Thermal simulation method and system based on 5G circuit board and circuit board

Abstract

The invention relates to the technical field of 5G circuit board thermal simulation, in particular to a thermal simulation method and system based on a 5G circuit board and the circuit board. The method comprises the following steps: carrying out material characteristic measurement calculation on the 5G circuit board, and carrying out material database construction to obtain an enhanced material attribute matrix; carrying out simplified model construction on the 5G circuit board, and carrying out boundary condition and heat source definition to obtain a simplified CAD model and boundary condition and heat source definition data; carrying out heat transfer equation solving on the simplified CAD model according to boundary conditions and heat source definition data, and carrying out temperature field calculation to obtain temperature field distribution data; and performing phase change latent heat influence analysis on the simplified CAD model according to the temperature field distribution data to obtain phase change latent heat data. Through a 5G circuit board thermal simulation technology, higher simulation precision, higher simulation speed and higher design automation capability are realized.

Description

Technical Field

[0001] The present invention relates to the field of 5G circuit board thermal simulation technology, and in particular to a thermal simulation method, system and circuit board based on a 5G circuit board. Background Art

[0002] The rapid development of 5G technology has put forward higher requirements for the heat dissipation of electronic equipment. 5G circuit boards have higher operating frequencies, higher integration, and greater power consumption density, which leads to a significant increase in heat generation. When components on circuit boards work at high temperatures, their performance will decline or even fail. Heat dissipation has become a key factor restricting the performance and reliability of 5G equipment.

[0003] Existing circuit board thermal simulation technology mainly uses simplified models and empirical formulas for estimation. For example, components are regarded as simple heat sources, and the internal structure of the PCB and the anisotropy of the material are ignored. This method has fast calculation speed, but low accuracy and cannot meet the needs of 5G circuit boards. Complex PCB structures require fine mesh division, resulting in huge calculation volume, long simulation time and low efficiency. Summary of the invention

[0004] Based on this, it is necessary to provide a thermal simulation method, system and circuit board based on a 5G circuit board to solve at least one of the above technical problems.

[0005] To achieve the above purpose, a thermal simulation method based on a 5G circuit board includes the following steps: Step S1: measuring and calculating the material properties of the 5G circuit board to obtain thermal conductivity characteristic data and interface thermal resistance data; constructing a material database based on the thermal conductivity characteristic data and the interface thermal resistance data to obtain an enhanced material property matrix; Step S2: construct a simplified model of the 5G circuit board, and define boundary conditions and heat sources to obtain a simplified CAD model and boundary conditions and heat source definition data; solve the heat transfer equation of the simplified CAD model according to the boundary conditions and heat source definition data, and calculate the temperature field to obtain temperature field distribution data; perform phase change latent heat influence analysis on the simplified CAD model according to the temperature field distribution data to obtain phase change latent heat data; perform entropy increase and thermal impedance analysis based on transient heat sources according to the phase change latent heat data to obtain path entropy value-added data and path thermal impedance data; extract key paths according to the path entropy value-added data and path thermal impedance data, and identify hot spots to obtain a key heat dissipation path diagram; Step S3: performing initial grid division according to the key heat dissipation path diagram, and performing adaptive grid optimization to obtain an adaptive grid structure; performing grid iterative optimization according to the adaptive grid structure to obtain an optimized grid structure; Step S4: using the optimized grid structure to extract thermal impedance data under different working conditions to obtain a thermal impedance data set; constructing a thermal impedance prediction model based on the thermal impedance data set to obtain a thermal impedance prediction model; using the thermal impedance prediction model to perform rapid temperature field calculations to obtain a rapid temperature field distribution; performing heat dissipation performance analysis on the rapid temperature field distribution, and performing design iterations and optimization to obtain an optimized design solution.

[0006] The present invention establishes a comprehensive material database (EMPM) by combining macroscopic measurement and microscopic simulation, which includes the thermal conductivity characteristics and interface thermal resistance data of commonly used materials for 5G circuit boards. This not only improves the accuracy of material parameters, but also provides a reliable data basis for subsequent simulation analysis, thereby improving the accuracy and credibility of the overall simulation. By constructing a simplified model, defining boundary conditions and heat sources, and combining phase change latent heat and transient heat source analysis, a more realistic simulation of the thermal behavior of 5G circuit boards is achieved. In particular, entropy increase and thermal impedance analysis can more accurately identify heat dissipation bottlenecks and critical paths, providing precise guidance for subsequent optimization design. The adaptive grid optimization strategy based on the key heat dissipation path is adopted to significantly improve the simulation efficiency while ensuring the simulation accuracy. By encrypting the grid in the key area and sparsely gridding in the non-key area, the number of grids can be effectively controlled, and the computing resources can be concentrated in the area with the greatest impact on the temperature field, so as to obtain more accurate results in a shorter time. The use of a neural network model to predict thermal impedance avoids repeated time-consuming finite element simulations and greatly speeds up the design iteration. The prediction model constructed by training data can quickly evaluate the heat dissipation performance of different design solutions, so as to efficiently find the best design solution, shorten the design cycle, and reduce R&D costs. Therefore, the present invention provides a thermal simulation method based on 5G circuit boards, which effectively solves the accuracy and efficiency problems faced by existing methods in the thermal design of 5G circuit boards. Through more accurate material property description, more comprehensive heat dissipation path analysis, smarter meshing and faster thermal impedance prediction, higher simulation accuracy, faster simulation speed and stronger design automation capabilities are achieved.

[0007] Preferably, step S1 comprises the following steps: Step S11: measuring the material properties of the 5G circuit board to obtain a material property table; Step S12: performing energy-minimized material NPT ensemble simulation according to the material property table to obtain equilibrium atomic trajectory data; Step S13: Calculate the heat conduction characteristics according to the equilibrium atomic trajectory data to obtain heat conduction characteristics data; Step S14: Calculate the interface thermal resistance according to the equilibrium atomic trajectory data to obtain the interface thermal resistance data; Step S15: Modeling and calibrating nanoscale material properties according to the heat conduction property data and the interface thermal resistance data to obtain a nanoscale material property table; Step S16: Perform data fusion and calibration according to the material property table and the nanoscale material property table to obtain an enhanced material property matrix.

[0008] The present invention obtains real material thermal property data, such as thermal conductivity, specific heat capacity and density, by actually measuring the commonly used materials of 5G circuit boards, avoiding the errors caused by using literature values ​​or empirical values, providing reliable basic data for subsequent simulation analysis, and ensuring the accuracy of the simulation results. The material is energy minimized using the NPT ensemble simulation to obtain the atomic trajectory data of the material in equilibrium. This provides the necessary data basis for the subsequent calculation of nanoscale thermal conductivity characteristics and interface thermal resistance, and can more deeply understand the influence of the microstructure of the material on its thermal properties. By analyzing the equilibrium atomic trajectory data, the thermal conductivity characteristic data of the material, such as thermal conductivity, is calculated. This makes up for the shortcomings of the macroscopic measurement method at the nanoscale, can more accurately describe the thermal conduction behavior of the material at the microscale, and improves the simulation accuracy. Using the equilibrium atomic trajectory data, the thermal resistance data of the material interface is calculated. This provides key parameters for accurately simulating heat transfer between different materials, can more realistically reflect the heat conduction at the interface of different materials in the 5G circuit board, and further improves the simulation accuracy. By modeling and calibrating the nanoscale thermal conductivity and interface thermal resistance data, a more accurate nanoscale material property model was established. This enables a more accurate description of the thermal behavior of materials at the nanoscale and provides more reliable material parameters for macroscale simulations. The macroscale measurement data and nanoscale simulation data were fused and calibrated to construct an enhanced material property matrix (EMPM). This combines material property information at two different scales, provides more comprehensive and accurate material parameters, lays a solid foundation for subsequent thermal simulation analysis, and effectively improves the overall simulation accuracy.

[0009] Preferably, step S2 comprises the following steps: Step S21: Obtain a 5G circuit board CAD model, and perform model simplification processing to obtain a simplified CAD model; Step S22: defining boundary conditions and heat sources according to the enhanced material property matrix and the simplified CAD model to obtain boundary condition and heat source definition data; Step S23: solving the heat transfer equation of the simplified CAD model according to the boundary conditions and the heat source definition data, and performing temperature field calculation to obtain temperature field distribution data; Step S24: performing phase change latent heat influence analysis on the simplified CAD model according to the temperature field distribution data to obtain phase change latent heat data; performing entropy increase and thermal impedance analysis based on transient heat source according to the phase change latent heat data to obtain path entropy value data and path thermal impedance data; Step S25: extract the key path according to the path entropy value-added data and the path thermal impedance data, and identify the hot spots to obtain a key heat dissipation path diagram.

