A probe card simulation method and system based on reaction force analysis

Abstract

The present invention discloses a probe card simulation method and system based on reaction force analysis, which is used in the field of wafer simulation testing. The probe card simulation method includes the following steps: integrating multiple physical field working conditions to construct a probe card geometric model, and importing the probe card geometric model into finite element simulation analysis software; using a hybrid voxel tree model to grid the probe card geometric model, and adjusting the grid density according to the distribution requirements of multiple physical fields, while setting the simulation boundary conditions using a preset boundary condition template according to actual simulation requirements; starting the simulation calculation process based on the adjusted grid density and the simulation boundary conditions; providing a visualization interface for the physical field distribution and structural stress state during the simulation calculation process, and iteratively optimizing the performance of the probe card during the simulation process based on the reaction force distribution results. The present invention ensures higher simulation accuracy in key areas by automatically adjusting the grid density to adapt to changes in the physical field.

Description

Technical Field

[0001] The present invention relates to the field of wafer simulation testing, and in particular to a probe card simulation method and system based on reaction force analysis. Background Art

[0002] Probe cards are a key tool widely used in semiconductor manufacturing to test the electrical performance of integrated circuits (ICs) or chips. They typically contain hundreds to thousands of tiny pins (probes) that must precisely contact the tiny contact points on the chip to ensure comprehensive functional and performance testing before the chip is packaged.

[0003] Probe cards must meet highly precise physical and electrical performance standards. Simulation can help engineers predict and optimize the performance of probe cards in actual operation, especially how to effectively transmit signals and withstand mechanical stress. Through simulation, engineers can verify the effectiveness of probe card designs before physical manufacturing, thereby reducing costly prototype testing. Simulation results can be used to adjust the design of probe cards to avoid potential design defects. Probe cards must maintain performance stability under different test environments (for example, temperature and humidity changes). Simulation helps engineers evaluate and optimize the behavior of probe cards under various environmental conditions. Probe cards will repeatedly contact the chip during use, which may cause the probe to deform or break. By analyzing the structural stress and fatigue life of the probe card through simulation, these problems can be predicted and solved in advance.

[0004] Traditional meshing methods are often unable to adaptively adjust the mesh density when dealing with complex geometric structures or multi-physics field environments, resulting in insufficient simulation accuracy in key areas (such as high stress concentration or high temperature areas). Fixed mesh strategies may use fine meshes evenly across the entire model, increasing computational load and time, especially in areas where the physical field does not change much. This method is inefficient and consumes unnecessary computing resources. Design optimization in traditional methods often relies on manual iteration and lacks systematic optimization algorithm support, making the design improvement process slow and difficult to effectively optimize for performance requirements under complex conditions. Previous technologies may only focus on a single physical field (such as only thermal or structural performance) and lack comprehensive performance evaluation of probe cards under composite loading (thermal, structural, electromagnetic, etc.). Traditional optimization methods may fall into local optimal solutions due to over-reliance on greedy strategies, and lack effective strategies to balance the relationship between exploring new possibilities and utilizing known optimal solutions.

[0005] Currently, no effective solutions have been proposed for the problems in related technologies. Summary of the Invention

[0006] In order to overcome the above problems, the present invention aims to propose a probe card simulation method and system based on reaction force analysis, with the aim of solving the problem that design optimization in traditional methods often relies on manual iteration and lacks systematic optimization algorithm support, which makes the design improvement process slow and difficult to effectively optimize performance requirements under complex conditions. Previous technologies may only focus on a single physical field (such as only focusing on thermal performance or structural performance) and lack comprehensive performance evaluation of the probe card under composite loading (thermal, structural, electromagnetic, etc.) conditions.

[0007] To this end, the specific technical solutions adopted in the present invention are as follows:

[0008] According to one aspect of the present invention, a probe card simulation method based on reaction force analysis is provided, the probe card simulation method comprising the following steps:

[0009] S1. Integrate multiple physical field conditions to build a probe card geometric model, and import the probe card geometric model into finite element simulation analysis software;

[0010] S2. Meshing the probe card geometry model using a hybrid voxel tree model, adjusting the mesh density based on the distribution requirements of the multi-physics field, and setting the simulation boundary conditions using a preset boundary condition template based on the actual simulation requirements; and starting the simulation calculation process based on the adjusted mesh density and the simulation boundary conditions.

[0011] S3. Provides a visual interface for the physical field distribution and structural stress state during the simulation calculation process, and iteratively optimizes the performance of the probe card during the simulation process based on the reaction force distribution results;

[0012] The hybrid voxel tree model is used to mesh the probe card geometry model, and the mesh density is adjusted according to the distribution requirements of the multi-physics field. Simultaneously, the boundary conditions of the simulation are set using a preset boundary condition template according to the actual simulation requirements. The simulation calculation process is started based on the adjusted mesh density and the simulated boundary conditions, including the following steps:

[0013] S21, obtaining material properties and performance parameters of each part in the probe card geometric model;

[0014] S22, meshing the loaded probe card geometry model based on the hybrid voxel tree model;

[0015] S23. In the finite element simulation analysis software, setting simulation boundary conditions according to the working environment and actual use conditions of the probe card;

[0016] S24, using the established hybrid voxel tree model and the set boundary conditions, starting a multi-physics simulation to calculate the physical response and chemical response of the probe card under the set loading conditions;

[0017] S25. Analyze the performance of the probe card based on the simulation calculation results to determine whether it meets predetermined performance standards and safety requirements; and adjust the geometric structure, material properties, and performance parameters of the probe card based on the determination results.

[0018] S26. If the geometric structure, material properties and performance parameters of the probe card do not meet the preset targets, repeat steps S21-S25, and adjust the grid division, boundary conditions and physical loading conditions according to the preset targets. After each iteration, re-evaluate the performance of the probe card during the simulation process; if the geometric structure, material properties and performance parameters of the probe card do not meet the preset targets, output the geometric structure, material properties and performance parameters of the probe card.

[0019] Optionally, integrating multiple physical field conditions to construct a probe card geometric model, and importing the probe card geometric model into finite element simulation analysis software includes the following steps:

[0020] S11, obtaining functional requirements of multiple physical fields in the probe card, and using mapping software to construct a probe card geometric model according to the functional requirements of the multiple physical fields;

[0021] S12, assigning corresponding material properties and performance parameters to various parts of the probe card geometric model, and configuring various physical loading conditions encountered by the probe card in actual operation;

[0022] S13, exporting the configured geometric model and converting it into a file format that matches the finite element simulation analysis software;

[0023] S14. Load the probe card geometric model file into the finite element simulation analysis software.

[0024] Optionally, various physical loading conditions include setting mechanical loads, heat conduction equations, and chemical reaction rates.

