate and calculate light propagation and chemical reactions within the photoresist during the photolithography process. The result evaluation and optimization module evaluates the linewidth uniformity, edge roughness, and overlay accuracy of the photolithographic pattern based on the simulation results, optimizing the exposure dose and focus depth of the photolithography process. The model library management module categorizes, manages, and stores photolithography models for stepper projection lithography, scanning projection lithography, and ArF and KrF lithography machines, facilitating quick access and updates for users. The real-time simulation module provides real-time simulation capabilities, connecting to actual lithography equipment to acquire and simulate exposure time, exposure intensity, ambient temperature, and humidity, providing timely feedback on the simulation results. The error compensation module introduces an error compensation algorithm to analyze and compensate for errors in the simulation results, improving simulation accuracy. 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 lithographic pattern and the actual dissolution rate data of the photoresist in the actual lithography experiment, and continuously optimizes the simulation model.

2. The compact photolithography simulation system based on a physical model according to claim 1, characterized in that: The model building module employs a modeling method based on wave optics theory, considering the diffraction of light in the projection lens of the lithography equipment and the interference of light on the surface and inside the photoresist to construct an accurate lithography model. Simultaneously, it combines quantum mechanics theory to accurately simulate the microscopic physical and chemical processes of electronic transitions and chemical bond breaking in photoresist molecules during the lithography process. Furthermore, this module introduces machine learning algorithms, utilizing historical lithography experiment light source parameters, photoresist characteristic parameters, lithography pattern size deviation data, and corresponding simulation results to adaptively adjust the model, improving its adaptability to lithography scenarios with different photoresist types and varying lithography pattern complexities.

3. The compact photolithography simulation system based on a physical model according to claim 2, characterized in that: The kernel visualization module utilizes virtual reality or augmented reality technology, allowing users to immerse themselves in the model kernel by wearing VR headsets or using AR devices to observe the propagation path of light within the photoresist and the dynamic chemical reactions of photoresist molecules. This provides an intuitive understanding of the photolithography process. The module supports multiple users simultaneously entering the virtual or augmented reality environment via network connection to mark, discuss, and collaboratively analyze the model kernel. Furthermore, the module features dynamic annotation capabilities, providing real-time annotation of the light intensity distribution, photoresist reaction rate, and key physical quantities, including their values, units, and trends, facilitating user analysis.

4. The compact photolithography simulation system based on a physical model according to claim 3, characterized in that: In the simulation calculation module, the physical simulation algorithm employs the finite element time-domain method and the fast Fourier transform method. The finite element time-domain method divides the lithography area into a three-dimensional mesh and iteratively solves for light propagation in the time domain using Maxwell's equations. The fast Fourier transform method utilizes frequency domain transformation to calculate light diffraction and interference, improving the efficiency and accuracy of the simulation calculation. Parallel computing technology is also introduced, distributing the simulation task to multiple computing cores or nodes for simultaneous computation, accelerating the simulation process. This module uses adaptive mesh generation technology, automatically adjusting the mesh density based on the intensity of light changes and the drastic changes in photoresist reaction rate during the lithography process. A fine mesh is used in areas of drastic physical quantity changes, while a coarse mesh is used in areas of gradual change, ensuring simulation accuracy and reducing computational load.

5. The compact photolithography simulation system based on a physical model according to claim 4, characterized in that: The result evaluation and optimization module employs a non-dominated sorting genetic algorithm for multi-objective optimization, simultaneously optimizing parameters such as exposure dose, focus depth, and photoresist development time in the lithography process. This improves the resolution, linewidth uniformity, and edge roughness of the lithographic pattern. Combined with sensitivity analysis, by changing the values ​​of individual parameters, the module observes the changes in the linewidth and overlay accuracy of the lithographic pattern, determining the degree of influence of key parameters on the lithography effect. Furthermore, this module introduces a deep learning model, trained using a large amount of simulation data of lithography effects under different combinations of process parameters and measurement data of lithography patterns from actual lithography experiments. Using lithography process parameters as input and predicted lithography effect indicators as output, it quickly predicts the lithography effect under different combinations of process parameters, providing guidance for the optimization process.

6. The compact photolithography simulation system based on a physical model according to claim 5, characterized in that: The model library management module classifies, manages, and stores lithography models for stepper projection lithography, scanning projection lithography, ArF lithography, and KrF lithography. It has an automatic model update function. When the system detects changes in the photoresist formula, exposure mode adjustment, or changes in the wavelength of the lithography machine's light source or the image aberration correction parameters, it automatically retrieves the latest model template with the corresponding changed parameters from the cloud and adjusts and updates the model based on local historical data and actual conditions to ensure the timeliness and accuracy of the model.

7. The compact photolithography 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, and acquires the exposure time, exposure intensity, ambient temperature, and humidity of the equipment in real time for simulation and timely feedback of results. At the same time, it provides intelligent early warning to the actual lithography equipment based on the real-time simulation results. When the simulation results show that the linewidth deviation of the lithography pattern or the edge roughness index exceeds 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 focus depth parameters.

8. The compact photolithography simulation system based on a physical model according to claim 7, characterized in that: The error compensation module introduces an error compensation algorithm to analyze and compensate for errors in light intensity calculation and photoresist reaction rate calculation caused by model simplification and numerical calculation methods. It establishes an error prediction model and uses machine learning and statistical analysis methods to predict the types and magnitudes of systematic and random errors under different combinations of photolithography process parameters and equipment conditions. During the simulation process, it adjusts relevant parameters based on the prediction results to control errors.

9. The compact photolithography 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 it with the actual size measurement data of the lithographic pattern and the actual dissolution rate data of the photoresist in actual lithography experiments. It 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. It comprehensively and objectively evaluates the performance of the simulation model under different lithography process parameters and different lithography pattern characteristics. Based on the evaluation results, it optimizes the material refractive index of the wave optics model and the reaction constant model parameters of the quantum mechanical model.

10. The compact photolithography simulation system based on a physical model according to claim 9, characterized in that: The model building module takes lithography equipment parameters and process requirements as input and outputs a 3D lithography model. The kernel visualization module takes model kernel data as input and outputs a visualization display interface. Users can select and combine modules according to their needs, and add special simulation modules for new photoresists to adapt to changes in lithography simulation requirements. It has a data security management mechanism, which uses AES symmetric encryption algorithm to encrypt and store enterprise-specific lithography process parameters, experimental data, sensitive data, and model data during the simulation process, and uses SSL / TLS network protocol to encrypt and transmit them. It sets access permissions for different roles such as 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 interacts and integrates with lithography process design software, lithography equipment control software, and lithography-related software through standardized data interfaces such as XML and JSON.

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