[0010] The present invention simplifies the CAD model, removes the details that have little impact on the thermal simulation results, significantly reduces the complexity and calculation amount of the model while ensuring the simulation accuracy, improves the simulation efficiency, and makes the subsequent meshing and simulation calculation faster and more stable. Accurately defining boundary conditions and heat sources is the key to thermal simulation. This step defines the air velocity and temperature of forced convection, as well as parameters such as the average power consumption and fluctuation amplitude of the chip according to the actual working environment and chip power consumption data, providing the necessary input conditions for the subsequent solution of the heat transfer equation and ensuring the accuracy and reliability of the simulation results. By solving the heat transfer equation, the temperature field distribution data of the 5G circuit board is obtained, which is the basis for thermal analysis. This step provides a preliminary understanding of the overall thermal performance of the 5G circuit board, and provides the necessary data support for the subsequent phase change latent heat influence analysis and entropy increase / thermal impedance analysis. The influence of the latent heat effect of the phase change material on the temperature field is considered, and combined with the characteristics of the transient heat source, an analysis based on entropy increase and thermal impedance is carried out. This makes the simulation results closer to the actual working conditions of the 5G circuit board, and can more accurately identify the heat dissipation bottlenecks and critical paths, providing more precise guidance for subsequent optimization design. By analyzing the path entropy value-added data and path thermal impedance data, the key heat dissipation paths are extracted and the hot spots on the 5G circuit board are identified. This provides a clear goal for subsequent grid optimization and design optimization, and can make more targeted improvements to improve heat dissipation efficiency. The key heat dissipation path diagram intuitively shows the main paths and heat dissipation bottlenecks of heat transfer.

[0011] Preferably, step S24 includes the following steps: Step S241: identifying the phase change material according to the enhanced material property matrix to obtain the phase change material; performing path selection and unit division according to the phase change material and the simplified CAD model to obtain path unit information; Step S242: judging the phase change state according to the temperature field distribution data and the phase change material to obtain a phase change state mark; calculating the phase change latent heat according to the temperature field distribution data and the phase change material to obtain the phase change latent heat data; Step S243: performing material thermal response analysis according to the enhanced material property matrix to obtain material thermal response data; Step S244: performing heat flux calculation according to the temperature field distribution data, the phase change latent heat data and the material thermal response data to obtain heat flux data; Step S245: Calculate the unit entropy increase rate according to the heat flux density data to obtain the unit entropy increase rate data; perform path entropy value-added integration according to the unit entropy increase rate data and the path unit information to obtain the path entropy value-added data; Step S246: Calculate the thermal impedance of the component according to the temperature field distribution data to obtain the thermal impedance data of the component; calculate the thermal impedance of the heat dissipation path according to the heat flux density data and the thermal impedance data of the component to obtain the thermal impedance data of the path.

[0012] The present invention lays a foundation for the subsequent calculation of phase change latent heat and entropy increase / thermal impedance analysis by identifying phase change materials and performing path selection and unit division. The acquisition of path unit information enables a more detailed analysis of the heat transfer process on the critical path, thereby more accurately identifying the heat dissipation bottleneck. Accurately judging the state of the phase change material and calculating its latent heat value is crucial for simulating the real heat transfer process. This step takes into account the heat absorption and heat release effects of the phase change material at different temperatures, and improves the accuracy of the simulation results, especially in the presence of temperature fluctuations. By analyzing the thermal response characteristics of the material, such as thermal diffusivity, the response speed of the material to temperature changes can be better understood. This helps to more accurately simulate the influence of transient heat sources, and correct the heat flux calculation to improve the reliability of the simulation results. Taking into account factors such as heat conduction, phase change latent heat and material thermal response, more accurate heat flux data is calculated. This provides a more reliable data basis for subsequent entropy increase rate calculation and thermal impedance calculation, making the analysis results more representative. By calculating the unit entropy increase rate and path entropy increase value, the irreversible loss in the heat transfer process can be quantitatively analyzed, thereby identifying the heat dissipation bottleneck and optimizing the heat dissipation path. Entropy increase analysis provides a new perspective for thermal design and can more effectively guide the optimization of heat dissipation solutions. Calculating the thermal impedance of components and the thermal impedance of heat dissipation paths can evaluate the efficiency of different heat dissipation paths and provide a reference for subsequent design optimization. By analyzing the thermal impedance data, the path with the largest thermal resistance can be found, so that more targeted improvements can be made to reduce thermal resistance and improve heat dissipation efficiency.

[0013] Preferably, step S244 is specifically as follows: Calculate the heat flux density of heat conduction according to the temperature field distribution data to obtain the heat flux density data of unit heat conduction; Calculate the heat flux density of convective heat transfer according to the temperature field distribution data to obtain the heat flux density data of unit convective heat transfer; Calculate the heat flux density of radiation heat transfer according to the temperature field distribution data to obtain the heat flux density data of unit radiation heat transfer; Perform heat flux superposition on unit heat conduction heat flux data, unit convection heat transfer heat flux data and unit radiation heat transfer heat flux data to obtain heat flux superposition data; Perform transient heat source impact analysis based on temperature field distribution data to obtain transient heat source impact data; According to the transient heat source influence data, the heat flux density distribution inside the unit is carried out to obtain the local heat flux density influence data; The heat flux direction is corrected for the heat flux superposition data according to the local heat flux influence data to obtain the heat flux data.

[0014] The present invention can accurately describe the heat conduction process inside the solid material by calculating the heat flux density of each unit of heat conduction, which is the basis of thermal analysis and provides an important data basis for the subsequent calculation of the total heat flux density and the analysis of the heat transfer path. Calculating the heat flux density of convection heat transfer can accurately describe the heat exchange process between the 5G circuit board and the surrounding environment, which is crucial for evaluating the performance of the radiator and optimizing the heat dissipation design. Considering the influence of radiation heat transfer, the heat transfer process can be simulated more comprehensively, especially under high temperature conditions, the role of radiation heat transfer cannot be ignored. Calculating the heat flux density of radiation heat transfer improves the accuracy and reliability of the simulation results. Superimposing the heat flux densities of the three heat exchange modes, more comprehensive heat flux density data is obtained, which can more accurately reflect the complex heat transfer process in the 5G circuit board. Considering the influence of transient heat sources, the thermal behavior of the 5G circuit board in the actual working environment can be more realistically simulated, because the power consumption of the chip often changes over time. This is crucial for evaluating the effectiveness of the heat dissipation solution and predicting the maximum temperature of the device. By analyzing the impact of transient heat sources on the heat flux density distribution inside the unit, the dynamic thermal behavior of the heat source can be simulated more precisely, thereby more accurately predicting the temperature change in the heat source area. By correcting the direction of the heat flux density, the flow direction of heat in the 5G circuit board can be more accurately described, which is crucial for identifying key heat dissipation paths and optimizing heat dissipation design, and can more effectively guide heat transfer from the heat source to the radiator.

[0015] Preferably, the transient heat source impact analysis is performed based on the temperature field distribution data as follows: The heat source position is identified according to the temperature field distribution data and the simplified CAD model to obtain the heat source position identification data; Determine the geometric shape according to the heat source position identification data to obtain the heat source geometric information; According to the heat source geometry information, the heat source power is analyzed over time to obtain the heat source power time function; Selecting a transient heat source model according to the heat source geometry information to obtain a selected transient heat source model; The transient heat source heat flux density is calculated according to the selected transient heat source model and the heat source power-time function to obtain the transient heat source impact data.

[0016] The present invention accurately identifies the location of the heat source as a prerequisite for transient heat source impact analysis. This step accurately locates the location and geometric information of the heat source by combining the temperature field distribution data and the simplified CAD model, providing the necessary spatial information for subsequent analysis. Determining the geometric shape and size of the heat source can more accurately calculate the volume and surface area of ​​the heat source, which is crucial for subsequent calculation of heat flux density and thermal analysis, and avoids errors caused by inaccurate geometric information. By analyzing the change law of heat source power over time, a heat source power-time function is established, which can more realistically simulate the thermal behavior of the 5G circuit board in actual work, because the power consumption of the chip is usually not constant, but varies with time. Selecting a suitable transient heat source model, such as a body heat source model, can more accurately describe the thermal characteristics of the heat source, simplify the calculation process, improve the simulation efficiency, and ensure the reliability of the simulation results. By calculating the heat flux density of the transient heat source, the heat output of the heat source at different times can be more accurately described, which is crucial for simulating the dynamic thermal behavior of the 5G circuit board and predicting the transient temperature response of the device, and can more effectively evaluate the performance of the heat dissipation solution.