[0025] Optionally, performing detailed meshing on the loaded probe card geometry model based on the hybrid voxel tree model includes the following steps:

[0026] S221, loading the three-dimensional geometric model of the probe card into finite element simulation analysis software;

[0027] S222. In the finite element simulation analysis software, mesh the three-dimensional geometric model of the probe card using a hybrid voxel tree model, annotate areas where physical field changes exceed a preset range, and automatically adjust the mesh size and distribution.

[0028] S223. After completing the mesh division, check the quality of the generated mesh and ensure that the mesh evenly covers the entire probe card model;

[0029] S224. Based on the quality inspection result of the mesh, if the mesh meets the requirements, the mesh data is exported; if the mesh does not meet the requirements, the process returns to step S222 and the mesh is optimized.

[0030] Optionally, in the finite element simulation analysis software, meshing the three-dimensional geometric model of the probe card using a hybrid voxel tree model, marking areas where the physical field changes exceed a preset range value, and automatically adjusting the mesh size and distribution include the following steps:

[0031] S2221. Setting parameters of the hybrid voxel tree model in finite element simulation analysis software;

[0032] S2222, converting the hybrid voxel tree model into a polyhedron boundary model through Boolean set operations;

[0033] S2223, meshing the three-dimensional geometric model of the probe card using a polyhedron boundary model, performing Boolean operations on the meshed model in the polyhedron boundary model, automatically detecting areas where physical field changes exceed a preset threshold, and marking the areas that exceed the preset threshold;

[0034] S2224. Evaluate the divided grid, identify the area of ​​the grid, and automatically adjust the size and distribution of the grid based on the evaluation results.

[0035] Optionally, meshing the three-dimensional geometric model of the probe card using a polyhedron boundary model, performing Boolean operations on the meshed grids in the polyhedron boundary model, automatically detecting areas where the physical field changes exceed a preset threshold, and marking the areas exceeding the preset threshold include the following steps:

[0036] S22231. Divide the three-dimensional geometric model of the probe card using a polyhedron boundary model, and set the size and density of the grid;

[0037] S22232. Perform Boolean operations on the divided meshes to merge, cut or reconstruct the meshes to meet the geometric and physical requirements of the model;

[0038] S22233. In polyhedral boundary models, automatically detect changes in the physical field in each grid cell and compare the physical field value of each grid cell with the surrounding grid or the global average value;

[0039] S22234. If the physical field change in the current area is detected to exceed a preset threshold, the current area is marked.

[0040] Optionally, the loading conditions include mechanical load, heat conduction equation, and chemical reaction rate equation;

[0041] The calculation formula for mechanical load is:

[0042]

[0043] The calculation formula of the heat conduction equation is:

[0044]

[0045] The formula for calculating the chemical reaction rate equation is:

[0046]

[0047] Where, σ represents the mechanical load;

[0048] E represents Young's modulus;

[0049] u represents displacement;

[0050] x represents the position;

[0051] α represents the thermal diffusivity;

[0052] Q represents the heat source per unit volume;

[0053] ρ represents density;

[0054] c p represents specific heat capacity;

[0055] T represents temperature;

[0056] t represents time;

[0057] (A) and (B) indicate the concentrations of the products;

[0058] (C) represents the concentration of the reactant;

[0059] k represents the reaction rate constant;

[0060] m and n represent the chemical reaction order of the reactants;

[0061] represents partial differential;

[0062] d represents a small increment of the variable;

[0063] represents the Laplace operator.

[0064] Optionally, providing a visualization interface of the physical field distribution and structural stress state during the simulation calculation process, and iteratively optimizing the performance of the probe card during the simulation process based on the reaction force distribution results includes the following steps:

[0065] S31. Use the visualization tools provided in the finite element simulation analysis software to display the physical field distribution and structural stress state;

[0066] S32. Using a performance optimization reinforcement learning algorithm, the performance of the probe card is evaluated based on the distribution of stress and reaction force by setting a reward mechanism.

[0067] S33, adjusting the probe card design parameters according to the results output by the performance optimization reinforcement learning algorithm, and re-performing the simulation to evaluate the performance changes after the adjustment;

[0068] S34. Monitor and analyze the frequency and efficiency of action selection in the performance optimization reinforcement learning algorithm, and adjust the exploration parameters appropriately based on real-time feedback until the preset performance standards are met or the upper limit of the number of simulations is reached.

[0069] Optionally, using a performance optimization reinforcement learning algorithm and setting a reward mechanism to evaluate the performance of the probe card based on the distribution of stress and reaction force includes the following steps:

[0070] S321, determining a reward function according to an optimization goal of the probe card performance;

[0071] S322. Setting a learning rate for the performance optimization reinforcement learning algorithm, and analyzing the distribution of stress and reaction force to determine the load-bearing capacity and possible failure points of the probe card structure in actual use;

[0072] S323. Initialize a Q value table based on the judgment result, wherein each state and action in the Q value table corresponds to an initially estimated Q value;

[0073] S324. Integrate the Q-learning algorithm with the finite element simulation software to ensure that in each simulation iteration, actions can be selected based on the current state and feedback can be obtained from the simulation results;

[0074] S325. Select an execution action according to the current Q value table and perform simulation;

[0075] S326. Calculate the reward value based on the simulation results and the reward mechanism, and update the Q-value table using the Q-learning update rule;

[0076] S327, executing new action selection and simulation according to the updated Q value table, and repeating this process until the performance standard is met or the set number of iterations is completed;

[0077] The calculation formula for the reward value is:

[0078] Q(x,y)=Q(x,y)+ε[K(x,y)+γmax y′ Q(x′, y′) − Q(x, y)];

[0079] Where x represents the current state;

[0080] y represents the action taken;

[0081] x′ represents the new state after the action;

[0082] y′ represents the action in the new state;

[0083] Q(x, y) represents the expected total reward of taking action y in a given state x;

[0084] ε represents the learning rate;

[0085] γ represents the discount factor;

[0086] Q(x′, y′) represents the total expected reward of taking action y in the new state x;

[0087] K(x,y) represents the total reward expected from taking action y in a specific state x.

[0088] According to another aspect of the present invention, there is also provided a probe card simulation system based on reaction force analysis, the system comprising: a geometric model construction and integration module, a meshing and simulation setting module, and a simulation result performance optimization module;

[0089] The geometric model construction and integration module, the meshing and simulation setting module, and the simulation result performance optimization module are connected in sequence;

[0090] The geometric model construction and integration module is used to integrate multiple physical field conditions to build the probe card geometric model and import the probe card geometric model into the finite element simulation analysis software;

[0091] The meshing and simulation setup module is used to mesh the probe card geometry using a hybrid voxel tree model, adjust the mesh density based on the distribution requirements of the multi-physics field, and set the simulation boundary conditions using a preset boundary condition template based on the actual simulation requirements; and start the simulation calculation process based on the adjusted mesh density and simulation boundary conditions;

[0092] The simulation result performance optimization module is used to provide a visual interface for the physical field distribution and structural stress state during the simulation calculation process, and iteratively optimize the performance of the probe card during the simulation process based on the reaction force distribution results.

[0093] Compared with the existing technology, this application has the following beneficial effects:

[0094] 1. The present invention integrates multiple physical field requirements to design the probe card, making the simulation closer to the actual usage environment. It can simulate and analyze the behavior of the probe card under various extreme conditions, improve the practicality and reliability of the design, and use finite element analysis software to analyze the structural strength, thermal stability and electromagnetic compatibility of the probe card, etc., to help engineers comprehensively evaluate all aspects of product design performance. Problems can be discovered and solved through simulation analysis, which can significantly reduce production risks and costs.

[0095] 2. The hybrid voxel tree model in the present invention can provide efficient meshing in complex geometry and multi-physics field environments. By automatically adjusting the mesh density to adapt to changes in the physical field, it ensures higher simulation accuracy in key areas such as high stress or high temperature areas. The adaptive meshing strategy allows the use of coarser meshes in areas where the physical field changes little, and fine meshes in key areas. This not only reduces the consumption of computing resources, but also speeds up the simulation process, making the entire simulation more efficient. By marking areas where the physical field changes exceed a preset threshold, engineers can easily identify areas where problems may exist. Boolean operations provide powerful tools in the meshing process, allowing engineers to adapt to complex model geometries by merging, cutting or reconstructing meshes. By performing a comprehensive mesh quality check before simulation, it is ensured that all meshes meet the requirements of numerical analysis. After each simulation, the model is adjusted and optimized based on the results, so that the design gradually approaches the expected goal. Multi-physics field simulation allows for simultaneous consideration of multiple loading conditions, such as thermal, structural, electromagnetic, etc., providing a comprehensive evaluation of the comprehensive performance of the probe card.

[0096] 3. Through visualization tools, the present invention allows engineers to intuitively observe the performance of the probe card under different physical field conditions, including temperature distribution, stress state, etc. This visualization support helps engineers better understand complex data and make more accurate decisions. It uses performance optimization reinforcement learning algorithms to continuously learn and adjust during the simulation process. By continuously iteratively updating the Q value table, the algorithm can optimize the design of the probe card to adapt to various test conditions and expected performance. It adopts a piecewise asymptotic greedy strategy to effectively balance exploration and utilization, avoid the problem of local optimal solutions, and allow the algorithm to more effectively search for global optimal solutions, bringing higher adaptability and optimization potential to the probe card design. Through reward mechanisms and performance evaluation, the reinforcement learning algorithm can be based on the set performance goals. BRIEF DESCRIPTION OF THE DRAWINGS

[0097] The above characteristics, features and advantages of the present invention and their implementation methods and methods will become more clearly understood in conjunction with the following description of the embodiments, which will be described in detail in conjunction with the accompanying drawings. Here, a schematic diagram is shown:

[0098] Figure 1 is a flow chart of a probe card simulation method based on reaction force analysis according to an embodiment of the present invention;

[0099] Figure 2 This is a principle block diagram of a probe card simulation system based on reaction force analysis according to an embodiment of the present invention.

[0100] In the picture:

[0101] 1. Geometric model construction and integration module; 2. Meshing and simulation setting module; 3. Simulation result performance optimization module. DETAILED DESCRIPTION

[0102] In order to help those skilled in the art better understand the present invention, the following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of this application.

[0103] According to an embodiment of the present invention, a probe card simulation method and system based on reaction force analysis are provided.

[0104] The present invention will now be further described with reference to the accompanying drawings and specific embodiments. Figure 1 As shown, according to one embodiment of the present invention, a probe card simulation method based on reaction force analysis is provided, and the probe card simulation method includes the following steps:

[0105] S1. Integrate multiple physical field conditions to build a probe card geometric model, and import the probe card geometric model into the finite element simulation analysis software.

[0106] Preferably, integrating multiple physical field conditions to construct a probe card geometric model, and importing the probe card geometric model into finite element simulation analysis software includes the following steps:

[0107] S11. Obtain the functional requirements of the multi-physics field in the probe card, and use mapping software to construct a probe card geometric model based on the functional requirements of the multi-physics field. The multi-physics field functional requirements usually involve different operating environments that the probe card needs to adapt to, such as temperature changes, electromagnetic field influences, mechanical stress, etc. The functional requirements define the performance standards and stability that the probe card should maintain in these environments.

[0108] S12. Assign corresponding material properties and performance parameters to each part of the probe card geometric model, and configure various physical loading conditions encountered by the probe card in actual operation; material properties and performance parameters include elastic modulus, density, thermal expansion coefficient, conductivity, etc. of the material; material properties directly affect the behavior and stability of the probe card in various physical fields; physical loading conditions include temperature range (e.g., -40°C to 85°C), mechanical stress (e.g., pressure, tension, bending), and voltage load (e.g., voltage peak during signal transmission);

[0109] S13, exporting the configured geometric model and converting it into a file format that matches the finite element simulation analysis software;

[0110] S14. Load the probe card geometric model file into the finite element simulation analysis software.

[0111] Preferably, various physical loading conditions include setting mechanical load, heat conduction equation and chemical reaction rate.

[0112] It should be explained that the use conditions and performance indicators of the probe card are collected and analyzed, and then the geometric model of the probe card is designed according to these requirements using CAD or other drawing software to ensure that the design meets all expected functional requirements. The correct material properties are assigned to each part in the geometric model of the probe card, and the physical loading conditions that may be encountered in the expected operation are set. The physical loading conditions will be used in the simulation analysis to verify the performance of the probe card. The probe card model created in the drawing software is exported to a format suitable for finite element analysis, such as STEP or IGES file, to ensure that the model can be correctly loaded and analyzed in the finite element software. Finally, the exported file is loaded into the finite element analysis software (such as ANSYS, ABAQUS, etc.), and further settings are made in the software, such as defining the mesh size, selecting the analysis type (static, dynamic, thermal analysis, etc.), and preparing for detailed simulation analysis.

[0113] S2. Use the hybrid voxel tree model to mesh the probe card geometry model, adjust the mesh density according to the distribution requirements of the multi-physics field, and set the simulation boundary conditions using the preset boundary condition template according to the actual simulation requirements; start the simulation calculation process based on the adjusted mesh density and simulation boundary conditions.

[0114] The hybrid voxel tree model is used to mesh the probe card geometry model, and the mesh density is adjusted according to the distribution requirements of the multi-physics field. Simultaneously, the boundary conditions of the simulation are set using a preset boundary condition template according to the actual simulation requirements. The simulation calculation process is started based on the adjusted mesh density and the simulated boundary conditions, including the following steps:

[0115] S21. Obtain material properties and performance parameters of each part in the probe card geometric model.