[0017] Preferably, step S3 comprises the following steps: Step S31: performing initial mesh division according to the key heat dissipation path diagram and the simplified CAD model to obtain an initial mesh structure; Step S32: performing finite element simulation on the initial grid structure according to the enhanced material property matrix, boundary conditions and heat source definition data, and performing error analysis to obtain preliminary temperature field data; Step S33: selecting an encrypted area according to the preliminary temperature field data to obtain an encrypted area; Step S34: selecting a sparse area according to the path entropy value-added data and the path thermal impedance data to obtain a sparse area; Step S35: applying the grid size control parameter to the dense area and the sparse area to obtain the grid size control parameter; Step S36: setting encryption and sparse thresholds according to the grid size control parameter to obtain a grid adjustment strategy; Step S37: performing mesh adjustment and iterative optimization on the simplified CAD model according to the mesh adjustment strategy to obtain an adaptive mesh structure; Step S38: performing mesh iterative optimization according to the enhanced material property matrix and the adaptive mesh structure to obtain an optimized mesh structure.

[0018] The present invention can effectively control the number of grids and improve the simulation efficiency under the premise of ensuring the simulation accuracy by performing initial grid division according to the key heat dissipation path diagram, and lay the foundation for subsequent adaptive grid optimization. Performing preliminary simulation and analyzing errors can understand the accuracy of the initial grid and provide guidance for subsequent grid optimization, thereby improving the grid quality in a targeted manner and improving the reliability of the simulation results. By analyzing the preliminary temperature field data, the area with a large temperature gradient, that is, the area with drastic heat flow changes, is identified and marked as an encrypted area, providing a clear target for subsequent grid encryption. By analyzing the path entropy value-added and thermal impedance data, the area with a small contribution to the overall heat dissipation is identified and marked as a sparse area, which can reduce the number of grids and improve the simulation efficiency without affecting the simulation accuracy. Applying different grid size control parameters to the encrypted area and the sparse area can achieve local encryption and sparseness of the grid, thereby improving the overall simulation efficiency under the premise of ensuring the simulation accuracy of the key area. Setting the encryption and sparse thresholds can automatically determine which units need to be encrypted or sparse according to indicators such as temperature gradient, entropy value-added and thermal impedance value, thereby achieving adaptive adjustment of the grid. Through iterative optimization, the grid is gradually adjusted to make the grid distribution more reasonable and better adapt to the changes in the temperature field, thereby improving the simulation accuracy and making the simulation results more convergent and stable. The final iterative optimization of the grid ensures that the final grid can accurately capture the details of the temperature field and make the simulation results meet the preset accuracy requirements, providing reliable grid data for subsequent thermal impedance prediction and design optimization.

[0019] Preferably, step S4 comprises the following steps: Step S41: using the optimized grid structure and the enhanced material property matrix to perform operating condition design and obtain an operating condition parameter table; Step S42: extracting thermal impedance data according to the operating condition parameter table to obtain a thermal impedance data set; Step S43: constructing a thermal impedance prediction model based on a neural network according to the thermal impedance data set to obtain a thermal impedance prediction model; Step S44: obtaining the working condition data of the 5G circuit board; inputting the working condition data of the 5G circuit board into the thermal impedance prediction model, performing rapid temperature field calculation, and obtaining rapid temperature field distribution; Step S45: performing heat dissipation performance analysis on the rapid temperature field distribution to obtain a heat dissipation performance evaluation report; Step S46: performing design iteration and optimization according to the heat dissipation performance evaluation report, the simplified CAD model, and the enhanced material property matrix to obtain an optimized design solution.

[0020] By designing multiple groups of different operating parameters, the present invention can comprehensively cover various working environments and load conditions that 5G circuit boards may encounter, thereby training a more generalized neural network model, which can accurately predict the thermal impedance under different working conditions. Extracting thermal impedance data under different working conditions and constructing a thermal impedance data set provide necessary training data for training the neural network model. The diversity and integrity of the data directly affect the prediction accuracy and generalization ability of the model. Constructing a thermal impedance prediction model based on a neural network can quickly and accurately predict the thermal impedance under different working conditions, avoiding repeated time-consuming finite element simulations and significantly improving the design efficiency. Using the trained neural network model, the temperature field distribution of the 5G circuit board under actual working conditions can be quickly calculated, providing fast and accurate temperature data for heat dissipation performance analysis, and shortening the design cycle. By analyzing the fast temperature field distribution, it is possible to evaluate whether the heat dissipation performance of the 5G circuit board meets the design requirements, identify potential heat dissipation problems, and provide guidance for design optimization. According to the heat dissipation performance evaluation report, combined with the simplified CAD model and enhanced material property matrix, the design of the 5G circuit board can be iteratively optimized, such as adjusting the size of the radiator, changing the material or improving the heat dissipation solution, and finally obtaining an optimized design solution that meets the heat dissipation requirements.

[0021] Preferably, the present invention also provides a thermal simulation system based on a 5G circuit board, which is used to execute the thermal simulation method based on a 5G circuit board as described above, and the thermal simulation system based on a 5G circuit board includes: The material database construction module is used to measure and calculate the material properties of the 5G circuit board to obtain the thermal conductivity characteristic data and the interface thermal resistance data; the material database is constructed based on the thermal conductivity characteristic data and the interface thermal resistance data to obtain the enhanced material property matrix; The topological structure entropy analysis module is used to construct a simplified model of the 5G circuit board, and define boundary conditions and heat sources to obtain a simplified CAD model and boundary conditions and heat source definition data; solve the heat transfer equation of the simplified CAD model according to the boundary conditions and heat source definition data, and calculate the temperature field to obtain temperature field distribution data; analyze the influence of phase change latent heat on the simplified CAD model according to the temperature field distribution data to obtain phase change latent heat data; perform entropy increase and thermal impedance analysis based on transient heat sources according to the phase change latent heat data to obtain path entropy value-added data and path thermal impedance data; extract key paths according to the path entropy value-added data and path thermal impedance data, and identify hot spots to obtain a key heat dissipation path diagram; The adaptive grid optimization module is used to perform initial grid division according to the key heat dissipation path diagram, and perform adaptive grid optimization to obtain an adaptive grid structure; perform grid iterative optimization according to the adaptive grid structure to obtain an optimized grid structure; The neural network thermal impedance prediction module is used to extract thermal impedance data under different working conditions using an optimized grid structure to obtain a thermal impedance data set; to construct a thermal impedance prediction model based on the thermal impedance data set to obtain a thermal impedance prediction model; to perform rapid temperature field calculations using the thermal impedance prediction model to obtain a rapid temperature field distribution; to perform heat dissipation performance analysis on the rapid temperature field distribution, and to perform design iterations and optimization to obtain an optimized design solution.

[0022] Preferably, the circuit board is manufactured by the thermal simulation method based on the 5G circuit board as described. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 A schematic diagram of the steps of a thermal simulation method based on a 5G circuit board; Figure 2 It is a schematic diagram of the detailed implementation steps of step S2 in the present invention.

[0024] The purpose, features and advantages of the present invention will be further described with reference to the accompanying drawings in conjunction with the embodiments. DETAILED DESCRIPTION

[0025] The technical method of the present invention is described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are 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 technicians in this field without creative work are within the scope of protection of the present invention.

[0026] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor methods and / or microcontroller methods.

[0027] It should be understood that, although the terms "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are used only to distinguish one unit from another unit. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0028] To achieve this, please refer to Figure 1 to Figure 2 , a thermal simulation method based on a 5G circuit board, comprising the following steps: Step S1: measuring and calculating the material properties of the 5G circuit board to obtain thermal conductivity characteristic data and interface thermal resistance data; constructing a material database based on the thermal conductivity characteristic data and the interface thermal resistance data to obtain an enhanced material property matrix; Step S2: construct a simplified model of the 5G circuit board, and define boundary conditions and heat sources to obtain a simplified CAD model and boundary conditions and heat source definition data; solve the heat transfer equation of the simplified CAD model according to the boundary conditions and heat source definition data, and calculate the temperature field to obtain temperature field distribution data; perform phase change latent heat influence analysis on the simplified CAD model according to the temperature field distribution data to obtain phase change latent heat data; perform entropy increase and thermal impedance analysis based on transient heat sources according to the phase change latent heat data to obtain path entropy value-added data and path thermal impedance data; extract key paths according to the path entropy value-added data and path thermal impedance data, and identify hot spots to obtain a key heat dissipation path diagram; Step S3: performing initial grid division according to the key heat dissipation path diagram, and performing adaptive grid optimization to obtain an adaptive grid structure; performing grid iterative optimization according to the adaptive grid structure to obtain an optimized grid structure; Step S4: using the optimized grid structure to extract thermal impedance data under different working conditions to obtain a thermal impedance data set; constructing a thermal impedance prediction model based on the thermal impedance data set to obtain a thermal impedance prediction model; using the thermal impedance prediction model to perform rapid temperature field calculations to obtain a rapid temperature field distribution; performing heat dissipation performance analysis on the rapid temperature field distribution, and performing design iterations and optimization to obtain an optimized design solution.

[0029] In the embodiment of the present invention, reference Figure 1 As shown, it is a schematic diagram of the step flow of the thermal simulation method based on the 5G circuit board of the present invention. In this example, the thermal simulation method based on the 5G circuit board includes the following steps: Step S1: measuring and calculating the material properties of the 5G circuit board to obtain thermal conductivity characteristic data and interface thermal resistance data; constructing a material database based on the thermal conductivity characteristic data and the interface thermal resistance data to obtain an enhanced material property matrix; In the embodiment of the present invention, thermal properties of various materials used in 5G circuit boards, such as FR-4 substrates, copper wires, chip packaging materials, and phase change heat dissipation materials, are measured. The thermal diffusivity and specific heat capacity are measured using a flash thermal conductivity meter, the density is measured using a balance, and the thermal conductivity is calculated. At the same time, molecular dynamics simulation is used to calculate the thermal conductivity characteristics and interface thermal resistance at the nanoscale. The macroscopic measurement data and nanoscale simulation data are fused and calibrated to finally construct an enhanced material property matrix (EMPM) containing the thermal properties of all materials.

[0030] Step S2: construct a simplified model of the 5G circuit board, and define boundary conditions and heat sources to obtain a simplified CAD model and boundary conditions and heat source definition data; solve the heat transfer equation of the simplified CAD model according to the boundary conditions and heat source definition data, and calculate the temperature field to obtain temperature field distribution data; perform phase change latent heat influence analysis on the simplified CAD model according to the temperature field distribution data to obtain phase change latent heat data; perform entropy increase and thermal impedance analysis based on transient heat sources according to the phase change latent heat data to obtain path entropy value-added data and path thermal impedance data; extract key paths according to the path entropy value-added data and path thermal impedance data, and identify hot spots to obtain a key heat dissipation path diagram; In an embodiment of the present invention, the CAD model of the 5G circuit board is imported into SpaceClaim for simplification, and details that are not important for thermal analysis, such as screw holes and chamfers, are removed, and simplified geometric shapes are used to replace complex components to obtain a simplified CAD model. According to the actual working environment and chip power consumption data, boundary conditions and heat sources are defined in FloTHERM, including the air velocity and temperature of forced convection, and the average power consumption and fluctuation amplitude of the chip. FloTHERM is used to perform steady-state thermal simulation to obtain the initial temperature field distribution. Based on the initial temperature field distribution, the state of the phase change material is identified, and the latent heat of the phase change is calculated. The latent heat of the phase change is added to the model as an additional heat source or heat sink. Considering the transient characteristics of chip power consumption, a sine function is used to simulate its change over time, and a transient thermal simulation is performed. Based on the transient temperature field distribution, the entropy increase rate of each unit and the thermal impedance of the key heat dissipation path are calculated. According to the entropy increase and thermal impedance, the key heat dissipation path and hot spots are identified, and a key heat dissipation path diagram is generated.

[0031] Step S3: performing initial grid division according to the key heat dissipation path diagram, and performing adaptive grid optimization to obtain an adaptive grid structure; performing grid iterative optimization according to the adaptive grid structure to obtain an optimized grid structure; In an embodiment of the present invention, the simplified CAD model is imported into Ansys Meshing, and the initial meshing is performed according to the key heat dissipation path diagram. A smaller mesh size is used near the key heat dissipation path and important components, a larger mesh size is used in other areas, and a hybrid meshing method is used. The initial mesh is imported into Ansys Fluent, and material properties are assigned according to EMPM, and boundary conditions and heat sources are set. A steady-state thermal simulation is performed, and the error of the temperature field distribution is analyzed. According to the temperature gradient, entropy value, and thermal impedance value, the areas that need to be encrypted and sparse are selected, and the mesh size control parameters are adjusted. Repeat the mesh adjustment and simulation process until the error of the temperature field distribution meets the preset accuracy requirements and an optimized mesh structure is obtained.

[0032] Step S4: extracting thermal impedance data under different working conditions using an optimized grid structure to obtain a thermal impedance data set; constructing a thermal impedance prediction model based on the thermal impedance data set to obtain a thermal impedance prediction model; performing rapid temperature field calculation using the thermal impedance prediction model to obtain a rapid temperature field distribution; performing heat dissipation performance analysis on the rapid temperature field distribution, and performing design iteration and optimization to obtain an optimized design solution; In an embodiment of the present invention, an optimized grid structure and EMPM are used to design multiple groups of different working conditions, such as changing ambient temperature, wind speed, and heat source power. Each group of working conditions is simulated, the thermal impedance data of the heat source is extracted, and a thermal impedance data set is constructed. A multi-layer perceptron (MLP) neural network model is constructed using TensorFlow, with ambient temperature, wind speed, and heat source power as inputs and thermal impedance as output. The neural network model is trained using the thermal impedance data set. The actual working condition data of the 5G circuit board is obtained and input into the trained neural network model to predict the thermal impedance value. The predicted thermal impedance value is used for fast temperature field calculation, and heat dissipation performance analysis is performed. If the heat dissipation performance does not meet the design requirements, the design parameters are adjusted according to the analysis results, such as modifying the geometry, replacing the material, or optimizing the heat dissipation solution, and the simulation and optimization process is repeated until the design goal is achieved.

[0033] Preferably, step S1 comprises the following steps: Step S11: measuring the material properties of the 5G circuit board to obtain a material property table; Step S12: performing energy-minimized material NPT ensemble simulation according to the material property table to obtain equilibrium atomic trajectory data; Step S13: Calculate the heat conduction characteristics according to the equilibrium atomic trajectory data to obtain heat conduction characteristics data; Step S14: Calculate the interface thermal resistance according to the equilibrium atomic trajectory data to obtain the interface thermal resistance data; Step S15: Modeling and calibrating nanoscale material properties according to the heat conduction property data and the interface thermal resistance data to obtain a nanoscale material property table; Step S16: Perform data fusion and calibration according to the material property table and the nanoscale material property table to obtain an enhanced material property matrix.

[0034] In an embodiment of the present invention, for a 5G circuit board, the key materials that need to be thermally simulated are first determined, such as PCB substrate materials (FR-4, Rogers, etc.), chip packaging materials, heat sink materials (copper, aluminum, graphene, etc.) and thermal interface materials. The thermal diffusivity and specific heat capacity of the material are measured using a Netzsch LFA 467 NanoFlash flash thermal conductivity meter. The material to be tested is cut into round samples with a diameter of 12.7 mm and a thickness of 1-3 mm. Before the measurement, a layer of graphite is sprayed on the surface of the sample to increase the absorption rate. The measurement temperature range is set to 25°C to 125°C, and the measurement is performed at intervals of 10°C. The measurement is repeated three times at each temperature point, and the average value is taken as the final result. The mass and size of the sample are measured using a Mettler Toledo XS205 balance to calculate the density of the sample. Substitute the measured thermal diffusivity, specific heat and density into the formula λ= α·ρ·c (λ is thermal conductivity, α is thermal diffusivity, ρ is density, c is specific heat) to calculate the thermal conductivity of the material. Record all measured data in the material property table, including the material name, thermal diffusivity, specific heat, density and calculated thermal conductivity, and mark the measurement error.

[0035] Select a key material in the material properties table, such as FR-4, and use Materials Studio software to build its atomic structure model. The main components of FR-4 are epoxy resin and glass fiber. In the simplified model, cross-linked epoxy resin molecular chains are used to represent the epoxy resin matrix, and amorphous silica molecules are embedded to represent the glass fiber reinforcement. Use the Forcite module for energy minimization, and use the COMPASS II force field to calculate the interaction between atoms. Set the convergence criteria for energy minimization to energy change less than 1.0×10^-5 kcal / mol and force less than 0.001 kcal / mol / Å. After energy minimization, use the Discover module to perform molecular dynamics simulation under the NPT ensemble. Set the simulation temperature to 298K, the pressure to 1atm, the time step to 1fs, and the total simulation time to 100ps. Save the atomic coordinates and velocity information every 100fs to obtain the equilibrium atomic trajectory data.

[0036] The thermal conductivity of the material is calculated using the equilibrium atomic trajectory data and the Green-Kubo formula. The atomic trajectory data is imported into the LAMMPS software, and the heat flux autocorrelation function is calculated using the fix heatflux command. The heat flux autocorrelation function is time-integrated to obtain the thermal conductivity value. The calculation is repeated three times, the average value is taken as the final result, and the calculation error is recorded.

[0037] Construct an atomic structure model that includes the interface between two materials, such as the interface between FR-4 and copper. Use the same NPT ensemble simulation method as step S12 to obtain equilibrium atomic trajectory data. Use the non-equilibrium molecular dynamics (NEMD) method to calculate the interfacial thermal resistance. During the simulation, a heat flow is applied to one of the materials and the temperature difference between the two materials is measured. The interfacial thermal resistance is calculated as R = ΔT / Q, where R is the interfacial thermal resistance, ΔT is the temperature difference, and Q is the heat flow.

[0038] The thermal conductivity and interface thermal resistance data calculated in steps S13 and S14 are recorded in the nanoscale material property table. Based on these data, a more accurate nanoscale material model can be established, for example, taking into account factors such as phonon scattering and interface effects. The model can be calibrated by comparing with experimental measurements or literature values ​​to improve the accuracy of the model.