[0116] S22. Based on the hybrid voxel tree model, mesh the loaded probe card geometry model.

[0117] Preferably, performing detailed meshing on the loaded probe card geometric model based on the hybrid voxel tree model comprises the following steps:

[0118] S221, loading the three-dimensional geometric model of the probe card into finite element simulation analysis software;

[0119] S222. In the finite element simulation analysis software, a hybrid voxel tree model is used to mesh the three-dimensional geometric model of the probe card, and areas where the physical field changes exceed a preset range are marked, while the mesh size and distribution are automatically adjusted.

[0120] Preferably, in the finite element simulation analysis software, meshing the three-dimensional geometric model of the probe card using a hybrid voxel tree model, marking areas where the physical field changes exceed a preset range value, and automatically adjusting the mesh size and distribution include the following steps:

[0121] S2221. Setting parameters of the hybrid voxel tree model in finite element simulation analysis software;

[0122] S2222, converting the hybrid voxel tree model into a polyhedron boundary model through Boolean set operations;

[0123] S2223, meshing the three-dimensional geometric model of the probe card using a polyhedron boundary model, performing Boolean operations on the meshed model in the polyhedron boundary model, automatically detecting areas where physical field changes exceed a preset threshold, and marking the areas that exceed the preset threshold;

[0124] S2224. Evaluate the divided grid, identify the area of ​​the grid, and automatically adjust the size and distribution of the grid based on the evaluation results.

[0125] It should be explained that data is obtained through literature research, experimental data or communication with material suppliers, and these data such as the thermal conductivity, elastic modulus, resistivity, etc. of the material are recorded in the database for calling in the simulation software. The hybrid voxel tree model method is used to achieve adaptive meshing, and the size and distribution of the mesh are dynamically adjusted according to the geometric complexity of the model and the requirements of the physical field, so as to improve the accuracy and efficiency of the calculation. The geometric model is converted into a format supported by the simulation software using CAD software or specific conversion tools, and then these files are loaded in the simulation software.

[0126] According to the specific application scenarios and simulation requirements of the probe card, appropriate voxel size, depth and splitting criteria are set to ensure a balance between the model's detail and computational efficiency. Boolean operation technology is used to convert the voxel model into a polyhedral mesh model that can be used for finite element analysis, and operations such as merging small voxels and deleting invalid voxels are performed. After the meshing is completed, based on the results of the physical field analysis, areas where the physical field changes exceed the predetermined threshold are automatically detected and marked, and mesh areas that require special attention are identified. Based on the results of the mesh quality assessment, the mesh is optimized and adjusted, such as adjusting the mesh density and improving the mesh shape, to ensure the accuracy and efficiency of the simulation.

[0127] The hybrid voxel tree model is an advanced technology for meshing and geometric modeling, especially suitable for complex geometries and multi-physics simulation environments. This model combines the characteristics of voxels and octrees, providing an efficient way to process and simplify the representation of 3D geometric data while ensuring simulation accuracy and efficiency.

[0128] The key features of the hybrid voxel tree model are as follows:

[0129] 1) Voxel representation: A voxel is a small cube in three-dimensional space. Each voxel stores physical properties about its location (such as material type, density, thermal conductivity, etc.). The hybrid voxel tree simplifies model processing by decomposing complex three-dimensional models into voxel grids.

[0130] 2) Octree structure: An octree is a tree-like data structure used for three-dimensional space partitioning. Each node represents a spatial region, which can be further subdivided into eight subregions. In the hybrid voxel tree model, the octree is used to dynamically manage the distribution of voxels, adjusting the size and distribution of voxels according to the complexity of geometric or physical requirements.

[0131] 3) Adaptive meshing: The model automatically adjusts the mesh density based on changes in the physical field or the complexity of the geometry, using finer meshes for areas with high detail or high variation, and coarser meshes for areas with less variation.

[0132] Boolean operations are a technique for combining or modifying geometric entities, commonly used in computer graphics, CAD (computer-aided design), and geometric modeling. These operations can be performed between two or more geometric entities and include operations such as union, intersection, and difference, used to create complex shapes and models. The following are the three basic types of Boolean operations:

[0133] 1) Union:

[0134] Operation: Merge two geometries into a new geometry that contains the entire space of the two original geometries.

[0135] Application: Used to merge two independent parts or shapes, commonly used in assembly or model integration.

[0136] 2) Intersection:

[0137] Operation: Create a new geometry that has the common parts of the two geometries.

[0138] Application: Used to find the area occupied by two shapes. It is often used in the analysis of overlapping and matching parts in design.

[0139] 3) Difference:

[0140] Operation: Cut out the part of one geometry that overlaps with another geometry.

[0141] Application: Used to remove parts of a complex block, such as openings, cuts or shape trimming.

[0142] Preferably, meshing the three-dimensional geometric model of the probe card using a polyhedron boundary model, performing Boolean operations on the meshes in the polyhedron boundary model, automatically detecting areas where the physical field changes exceed a preset threshold, and marking the areas exceeding the preset threshold include the following steps:

[0143] S22231. Divide the three-dimensional geometric model of the probe card using a polyhedron boundary model, and set the size and density of the grid;

[0144] S22232. Perform Boolean operations on the divided meshes to merge, cut or reconstruct the meshes to meet the geometric and physical requirements of the model;

[0145] S22233. In polyhedral boundary models, automatically detect changes in the physical field in each grid cell and compare the physical field value of each grid cell with the surrounding grid or the global average value;

[0146] S22234. If the physical field change in the current area is detected to exceed a preset threshold, the current area is marked.

[0147] S223. After completing the mesh division, check the quality of the generated mesh and ensure that the mesh evenly covers the entire probe card model;

[0148] S224. Based on the quality inspection result of the mesh, if the mesh meets the requirements, the mesh data is exported; if the mesh does not meet the requirements, the process returns to step S222 and the mesh is optimized.

[0149] Preferably, the loading conditions include mechanical load, heat conduction equation and chemical reaction rate equation;

[0150] The calculation formula for mechanical load is:

[0151]

[0152] The calculation formula of the heat conduction equation is:

[0153]

[0154] The formula for calculating the chemical reaction rate equation is:

[0155]

[0156] Where, σ represents the mechanical load;

[0157] E represents Young's modulus;

[0158] u represents displacement;

[0159] x represents the position;

[0160] α represents the thermal diffusivity;

[0161] Q represents the heat source per unit volume;

[0162] ρ represents density;

[0163] c p represents specific heat capacity;

[0164] T represents temperature;

[0165] t represents time;

[0166] (A) and (B) indicate the concentrations of the products;

[0167] (C) represents the concentration of the reactant;

[0168] k represents the reaction rate constant;

[0169] m and n represent the chemical reaction order of the reactants;

[0170] represents partial differential;

[0171] d represents a small increment of the variable;

[0172] represents the Laplace operator.