[0039] The macroscopic material properties measured in step S11 and the nanoscale material properties calculated in step S15 are fused. For example, for thermal conductivity, a weighted average method can be used to combine the macroscopic measured values ​​and the nanoscale calculated values, and the weights can be determined based on the microstructure of the material and the measurement / calculation errors. The fused data is calibrated using existing experimental data or literature data to correct the deviation. Finally, an enhanced material property matrix (EMPM) is obtained, which contains the name of each material, the macroscopic and nanoscale thermal parameters, and the fused calibrated values ​​and error ranges.

[0040] Preferably, step S2 comprises the following steps: Step S21: Obtain a 5G circuit board CAD model, and perform model simplification processing to obtain a simplified CAD model; Step S22: defining boundary conditions and heat sources according to the enhanced material property matrix and the simplified CAD model to obtain boundary condition and heat source definition data; Step S23: solving the heat transfer equation of the simplified CAD model according to the boundary conditions and the heat source definition data, and performing temperature field calculation to obtain temperature field distribution data; Step S24: performing phase change latent heat influence analysis on the simplified CAD model according to the temperature field distribution data to obtain phase change latent heat data; performing entropy increase and thermal impedance analysis based on transient heat source according to the phase change latent heat data to obtain path entropy value data and path thermal impedance data; Step S25: extract the key path according to the path entropy value-added data and the path thermal impedance data, and identify the hot spots to obtain a key heat dissipation path diagram.

[0041] As an example of the present invention, refer to Figure 2 As shown, in this example, step S2 includes: Step S21: Obtain a 5G circuit board CAD model, and perform model simplification processing to obtain a simplified CAD model; In an embodiment of the present invention, a STEP format CAD model of a 5G circuit board is exported from Altium Designer. The STEP model is imported into SpaceClaim. Check the integrity of the model to ensure that there are no missing or erroneous geometric elements. Remove components that have little impact on the thermal simulation results, such as screw holes, test points, and decorative features on the housing. Simplify components with complex shapes, such as BGA packaged chips, into cuboids, retaining their dimensions and position information. For vias and through-hole structures on the PCB board, replace them with solid models of equivalent thermal conductivity according to their density and size. Simplify the copper routing network on the PCB into several representative copper-clad areas and assign corresponding thickness and conductivity. The simplified model retains all key components related to heat dissipation, such as chips, heat sinks, PCB substrates, etc., and ensures that their geometric shapes, sizes, and relative positions are accurate. Save the simplified model as a new STEP file, that is, the simplified CAD model.

[0042] Step S22: defining boundary conditions and heat sources according to the enhanced material property matrix and the simplified CAD model to obtain boundary condition and heat source definition data; In an embodiment of the present invention, the simplified CAD model is imported into FloTHERM. Boundary conditions are defined according to the actual working conditions and environmental conditions of the 5G circuit board. Assume that the circuit board works in a forced convection environment, set the wind speed to 3m / s, the wind direction is parallel to the surface of the circuit board, and the ambient temperature is 25°C. Enter these parameters into the corresponding boundary condition setting interface in FloTHERM. Define the heat source according to the power consumption data of the chip. For example, set the power consumption of the CPU chip to 3W, and apply the power consumption evenly to the simplified model surface corresponding to the CPU chip. Record the power, position, and effective area information of all heat sources in a table. Save the defined boundary conditions and heat source information as a file, namely the boundary conditions and heat source definition data.

[0043] Step S23: solving the heat transfer equation of the simplified CAD model according to the boundary conditions and the heat source definition data, and performing temperature field calculation to obtain temperature field distribution data; In an embodiment of the present invention, in FloTHERM, the simplified CAD model is meshed. Hexahedral meshes are mainly used, and local tetrahedral meshes are used for transition in areas with complex geometric shapes. The mesh size is set to ensure that the mesh density in key areas is high enough. The material properties in the enhanced material property matrix (EMPM) obtained in step S16 are assigned to the corresponding model components. Based on the defined boundary conditions and heat sources, FloTHERM automatically establishes heat transfer control equations, including three heat transfer modes: heat conduction, convection, and radiation. Select a steady-state solver and set the convergence accuracy to 10^-4. Run the solver to calculate the temperature field distribution. The calculated temperature field data, including the temperature value of each grid node, is exported as a data file, namely, the temperature field distribution data.

[0044] Step S24: performing phase change latent heat influence analysis on the simplified CAD model according to the temperature field distribution data to obtain phase change latent heat data; performing entropy increase and thermal impedance analysis based on transient heat source according to the phase change latent heat data to obtain path entropy value data and path thermal impedance data; In the embodiment of the present invention, it is assumed that phase change heat dissipation materials are used in 5G circuit boards. According to the temperature field distribution data, the state of the phase change material (solid or liquid) is determined. If the phase change material is within the phase change temperature range, the latent heat absorbed or released is calculated. The calculation formula for latent heat is Q = m·L, where Q is the latent heat, m is the mass of the phase change material, and L is the phase change latent heat coefficient. The calculated latent heat data is added to the thermal simulation model as an additional heat source or heat sink. Assuming that the power consumption of the CPU chip changes with time, a sinusoidal function is used to simulate its transient characteristics, for example, P(t) = P_avg + P_amp* sin(ωt), where P_avg is the average power consumption, P_amp is the power consumption fluctuation amplitude, ω is the angular frequency, and t is time. Add the transient heat source to the FloTHERM model. Recalculate the temperature field distribution, and calculate the entropy increase rate of each unit and the thermal impedance of the key heat dissipation path based on the new temperature field distribution. Save the calculated path entropy increase data and path thermal impedance data separately.

[0045] Step S25: extracting key paths according to the path entropy value-added data and the path thermal impedance data, and identifying hot spots to obtain a key heat dissipation path diagram; In the embodiment of the present invention, the path with the largest entropy increase, i.e., the heat dissipation bottleneck, is identified based on the path entropy value data. The path with the largest thermal impedance is identified based on the path thermal impedance data, which also represents the path with low heat dissipation efficiency. These paths are marked as critical heat dissipation paths. The area with the highest temperature, i.e., the hot spot, is identified based on the temperature field distribution data. The critical heat dissipation paths and the hot spot positions are marked on the simplified CAD model to generate a critical heat dissipation path diagram.

[0046] Preferably, step S24 includes the following steps: Step S241: identifying the phase change material according to the enhanced material property matrix to obtain the phase change material; performing path selection and unit division according to the phase change material and the simplified CAD model to obtain path unit information; Step S242: judging the phase change state according to the temperature field distribution data and the phase change material to obtain a phase change state mark; calculating the phase change latent heat according to the temperature field distribution data and the phase change material to obtain the phase change latent heat data; Step S243: performing material thermal response analysis according to the enhanced material property matrix to obtain material thermal response data; Step S244: performing heat flux calculation according to the temperature field distribution data, the phase change latent heat data and the material thermal response data to obtain heat flux data; Step S245: Calculate the unit entropy increase rate according to the heat flux density data to obtain the unit entropy increase rate data; perform path entropy value-added integration according to the unit entropy increase rate data and the path unit information to obtain the path entropy value-added data; Step S246: Calculate the thermal impedance of the component according to the temperature field distribution data to obtain the thermal impedance data of the component; calculate the thermal impedance of the heat dissipation path according to the heat flux density data and the thermal impedance data of the component to obtain the thermal impedance data of the path.

[0047] In an embodiment of the present invention, each material record in the enhanced material property matrix (EMPM) is traversed. If the name of the material contains the word "phase change" or its latent heat value is greater than 100 J / g, the material is identified as a phase change material, and its name, phase change temperature range and latent heat value are recorded. In the simplified CAD model, the area containing the phase change material is identified. Taking the CPU chip as the heat source and the heat sink as the heat sink as an example, a straight line path from the center of the CPU chip to the bottom of the heat sink is defined. The path is discretized, and a series of cubic units are constructed with an interval of 0.1mm, and the points on the path are used as unit centers, and the side length of each unit is 0.1mm. The center coordinates, material type and number of the path to which each unit belongs are recorded, and this information is stored as path unit information.

[0048] For each path unit, the temperature value of the unit is extracted from the temperature field distribution data according to its center coordinates. The temperature value is compared with the phase change temperature range of the phase change material. If the unit temperature is lower than the lower limit of the phase change temperature range, the unit is marked as solid; if the unit temperature is higher than the upper limit of the phase change temperature range, the unit is marked as liquid; if the unit temperature is within the phase change temperature range, the unit is marked as a phase change state. For units in a phase change state, their latent heat values ​​are calculated according to their temperature values ​​and the latent heat curve of the phase change material. The calculation formula for latent heat can use a linear interpolation method to calculate the corresponding latent heat value according to the position of the unit temperature in its phase change temperature range. The phase change state mark and latent heat value of each unit are stored as phase change latent heat data.