[0173] It should be explained that the geometric model of the probe card is converted into a polyhedron boundary model. This process is usually completed in finite element analysis software. By selecting the appropriate resolution to set the size and density of the grid, it is ensured that every detail can be fully captured. High-precision areas, such as high stress concentration or high temperature gradient areas, will be set with a denser grid. Boolean operations are used to optimize the grid structure, including merging adjacent grid cells with similar characteristics, or cutting the grid to adapt to complex geometric structures, which helps to reduce the total number of grids while maintaining the level of detail required for analysis. Physical field analysis is performed through the set analysis software, and areas where physical field changes exceed the preset threshold are automatically identified, including changes in physical quantities such as temperature, stress, and electromagnetic fields. Once these areas are identified, they are immediately marked. , in order to further analyze or re-divide the grid, set the automatic annotation function in the software, and when a physical field change exceeding the threshold is detected, these areas are immediately visually annotated. Visual annotation helps engineers identify areas that may require special attention or design improvements. Use the mesh quality detection tool of the finite element software to check the uniformity of the mesh, the shape quality and size distribution of the mesh units to ensure that all meshes meet the standards of numerical analysis and there are no mesh units that are too distorted or extremely uneven in size. If the mesh inspection results show that the mesh quality does not meet expectations, it is necessary to adjust the meshing parameters or re-run the Boolean operation. If the mesh meets the requirements, the mesh data can be exported to a format suitable for further simulation analysis, such as a file format supported by FEA or CFD software.

[0174] S23. In the finite element simulation analysis software, set the simulation boundary conditions according to the working environment and actual use conditions of the probe card.

[0175] S24. Using the established hybrid voxel tree model and the set boundary conditions, start multi-physics simulation to calculate the physical response and chemical response of the probe card under the set loading conditions.

[0176] S25. Analyze the performance of the probe card based on the simulation calculation results to determine whether it meets the predetermined performance standards and safety requirements; and adjust the geometric structure, material properties and performance parameters of the probe card based on the judgment results.

[0177] S26. If the geometric structure, material properties and performance parameters of the probe card do not meet the preset targets, repeat steps S21-S25, and adjust the grid division, boundary conditions and physical loading conditions according to the preset targets. After each iteration, re-evaluate the performance of the probe card during the simulation process; if the geometric structure, material properties and performance parameters of the probe card do not meet the preset targets, output the geometric structure, material properties and performance parameters of the probe card.

[0178] It should be explained that the working environment of the probe card should be defined, including temperature range, voltage or current level, mechanical pressure, etc., and these conditions should be set as boundary conditions in the simulation software. For example, if the probe card works at high temperature, the corresponding temperature boundary conditions should be set to ensure that the simulation reflects the actual operating environment. Based on the set boundary conditions and the prepared mesh model, the simulation analysis is started, and the multi-physics simulation module (such as ANSYS Multiphysics) is used to calculate the stress, temperature distribution, electromagnetic effect, etc. of the probe card under various loading conditions. Through these calculations, the physical and chemical stability of the probe card is evaluated, and the simulation results are analyzed, especially focusing on those areas that exceed the safety threshold or potential failure points. Based on the analysis results, the design of the probe card is adjusted, such as changing the material to improve heat resistance or adjusting the geometry to reduce stress concentration, to ensure that the probe card meets all performance and safety standards. Simulation should not be a one-time process. Based on the results of each simulation, it may be necessary to go back to the previous step to adjust the model meshing, update the boundary conditions, or optimize the loading conditions. This iterative process helps to gradually improve the design of the probe card until all predetermined performance goals are met.

[0179] In the finite element simulation analysis software, setting the boundary conditions of the simulation according to the working environment and actual usage conditions of the probe card includes the following steps: Setting the boundary conditions of the probe card in the finite element simulation analysis software usually involves defining various parameters of its operating environment, such as temperature range, mechanical load (such as pressure or impact), voltage and current levels, and configuring them within the software by specifying corresponding parameter values ​​and properties to ensure that the simulation environment accurately reflects the working status of the probe card in actual use; the key to this process is to accurately input the environment and operating parameters to simulate the behavior of the probe card under various physical influences, so as to predict its performance and identify potential structural problems.

[0180] Using the established hybrid voxel tree model and the set boundary conditions, launching a multiphysics simulation to calculate the physical and chemical responses of the probe card under the set loading conditions involves the following steps:

[0181] Load the 3D geometry of the probe card into the finite element simulation software. The model has been meshed using a hybrid voxel tree model to ensure that the mesh distribution is adapted to various physical and chemical loading conditions.

[0182] According to the working environment and actual use conditions of the probe card, set corresponding boundary conditions, including temperature boundary, electromagnetic field strength, mechanical load, etc. These conditions directly affect the accuracy of the simulation and the reliability of the results;

[0183] Depending on the physical and chemical responses that need to be evaluated, select the appropriate simulation type, such as thermal analysis, structural analysis, electromagnetic analysis, or a combination of them to simulate the multiphysics environment;

[0184] After all necessary conditions and parameters are set, the simulation process is started. The software will calculate the probe card's response under various loading conditions based on the set model and boundary conditions. This includes temperature changes, stress distribution, electromagnetic effects, and other related physical and chemical responses.

[0185] After the simulation is complete, analyze and evaluate the physical and chemical response of the probe card. Use the tools provided by the software to visualize the simulation results and identify any unexpected behavior or potential failure points.

[0186] S3. Provide a visual interface for the physical field distribution and structural stress state during the simulation calculation process, and iteratively optimize the performance of the probe card during the simulation process based on the reaction force distribution results.

[0187] Preferably, providing a visual interface of the physical field distribution and structural stress state during the simulation calculation process, and iteratively optimizing the performance of the probe card during the simulation process based on the reaction force distribution results includes the following steps:

[0188] S31. Use the visualization tools provided in the finite element simulation analysis software to display the physical field distribution and structural stress state;

[0189] S32. Using the performance optimization reinforcement learning algorithm, the performance of the probe card is evaluated based on the distribution of stress and reaction force by setting up a reward mechanism.

[0190] Preferably, using a performance optimization reinforcement learning algorithm and setting a reward mechanism to evaluate the performance of the probe card based on the distribution of stress and reaction force includes the following steps:

[0191] S321, determining a reward function according to an optimization goal of the probe card performance;

[0192] S322. Setting a learning rate for the performance optimization reinforcement learning algorithm, and analyzing the distribution of stress and reaction force to determine the load-bearing capacity and possible failure points of the probe card structure in actual use;

[0193] S323. Initialize a Q value table based on the judgment result, wherein each state and action in the Q value table corresponds to an initially estimated Q value;

[0194] S324. Integrate the Q-learning algorithm with the finite element simulation software to ensure that in each simulation iteration, actions can be selected based on the current state and feedback can be obtained from the simulation results;

[0195] S325. Select an execution action according to the current Q value table and perform simulation;

[0196] S326. Calculate the reward value based on the simulation results and the reward mechanism, and update the Q-value table using the Q-learning update rule;

[0197] S327, executing new action selection and simulation according to the updated Q value table, and repeating this process until the performance standard is met or the set number of iterations is completed;

[0198] The calculation formula for the reward value is:

[0199] Q(x,y)=Q(x,y)+ε[K(x,y)+γmax y′ Q(x′,y′)-Q(x,y)];

[0200] Where x represents the current state;

[0201] y represents the action taken;

[0202] x′ represents the new state after the action;

[0203] y′ represents the action in the new state;

[0204] Q(x, y) represents the expected total reward of taking action y in a given state x;

[0205] ε represents the learning rate;

[0206] γ represents the discount factor;

[0207] Q(x′, y′) represents the total expected reward of taking action y in the new state x;

[0208] K(x,y) represents the expected total reward of taking action y in a specific state x.

[0209] It should be explained that after the simulation is completed, the built-in visualization tools of the simulation software are used to display the physical field (such as temperature, electromagnetic field) distribution and structural stress state of the probe card. These tools usually provide various graphical display options, such as contour maps, heat maps and vector field maps, so that every detail of the simulation results can be viewed and analyzed in detail. The reward function is set to reflect the goal of optimizing the performance of the probe card. For example, if the goal is to minimize stress concentration, the reward function can be set to increase the reward value when the stress is below a certain threshold. The key to choosing the reward function is to ensure that it can correctly reflect the design goals and the expected performance improvement. An appropriate learning rate is selected to control the speed and stability of the learning process. The learning rate determines the extent to which new information covers old information. A moderate learning rate helps the algorithm converge effectively and find the optimal strategy. Initialize the Q value table for each value in the simulation. The algorithm provides a starting estimate for each possible state and action combination. These initial values ​​are usually set to zero or preset based on prior knowledge. The reinforcement learning algorithm is interfaced with the simulation software, allowing the algorithm to execute decisions in the simulation environment (such as modifying boundary conditions or grid configurations) and receive feedback based on the simulation results to update the Q value. The next action is selected based on the optimal strategy in the Q value table, and then the action is executed in the simulation environment, such as adjusting the load conditions or changing the material properties. After the simulation is completed, the reward value is calculated according to the set reward function, and then the Q value table is updated according to the Q learning formula to improve future decisions. The simulation, evaluation, and learning process is repeated. Each iteration selects an action based on the updated Q value table, and the design of the probe card is continuously optimized until the performance reaches the predetermined standard or the set number of iterations is reached.

[0210] The performance-optimized reinforcement learning algorithm is an improved Q-learning algorithm. The piecewise asymptotic greedy strategy adopted by the improved Q-learning algorithm is designed to solve the problem that traditional greedy strategies may fall into local optimal solutions. This strategy enables the algorithm to search for the global optimal solution more effectively by dynamically adjusting the balance between exploration and utilization. The following are the detailed steps of the improved greedy strategy: it mainly includes initialization parameter setting, initial exploration through greedy strategy, re-evaluation of action probability to adapt to the learning stage, and dynamic adjustment of strategy and exploration intensity (H). In the initial stage, the algorithm encourages exploration through a higher exploration probability (∈), and then gradually increases the utilization of the optimal action, thereby balancing exploration and utilization and optimizing performance. By comparing the efficiency of consecutive iterations and adjusting parameters, the algorithm continues to optimize until the upper limit of the learning times is reached or other termination conditions are met, and finally determines the optimal strategy and Q value. This method is suitable for complex decision-making environments that need to deal with uncertainty and dynamic changes.

[0211] S33. Adjust the probe card design parameters according to the results output by the performance optimization reinforcement learning algorithm, and re-execute the simulation to evaluate the performance changes after the adjustment.

[0212] S34. Monitor and analyze the frequency and efficiency of action selection in the performance optimization reinforcement learning algorithm, and adjust the exploration parameters appropriately based on real-time feedback until the preset performance standards are met or the upper limit of the number of simulations is reached.

[0213] It is important to explain that the results of the performance optimization reinforcement learning algorithm are first analyzed, with particular attention paid to which parameter adjustments lead to performance improvements or degradations. Based on the analysis results, the probe card design parameters, such as geometry, material properties, or loading conditions, are adjusted. Simulations are then re-executed on the adjusted model to evaluate the specific impact of these changes on the probe card's performance. The effectiveness of the design adjustments is verified by comparing the simulation results before and after the adjustments. The simulation results are analyzed to determine whether the predetermined performance standards are met or whether further adjustments are needed. During the reinforcement learning process, the frequency and efficiency of each action are monitored in real time, including tracking which actions lead to performance improvements and which are potentially suboptimal choices. The specific contribution of each action to performance is analyzed to determine the most effective strategy. This information is used to adjust the action selection strategy to optimize the balance between exploration and exploitation. Based on the analysis results of the action selection, the exploration parameters (such as the exploration intensity H or the exploration probability ∈) are appropriately adjusted to more effectively guide the optimal design solution. Simulations are then continued, and the algorithm is adjusted based on real-time feedback until the simulation results indicate that the probe card design meets the preset performance standards or the upper limit of the number of simulations is reached.

[0214] A specific example is as follows: An electronic equipment manufacturer wants to develop a high-performance probe card that can operate stably under various environmental conditions. This probe card needs to be able to withstand temperature fluctuations, strong electromagnetic field interference, and varying degrees of mechanical stress.

[0215] 1) Collect requirements and prepare for design:

[0216] Functional requirements acquisition: The team first determines the conditions that the probe card must meet, such as temperature range (-40°C to 85°C), electromagnetic field effects (requiring anti-interference capabilities), and mechanical stresses (such as pressure and tension).

[0217] Design the geometric model: Use CAD software to design the preliminary geometric model of the probe card based on these multi-physics requirements.

[0218] 2) Material selection and model configuration:

[0219] Material property configuration: Select a suitable material such as high-performance plastic or metal alloy, and configure the material properties (e.g. elastic modulus, conductivity).

[0220] Multi-physics simulation: Import the designed probe card model into simulation software to perform thermal analysis, electromagnetic compatibility analysis, and structural strength analysis to evaluate the response under different physical loading conditions.

[0221] 3) Performance Optimization Reinforcement Learning:

[0222] Apply reinforcement learning algorithms: Based on preliminary simulation results, use reinforcement learning algorithms to optimize design parameters, such as adjusting probe placement or material thickness, to improve performance and durability.

[0223] Parameter tuning and resimulation: Adjust key design parameters based on feedback provided by the algorithm and re-simulate to verify performance improvements.