[0049] For each material in the EMPM, calculate its thermal diffusivity. The thermal diffusivity is calculated as α = λ / (ρ·c), where α is the thermal diffusivity, λ is the thermal conductivity, ρ is the density, and c is the specific heat capacity. Thermal diffusivity reflects the response speed of a material to temperature changes. Record the thermal diffusivity of each material as the material thermal response data.

[0050] For each path unit, the heat flux density of thermal conduction is calculated using Fourier's law according to its temperature value and the temperature value of the adjacent unit. The calculation formula of the heat flux density of thermal conduction is q = -λ·∇T, where q is the heat flux density, λ is the thermal conductivity, and ∇T is the temperature gradient. For units containing phase change materials, the latent heat value calculated in step S242 is converted into an equivalent heat flux density and superimposed on the heat flux density of thermal conduction. According to the thermal response data of the material, the heat flux density is corrected to take into account the thermal inertia effect of the material. The heat flux density vector of each unit is recorded as heat flux density data.

[0051] For each path unit, calculate its entropy increase rate according to its heat flux and temperature. The calculation formula of entropy increase rate is ds / dt = q / T, where ds / dt is the entropy increase rate, q is the heat flux, and T is the temperature. The entropy increase rate of each unit is recorded as unit entropy increase rate data. For each path, the entropy increase rates of all units on the path are integrated to obtain the path entropy value. The integration method can adopt a numerical integration method, such as the trapezoidal formula or the Simpson formula. The entropy value of each path is recorded as the path entropy value data.

[0052] According to the temperature field distribution data, the average temperature and ambient temperature of the CPU chip are extracted. The calculation formula of the component thermal impedance is R = (T_chip - T_amb) / P, where R is the component thermal impedance, T_chip is the average chip temperature, T_amb is the ambient temperature, and P is the chip power consumption. The calculated component thermal impedance is recorded as the component thermal impedance data. For each path, the path thermal impedance is calculated based on the heat flux density and component thermal impedance on the path. The calculation formula of the path thermal impedance is R_path = ∫(1 / λ)dx + R_interface + R_component, where R_path is the path thermal impedance, λ is the thermal conductivity of the material on the path, x is the path length, R_interface is the interface thermal resistance, and R_component is the component thermal impedance. The thermal impedance of each path is recorded as the path thermal impedance data.

[0053] Preferably, step S244 is specifically as follows: Calculate the heat flux density of heat conduction according to the temperature field distribution data to obtain the heat flux density data of unit heat conduction; Calculate the heat flux density of convective heat transfer according to the temperature field distribution data to obtain the heat flux density data of unit convective heat transfer; Calculate the heat flux density of radiation heat transfer according to the temperature field distribution data to obtain the heat flux density data of unit radiation heat transfer; Perform heat flux superposition on unit heat conduction heat flux data, unit convection heat transfer heat flux data and unit radiation heat transfer heat flux data to obtain heat flux superposition data; Perform transient heat source impact analysis based on temperature field distribution data to obtain transient heat source impact data; According to the transient heat source influence data, the heat flux density distribution inside the unit is carried out to obtain the local heat flux density influence data; The heat flux direction is corrected for the heat flux superposition data according to the local heat flux influence data to obtain the heat flux data.

[0054] In an embodiment of the present invention, for each grid unit, the central node temperature and the central node temperature of the six adjacent units (assuming a structured hexahedral grid is used) are obtained. The temperature gradient of each unit in the x, y, and z directions is calculated using the central difference method. For example, in the x direction, the temperature gradient calculation formula is ∇T_x = (T_x+Δx- T_x-Δx) / (2Δx), where T_x+Δx and T_x-Δx are the temperatures of the adjacent units in the x direction, respectively, and Δx is the length of the unit in the x direction. According to Fourier's law, the heat conduction heat flux density components of each unit in the x, y, and z directions are calculated: q_x = -λ·∇T_x, q_y = -λ·∇T_y, q_z = -λ·∇T_z, where λ is the thermal conductivity of the material in which the unit is located, taken from the enhanced material property matrix (EMPM). The three heat flux components (q_x, q_y, q_z) of each unit are combined into a vector as the heat conduction heat flux data of the unit and stored in the unit heat conduction heat flux data set.

[0055] For each unit located on the surface of the 5G circuit board, the convective heat flux density is calculated based on the pre-set convective heat transfer coefficient h and the ambient temperature T_amb, as well as the unit surface temperature T_surf (taking the temperature of the unit center node as an approximation). The calculation formula for convective heat flux density is q_conv = h·(T_surf - T_amb). The calculated heat flux value is used as the normal vector of the unit, pointing away from the board surface. For internal units, the convective heat flux density is zero. The convective heat flux density data of each unit is stored in the unit convective heat flux density data set.

[0056] For each unit located on the surface of the 5G circuit board, the radiation heat flux density is calculated according to the Stefan-Boltzmann law. The calculation formula for the radiation heat flux density is q_rad = ε·σ·(T_surf^4 - T_amb^4), where ε is the emissivity of the material where the unit is located, σ is the Stefan-Boltzmann constant, T_surf is the unit surface temperature, and T_amb is the ambient temperature. The calculated heat flux value is used as the normal vector of the unit, pointing away from the board surface. For internal units, the radiation heat flux density is zero. The radiation heat flux density data of each unit is stored in the unit radiation heat flux density data set.

[0057] For each unit, the three vectors of unit heat conduction heat flux, unit convection heat flux and unit radiation heat flux are superimposed to obtain the total heat flux of the unit. The total heat flux data of each unit is stored in the heat flux superposition data set.

[0058] According to the transient power consumption curve of the CPU chip, for example, P(t) = P_avg + P_amp * sin(ωt), calculate the power consumption value at each moment. Divide the power consumption value at each moment by the surface area of ​​the chip to obtain the transient heat flux density value. Store the heat flux density value at each moment in the transient heat source impact data set.

[0059] For the unit corresponding to the CPU chip, the transient heat flux density value is evenly distributed inside the chip unit according to the transient heat source impact data. Assuming that the chip unit is further subdivided into several subunits, the transient heat flux density value is evenly distributed to each subunit. The heat flux density data of each subunit is stored in the local heat flux density impact data set.

[0060] For the unit corresponding to the CPU chip, the total heat flux direction of the unit in the heat flux superposition data is corrected according to the heat flux direction of each subunit in the local heat flux impact data. For example, the average vector of the heat flux directions of all subunits can be calculated, and the average vector can be used as the corrected total heat flux direction. The corrected heat flux data is stored in the final heat flux data set.

[0061] Preferably, the transient heat source impact analysis is performed based on the temperature field distribution data as follows: The heat source position is identified according to the temperature field distribution data and the simplified CAD model to obtain the heat source position identification data; Determine the geometric shape according to the heat source position identification data to obtain the heat source geometric information; According to the heat source geometry information, the heat source power is analyzed over time to obtain the heat source power time function; Selecting a transient heat source model according to the heat source geometry information to obtain a selected transient heat source model; The transient heat source heat flux density is calculated according to the selected transient heat source model and the heat source power-time function to obtain the transient heat source impact data.

[0062] In an embodiment of the present invention, the simplified CAD model is imported into a program library capable of geometric analysis, such as OpenCASCADE. All geometric entities in the simplified CAD model are traversed. For each entity, its predefined attributes in the model, such as name or label, are checked. If the name of the entity contains keywords such as "CPU" or "GPU", or its label is set to "heat source", the entity is identified as a heat source. The geometric center coordinates (x, y, z) and bounding box information of the heat source entity are extracted, including the minimum vertex coordinates (x_min, y_min, z_min) and maximum vertex coordinates (x_max, y_max, z_max) of the bounding box. The geometric center coordinates and bounding box information of the heat source are stored in the heat source position identification data structure.

[0063] According to the bounding box information in the heat source position identification data, calculate the size of the heat source, such as length L = x_max- x_min, width W = y_max - y_min, height H = z_max - z_min. Determine the shape type of the heat source based on the actual geometric shape of the heat source entity in the simplified CAD model. If the heat source entity is a cuboid, record its shape type as "cuboid" and record its length, width and height. If the heat source entity is a cylinder, record its shape type as "cylinder" and record its radius and height. Store the shape type and size information of the heat source in the heat source geometry information data structure.

[0064] Assume that the power of the heat source changes sinusoidally over time. Define the heat source power time function as P(t) = P_avg+ P_amp * sin(2πft + φ), where P_avg is the average power consumption, P_amp is the power consumption fluctuation amplitude, f is the frequency, φ is the phase, and t is the time. According to the actual working conditions of the 5G circuit board, set P_avg = 3W, P_amp = 1W, f= 1Hz, φ = 0. Substituting these parameters into the heat source power time function, we get P(t) = 3 + 1 * sin(2πt).