[0224] 4) Prototype manufacturing and testing:

[0225] Prototype Fabrication: Probe card prototypes are fabricated based on the final optimized design.

[0226] Real-world testing: Testing the performance of the prototype in a controlled environment, such as through thermal cycling, mechanical strength, and electromagnetic compatibility testing, to ensure it meets all pre-set standards.

[0227] According to another embodiment of the present invention, Figure 2 As shown, a probe card simulation system based on reaction force analysis is also provided, which includes: a geometric model construction and integration module 1, a meshing and simulation setting module 2, and a simulation result performance optimization module 3;

[0228] The geometric model construction and integration module 1, the meshing and simulation setting module 2, and the simulation result performance optimization module 3 are connected in sequence;

[0229] Geometric model construction and integration module 1 is used to integrate multiple physical field conditions to build a probe card geometric model and import the probe card geometric model into the finite element simulation analysis software;

[0230] Meshing and simulation setting module 2 is used to mesh the probe card geometry model using a hybrid voxel tree model, adjust the mesh density according to the distribution requirements of the multi-physics field, and set the simulation boundary conditions using a preset boundary condition template according to the actual simulation requirements; and start the simulation calculation process based on the adjusted mesh density and simulation boundary conditions;

[0231] The simulation result performance optimization module 3 is used to provide a visual interface for the physical field distribution and structural stress state during the simulation calculation process, and iteratively optimize the performance of the probe card during the simulation process based on the reaction force distribution results.

[0232] In summary, with the help of the above technical solutions of the present invention, the present invention designs a probe card by integrating multi-physical field requirements, so that the simulation is closer to the actual use environment, can simulate and analyze the behavior of the probe card under various extreme conditions, improve the practicality and reliability of the design, and use finite element analysis software to analyze the structural strength, thermal stability and electromagnetic compatibility of the probe card, etc., to help engineers comprehensively evaluate all aspects of product design performance, and find and solve problems through simulation analysis, which can significantly reduce production risks and costs; the hybrid voxel tree model in the present invention can provide efficient meshing in complex geometry and multi-physical field environments, and automatically adjust the mesh density to adapt to changes in the physical field to ensure higher simulation accuracy in key areas such as high stress or high temperature areas. The adaptive meshing strategy allows the use of coarser meshes in areas where the physical field changes little, and fine meshes in key areas, which not only reduces the consumption of computing resources, but also speeds up the simulation process, making the entire simulation more efficient. By marking areas where the physical field changes exceed the preset threshold, engineers can easily identify areas where problems may exist. Boolean operations provide powerful tools in the meshing process, allowing Engineers can adapt to complex model geometry by merging, cutting or reconstructing meshes, and perform comprehensive mesh quality checks before simulation to ensure that all meshes meet the requirements of numerical analysis. After each simulation, the model is adjusted and optimized based on the results, so that the design gradually approaches the expected goal. Multi-physics field simulation allows for simultaneous consideration of multiple loading conditions, such as thermal, structural, electromagnetic, etc., providing a comprehensive evaluation of the comprehensive performance of the probe card. Through visualization tools, the present invention allows engineers to intuitively observe the performance of the probe card under different physical field conditions, including temperature distribution, stress state, etc. This visualization support helps engineers better understand complex data and make more accurate decisions. The performance optimization reinforcement learning algorithm is used to continuously learn and adjust during the simulation process. By continuously iteratively updating the Q value table, the algorithm can optimize the design of the probe card to adapt to various test conditions and expected performance. A piecewise asymptotic greedy strategy is adopted to effectively balance exploration and utilization, avoid the problem of local optimal solutions, and allow the algorithm to more effectively search for global optimal solutions, bringing higher adaptability and optimization potential to the probe card design. Through reward mechanisms and performance evaluation, the reinforcement learning algorithm can be based on the set performance goals.

[0233] Although the present invention has been disclosed above with reference to preferred embodiments, the embodiments are merely examples for the purpose of illustration and are not intended to limit the present invention. Those skilled in the art may make various modifications and alterations without departing from the spirit and scope of the present invention. The scope of protection claimed by the present invention shall be subject to the claims.

Claims

1. A probe card simulation method based on reaction force analysis, characterized in that: The probe card simulation method comprises the following steps: S1. Integrate multiple physical field conditions to build a probe card geometric model, and import the probe card geometric model into finite element simulation analysis software; S2. Meshing the probe card geometry model using a hybrid voxel tree model, adjusting the mesh density based on the distribution requirements of the multi-physics field, and setting the simulation boundary conditions using a preset boundary condition template based on the actual simulation requirements; and starting the simulation calculation process based on the adjusted mesh density and the simulation boundary conditions. S3. Provides a visual interface for the physical field distribution and structural stress state during the simulation calculation process, and iteratively optimizes the performance of the probe card during the simulation process based on the reaction force distribution results; The method of meshing the probe card geometric model using a hybrid voxel tree model and adjusting the mesh density according to the distribution requirements of the multi-physics field, and setting the simulation boundary conditions using a preset boundary condition template according to the actual simulation requirements, and starting the simulation calculation process based on the adjusted mesh density and the simulation boundary conditions includes the following steps: S21, obtaining material properties and performance parameters of each part in the probe card geometric model; S22, meshing the loaded probe card geometry model based on the hybrid voxel tree model; S23. In the finite element simulation analysis software, setting simulation boundary conditions according to the working environment and actual use conditions of the probe card; S24, using the established hybrid voxel tree model and the set boundary conditions, starting a multi-physics simulation to calculate the physical response and chemical response of the probe card under the set loading conditions; S25. Analyze the performance of the probe card based on the simulation calculation results to determine whether it meets predetermined performance standards and safety requirements; and adjust the geometric structure, material properties, and performance parameters of the probe card based on the determination results. S26. If the geometry, material properties, and performance parameters of the probe card do not meet the preset targets, repeating steps S21-S25, adjusting the meshing, boundary conditions, and physical loading conditions according to the preset targets, and re-evaluating the performance of the probe card during the simulation after each iteration; if the geometry, material properties, and performance parameters of the probe card do not meet the preset targets, outputting the geometry, material properties, and performance parameters of the probe card; The detailed meshing of the loaded probe card geometric model based on the hybrid voxel tree model includes the following steps: S221, loading the three-dimensional geometric model of the probe card into finite element simulation analysis software; S222. In the finite element simulation analysis software, mesh the three-dimensional geometric model of the probe card using a hybrid voxel tree model, annotate areas where physical field changes exceed a preset range, and automatically adjust the mesh size and distribution. S223. After completing the mesh division, check the quality of the generated mesh and ensure that the mesh evenly covers the entire probe card model; S224. Based on the quality inspection result of the mesh, if the mesh meets the requirements, the mesh data is exported; if the mesh does not meet the requirements, the process returns to step S222 and the mesh is optimized.

2. The probe card simulation method based on reaction force analysis according to claim 1, characterized in that: The method of integrating multiple physical field conditions to construct a probe card geometric model and importing the probe card geometric model into the finite element simulation analysis software includes the following steps: S11, obtaining functional requirements of multiple physical fields in the probe card, and using mapping software to construct a probe card geometric model according to the functional requirements of the multiple physical fields; S12, assigning corresponding material properties and performance parameters to various parts of the probe card geometric model, and configuring various physical loading conditions encountered by the probe card in actual operation; S13, exporting the configured geometric model and converting it into a file format that matches the finite element simulation analysis software; S14. Load the probe card geometric model file into the finite element simulation analysis software.

3. The probe card simulation method based on reaction force analysis according to claim 2, characterized in that: The various physical loading conditions include setting mechanical loads, heat conduction equations, and chemical reaction rates.

4. The probe card simulation method based on reaction force analysis according to claim 3, characterized in that: The method of meshing the three-dimensional geometric model of the probe card using a hybrid voxel tree model in the finite element simulation analysis software, marking areas where the physical field changes exceed a preset range, and automatically adjusting the mesh size and distribution includes the following steps: S2221. Setting parameters of the hybrid voxel tree model in finite element simulation analysis software; S2222, converting the hybrid voxel tree model into a polyhedron boundary model through Boolean set operations; S2223, meshing the three-dimensional geometric model of the probe card using a polyhedron boundary model, performing Boolean operations on the meshed model in the polyhedron boundary model, automatically detecting areas where physical field changes exceed a preset threshold, and marking the areas that exceed the preset threshold; S2224. Evaluate the divided grid, identify the area of ​​the grid, and automatically adjust the size and distribution of the grid based on the evaluation results.

5. The probe card simulation method based on reaction force analysis according to claim 4, characterized in that: The method of meshing the three-dimensional geometric model of the probe card using the polyhedron boundary model, performing Boolean operations on the meshed parts in the polyhedron boundary model, automatically detecting areas where the physical field changes exceed a preset threshold, and marking the areas exceeding the preset threshold includes the following steps: S22231. Divide the three-dimensional geometric model of the probe card using a polyhedron boundary model, and set the size and density of the grid; S22232. Perform Boolean operations on the divided meshes to merge, cut or reconstruct the meshes to meet the geometric and physical requirements of the model; S22233. In polyhedral boundary models, automatically detect changes in the physical field in each grid cell and compare the physical field value of each grid cell with the surrounding grid or the global average value; S22234. If the physical field change in the current area is detected to exceed a preset threshold, the current area is marked.

6. The probe card simulation method based on reaction force analysis according to claim 5, characterized in that: The loading conditions include mechanical load, heat conduction equation and chemical reaction rate equation; The calculation formula of the mechanical load is: The calculation formula of the heat conduction equation is: The calculation formula of the chemical reaction rate equation is: Where, σ represents the mechanical load; E represents Young's modulus; u represents displacement; x represents the position; α represents the thermal diffusivity; Q represents the heat source per unit volume; ρ represents density; c p represents specific heat capacity; T represents temperature; t represents time; (A) and (B) indicate the concentrations of the products; (C) represents the concentration of the reactant; k represents the reaction rate constant; m and n represent the chemical reaction order of the reactants; represents partial differential; d represents a small increment of the variable; represents the Laplace operator.

7. The probe card simulation method based on reaction force analysis according to claim 1, characterized in that: Providing a visual interface for the physical field distribution and structural stress state during the simulation calculation process, and iteratively optimizing the performance of the probe card during the simulation process based on the reaction force distribution results, includes the following steps: S31. Use the visualization tools provided in the finite element simulation analysis software to display the physical field distribution and structural stress state; S32. Using a performance optimization reinforcement learning algorithm, the performance of the probe card is evaluated based on the distribution of stress and reaction force by setting a reward mechanism. S33, adjusting the probe card design parameters according to the results output by the performance optimization reinforcement learning algorithm, and re-performing the simulation to evaluate the performance changes after the adjustment; S34. Monitor and analyze the frequency and efficiency of action selection in the performance optimization reinforcement learning algorithm, and adjust the exploration parameters appropriately based on real-time feedback until the preset performance standards are met or the upper limit of the number of simulations is reached.

8. The probe card simulation method based on reaction force analysis according to claim 7, characterized in that: The method of utilizing a performance optimization reinforcement learning algorithm to evaluate the performance of a probe card based on the distribution of stress and reaction force by setting a reward mechanism includes the following steps: S321, determining a reward function according to an optimization goal of the probe card performance; S322. Setting a learning rate for the performance optimization reinforcement learning algorithm, and analyzing the distribution of stress and reaction force to determine the load-bearing capacity and possible failure points of the probe card structure in actual use; S323. Initialize a Q value table based on the judgment result, wherein each state and action in the Q value table corresponds to an initially estimated Q value; S324. Integrate the Q-learning algorithm with the finite element simulation software to ensure that in each simulation iteration, actions can be selected based on the current state and feedback can be obtained from the simulation results; S325. Select an execution action according to the current Q value table and perform simulation; S326. Calculate the reward value based on the simulation results and the reward mechanism, and update the Q-value table using the Q-learning update rule; S327, executing new action selection and simulation according to the updated Q value table, and repeating this process until the performance standard is met or the set number of iterations is completed; The calculation formula of the reward value is: Q(x,y)=Q(x,y)+ε[K(x,y)+γmax y′ Q(x′,y′)-Q(x,y)]; Where x represents the current state; y represents the action taken; x′ represents the new state after the action; y′ represents the action in the new state; Q(x, y) represents the expected total reward of taking action y in a given state x; ε represents the learning rate; γ represents the discount factor; Q(x′, y′) represents the total expected reward of taking action y in the new state x; K(x,y) represents the total reward expected from taking action y in a specific state x.

9. A probe card simulation system based on reaction force analysis, used to implement the probe card simulation method based on reaction force analysis according to any one of claims 1 to 8, characterized in that: The system includes: geometric model construction and integration module, meshing and simulation setting module and simulation result performance optimization module; The geometric model construction and integration module, the meshing and simulation setting module, and the simulation result performance optimization module are connected in sequence; The geometric model construction and integration module is used to integrate multiple physical field conditions to build the probe card geometric model and import the probe card geometric model into the finite element simulation analysis software; The meshing and simulation setup module is used to mesh the probe card geometry using a hybrid voxel tree model, adjust the mesh density based on the distribution requirements of the multi-physics field, and set the simulation boundary conditions using a preset boundary condition template based on the actual simulation requirements; and start the simulation calculation process based on the adjusted mesh density and simulation boundary conditions; The simulation result performance optimization module is used to provide a visual interface for the physical field distribution and structural stress state during the simulation calculation process, and iteratively optimize the performance of the probe card during the simulation process based on the reaction force distribution results.

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