[0065] Since the geometric shape of the heat source is a cuboid or a cylinder, and its power varies sinusoidally with time, the body heat source model is selected as the transient heat source model. The body heat source model assumes that the heat is uniformly distributed within the volume of the heat source.

[0066] At each time step Δt, for example Δt = 0.01s, the instantaneous power P(t_i) of the heat source is calculated according to the heat source power time function P(t), where t_i = i * Δt and i is the index of the time step. Divide the instantaneous power P(t_i) by the volume V of the heat source to obtain the transient heat flux q(t_i) = P(t_i) / V. Calculate the volume V of the heat source according to the heat source geometry information. For example, if the heat source is a cuboid, V = L * W * H. If the heat source is a cylinder, V = πr^2h, where r is the radius and h is the height. Store the transient heat flux q(t_i) at each time step in the transient heat source influence data structure, which contains the time step index i and the corresponding heat flux value q(t_i).

[0067] Preferably, step S3 comprises the following steps: Step S31: performing initial mesh division according to the key heat dissipation path diagram and the simplified CAD model to obtain an initial mesh structure; Step S32: performing finite element simulation on the initial grid structure according to the enhanced material property matrix, boundary conditions and heat source definition data, and performing error analysis to obtain preliminary temperature field data; Step S33: selecting an encrypted area according to the preliminary temperature field data to obtain an encrypted area; Step S34: selecting a sparse area according to the path entropy value-added data and the path thermal impedance data to obtain a sparse area; Step S35: applying the grid size control parameter to the dense area and the sparse area to obtain the grid size control parameter; Step S36: setting encryption and sparse thresholds according to the grid size control parameter to obtain a grid adjustment strategy; Step S37: performing mesh adjustment and iterative optimization on the simplified CAD model according to the mesh adjustment strategy to obtain an adaptive mesh structure; Step S38: performing mesh iterative optimization according to the enhanced material property matrix and the adaptive mesh structure to obtain an optimized mesh structure.

[0068] In an embodiment of the present invention, the simplified CAD model is imported into the Ansys Meshing module. Based on the key heat dissipation path diagram, a smaller mesh size, such as 0.2 mm, is set near the key heat dissipation path and important components such as heat sources and radiators. In other areas, a larger mesh size is set, such as 0.5 mm. A hybrid meshing method is adopted, and hexahedral meshes are used in key areas and near important components to improve accuracy, and tetrahedral meshes are used in other areas to simplify the mesh generation process. Generate an initial mesh, and check the mesh quality to ensure that the mesh's aspect ratio, distortion, skewness and other indicators meet the simulation accuracy requirements. The generated initial mesh and information such as node coordinates, unit connection relationships, etc. are saved as an initial mesh structure data file.

[0069] Import the initial mesh structure into the Ansys Fluent module. According to the enhanced material property matrix (EMPM), assign corresponding material properties to each mesh element, including thermal conductivity, density, and specific heat capacity. Set the boundary conditions and heat sources according to the boundary conditions and heat source definition data. Select the steady-state heat conduction solver and set the convergence residual to 1e-6. Run the simulation calculation to obtain the preliminary temperature field distribution. Calculate the temperature gradient of each unit and compare the temperature difference between adjacent units. If the maximum temperature difference exceeds the preset threshold, such as 0.1°C, it is considered that the error is large and mesh encryption is required. Save the calculated temperature field distribution data and error analysis results as preliminary temperature field data files.

[0070] Analyze the preliminary temperature field data and calculate the temperature gradient of each unit. Mark the units whose temperature gradient exceeds the preset threshold, such as 10°C / mm, as units that need to be encrypted. Define the area where these units are located as the encrypted area, and record the boundary information of the encrypted area, such as the bounding box or geometric shape.

[0071] Analyze the path entropy value-added data and path thermal impedance data. Mark the cells on the path whose entropy value-added and thermal impedance values ​​are both lower than the preset threshold as cells that can be sparse. Define the area where these cells are located as a sparse area, and record the boundary information of the sparse area.

[0072] For the dense area, set a smaller grid size control parameter, such as 0.1 mm. For the sparse area, set a larger grid size control parameter, such as 1 mm. Store the grid size control parameter of each area in the grid size control parameter data structure.

[0073] According to the mesh size control parameters, set the density and sparse thresholds. For example, set the density threshold to a temperature gradient of 5°C / mm and the sparse threshold to a temperature gradient of 1°C / mm. If the temperature gradient of a cell exceeds the density threshold, the mesh is densified; if the temperature gradient of a cell is below the sparse threshold, the mesh is sparse. Store these thresholds and the corresponding operations (density or sparse) in the mesh adjustment strategy data structure.

[0074] Re-import the simplified CAD model into the Ansys Meshing module. According to the mesh adjustment strategy, mesh the dense area and the sparse area. For the dense area, reduce the mesh size to the set control parameter value. For the sparse area, increase the mesh size to the set control parameter value. Regenerate the mesh and check the mesh quality. Repeat steps S32 to S36 for iterative optimization until the error of the temperature field distribution meets the preset accuracy requirements. Save the final generated mesh and information such as node coordinates and unit connection relationships as an adaptive mesh structure data file.

[0075] Import the adaptive mesh structure into the Ansys Fluent module. Repeat the simulation process of step S32 and perform error analysis. If the error is still large, further adjust the mesh size control parameters and the density / sparseness threshold according to the error distribution, and repeat step S37 until the error of the temperature field distribution meets the preset accuracy requirements. Save the final generated mesh and information such as node coordinates and unit connection relationships as an optimized mesh structure data file.

[0076] Preferably, step S4 comprises the following steps: Step S41: using the optimized grid structure and the enhanced material property matrix to perform operating condition design and obtain an operating condition parameter table; Step S42: extracting thermal impedance data according to the operating condition parameter table to obtain a thermal impedance data set; Step S43: constructing a thermal impedance prediction model based on a neural network according to the thermal impedance data set to obtain a thermal impedance prediction model; Step S44: obtaining the working condition data of the 5G circuit board; inputting the working condition data of the 5G circuit board into the thermal impedance prediction model, performing rapid temperature field calculation, and obtaining rapid temperature field distribution; Step S45: performing heat dissipation performance analysis on the rapid temperature field distribution to obtain a heat dissipation performance evaluation report; Step S46: performing design iteration and optimization according to the heat dissipation performance evaluation report, the simplified CAD model, and the enhanced material property matrix to obtain an optimized design solution.

[0077] In an embodiment of the present invention, key parameters affecting the thermal performance of a 5G circuit board, such as ambient temperature, wind speed, and heat source power, are determined. The variation range of each parameter is set. The ambient temperature variation range is set to 20°C to 80°C, with a step size of 10°C. The wind speed variation range is set to 1 m / s to 5 m / s, with a step size of 1 m / s. The heat source power variation range is set to 1W to 5 W, with a step size of 1 W. Using the Latin hypercube sampling method, 100 groups of different operating condition parameter combinations are generated. Each group of operating condition parameter combinations, including ambient temperature, wind speed, and heat source power, and the corresponding enhanced material property matrix (EMPM) data, are stored in the operating condition parameter table.

[0078] For each set of operating parameters in the operating parameter table, import the optimized grid structure into Ansys Fluent. Set the boundary conditions and heat source according to the operating parameters. Run the simulation calculation to obtain the temperature field distribution. Extract the average temperature of the heat source and the ambient temperature to calculate the thermal impedance. The calculation formula of thermal impedance is R = (T_source - T_amb) / P, where R is the thermal impedance, T_source is the average temperature of the heat source, T_amb is the ambient temperature, and P is the power of the heat source. Store each set of operating parameters and its corresponding thermal impedance value in the thermal impedance data set.

[0079] Use TensorFlow to build a multi-layer perceptron (MLP) neural network model. The input layer contains three neurons, corresponding to ambient temperature, wind speed, and heat source power. The output layer contains one neuron, corresponding to thermal impedance. Set two hidden layers, each containing 10 neurons, and use the ReLU activation function. Use mean square error (MSE) as the loss function and use the Adam optimizer for training. Divide the thermal impedance dataset into a training set and a test set with a ratio of 8:2. Use the training set to train the neural network model, and use the test set to evaluate the prediction accuracy of the model. Save the trained neural network model as the thermal impedance prediction model.

[0080] Obtain the actual working condition data of the 5G circuit board, including ambient temperature, wind speed, and heat source power. Input this data into the trained thermal impedance prediction model to obtain the predicted thermal impedance value. Substitute the predicted thermal impedance value into the simplified thermal resistance network model to calculate the temperature of the key nodes. For example, a one-dimensional thermal resistance network model can be used to connect the heat source, heat sink, and ambient temperature nodes, and calculate the temperature of each node based on the thermal impedance. Store the calculated temperature distribution data as fast temperature field distribution data.

[0081] Analyze the rapid temperature field distribution data and extract key indicators such as maximum temperature, average temperature and temperature gradient. Compare these indicators with the preset design requirements to evaluate whether the heat dissipation performance meets the requirements. Organize the analysis results into a heat dissipation performance evaluation report.

[0082] If the heat dissipation performance evaluation report shows that the heat dissipation performance does not meet the design requirements, design iteration and optimization are required. Based on the problems identified in the evaluation report, such as the maximum temperature is too high, the simplified CAD model can be modified, such as increasing the size of the heat sink or changing the heat sink material. Based on the enhanced material property matrix (EMPM), a material with better thermal conductivity can be selected. Repeat steps S41 to S45 until the heat dissipation performance meets the design requirements. The final design solution, including the geometry, material selection, and heat dissipation solution, is stored as the optimized design solution.

[0083] Preferably, the present invention also provides a thermal simulation system based on a 5G circuit board, which is used to execute the thermal simulation method based on a 5G circuit board as described above, and the thermal simulation system based on a 5G circuit board includes: The material database construction module is used to measure and calculate the material properties of the 5G circuit board to obtain the thermal conductivity characteristic data and the interface thermal resistance data; the material database is constructed based on the thermal conductivity characteristic data and the interface thermal resistance data to obtain the enhanced material property matrix; The topological structure entropy analysis module is used to construct a simplified model of the 5G circuit board, and define boundary conditions and heat sources to obtain a simplified CAD model and boundary conditions and heat source definition data; solve the heat transfer equation of the simplified CAD model according to the boundary conditions and heat source definition data, and calculate the temperature field to obtain temperature field distribution data; analyze the influence of phase change latent heat on the simplified CAD model according to the temperature field distribution data to obtain phase change latent heat data; perform entropy increase and thermal impedance analysis based on transient heat sources according to the phase change latent heat data to obtain path entropy value-added data and path thermal impedance data; extract key paths according to the path entropy value-added data and path thermal impedance data, and identify hot spots to obtain a key heat dissipation path diagram; The adaptive grid optimization module is used to perform initial grid division according to the key heat dissipation path diagram, and perform adaptive grid optimization to obtain an adaptive grid structure; perform grid iterative optimization according to the adaptive grid structure to obtain an optimized grid structure; The neural network thermal impedance prediction module is used to extract thermal impedance data under different working conditions using an optimized grid structure to obtain a thermal impedance data set; to construct a thermal impedance prediction model based on the thermal impedance data set to obtain a thermal impedance prediction model; to perform rapid temperature field calculations using the thermal impedance prediction model to obtain a rapid temperature field distribution; to perform heat dissipation performance analysis on the rapid temperature field distribution, and to perform design iterations and optimization to obtain an optimized design solution.

[0084] Preferably, the circuit board is manufactured by the thermal simulation method based on the 5G circuit board as described.

[0085] Therefore, the embodiments should be regarded as illustrative and non-restrictive from all points, and the scope of the present invention is limited by the appended claims rather than the above description, and it is intended that all changes falling within the meaning and range of equivalent elements of the application documents are included in the present invention.

[0086] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A thermal simulation method based on a 5G circuit board, characterized in that: The following steps are involved: Step S1: measuring and calculating the material properties of the 5G circuit board to obtain thermal conductivity characteristic data and interface thermal resistance data; constructing a material database based on the thermal conductivity characteristic data and the interface thermal resistance data to obtain an enhanced material property matrix; Step S2: construct a simplified model of the 5G circuit board, and define boundary conditions and heat sources to obtain a simplified CAD model and boundary conditions and heat source definition data; solve the heat transfer equation of the simplified CAD model according to the boundary conditions and heat source definition data, and calculate the temperature field to obtain temperature field distribution data; perform phase change latent heat influence analysis on the simplified CAD model according to the temperature field distribution data to obtain phase change latent heat data; According to the phase change latent heat data, entropy increase and thermal impedance analysis based on transient heat source is performed to obtain path entropy increase data and path thermal impedance data; According to the path entropy value-added data and path thermal impedance data, the key path is extracted and hot spots are identified to obtain the key heat dissipation path diagram; Step S3: performing initial grid division according to the key heat dissipation path diagram, and performing adaptive grid optimization to obtain an adaptive grid structure; performing grid iterative optimization according to the adaptive grid structure to obtain an optimized grid structure; Step S4: using the optimized grid structure to extract thermal impedance data under different working conditions to obtain a thermal impedance data set; constructing a thermal impedance prediction model based on the thermal impedance data set to obtain a thermal impedance prediction model; using the thermal impedance prediction model to perform rapid temperature field calculations to obtain a rapid temperature field distribution; performing heat dissipation performance analysis on the rapid temperature field distribution, and performing design iterations and optimization to obtain an optimized design solution.

2. The thermal simulation method based on the 5G circuit board according to claim 1 is characterized in that: Step S1 includes the following steps: Step S11: measuring the material properties of the 5G circuit board to obtain a material property table; Step S12: performing energy-minimized material NPT ensemble simulation according to the material property table to obtain equilibrium atomic trajectory data; Step S13: Calculate the heat conduction characteristics according to the equilibrium atomic trajectory data to obtain heat conduction characteristics data; Step S14: Calculate the interface thermal resistance according to the equilibrium atomic trajectory data to obtain the interface thermal resistance data; Step S15: Modeling and calibrating nanoscale material properties according to the heat conduction property data and the interface thermal resistance data to obtain a nanoscale material property table; Step S16: Perform data fusion and calibration according to the material property table and the nanoscale material property table to obtain an enhanced material property matrix.

3. The thermal simulation method based on the 5G circuit board according to claim 1 is characterized in that: Step S2 includes the following steps: Step S21: Obtain a 5G circuit board CAD model, and perform model simplification processing to obtain a simplified CAD model; Step S22: defining boundary conditions and heat sources according to the enhanced material property matrix and the simplified CAD model to obtain boundary condition and heat source definition data; Step S23: solving the heat transfer equation of the simplified CAD model according to the boundary conditions and the heat source definition data, and performing temperature field calculation to obtain temperature field distribution data; Step S24: performing phase change latent heat influence analysis on the simplified CAD model according to the temperature field distribution data to obtain phase change latent heat data; performing entropy increase and thermal impedance analysis based on transient heat source according to the phase change latent heat data to obtain path entropy value data and path thermal impedance data; Step S25: extract the key path according to the path entropy value-added data and the path thermal impedance data, and identify the hot spots to obtain a key heat dissipation path diagram.

4. The thermal simulation method based on the 5G circuit board according to claim 3 is characterized in that: Step S24 includes the following steps: Step S241: identifying the phase change material according to the enhanced material property matrix to obtain the phase change material; performing path selection and unit division according to the phase change material and the simplified CAD model to obtain path unit information; Step S242: judging the phase change state according to the temperature field distribution data and the phase change material to obtain a phase change state mark; calculating the phase change latent heat according to the temperature field distribution data and the phase change material to obtain the phase change latent heat data; Step S243: performing material thermal response analysis according to the enhanced material property matrix to obtain material thermal response data; Step S244: performing heat flux calculation according to the temperature field distribution data, the phase change latent heat data and the material thermal response data to obtain heat flux data; Step S245: Calculate the unit entropy increase rate according to the heat flux density data to obtain the unit entropy increase rate data; perform path entropy value-added integration according to the unit entropy increase rate data and the path unit information to obtain the path entropy value-added data; Step S246: Calculate the thermal impedance of the component according to the temperature field distribution data to obtain the thermal impedance data of the component; calculate the thermal impedance of the heat dissipation path according to the heat flux density data and the thermal impedance data of the component to obtain the thermal impedance data of the path.

5. The thermal simulation method based on the 5G circuit board according to claim 4 is characterized in that: Step S244 is specifically as follows: Calculate the heat flux density of heat conduction according to the temperature field distribution data to obtain the heat flux density data of unit heat conduction; Calculate the heat flux density of convective heat transfer according to the temperature field distribution data to obtain the heat flux density data of unit convective heat transfer; Calculate the heat flux density of radiation heat transfer according to the temperature field distribution data to obtain the heat flux density data of unit radiation heat transfer; Perform heat flux superposition on unit heat conduction heat flux data, unit convection heat transfer heat flux data and unit radiation heat transfer heat flux data to obtain heat flux superposition data; Perform transient heat source impact analysis based on temperature field distribution data to obtain transient heat source impact data; According to the transient heat source influence data, the heat flux density distribution inside the unit is carried out to obtain the local heat flux density influence data; The heat flux direction is corrected for the heat flux superposition data according to the local heat flux influence data to obtain the heat flux data.

6. The thermal simulation method based on the 5G circuit board according to claim 5 is characterized in that: The transient heat source impact analysis based on the temperature field distribution data is as follows: The heat source position is identified according to the temperature field distribution data and the simplified CAD model to obtain the heat source position identification data; Determine the geometric shape according to the heat source position identification data to obtain the heat source geometric information; According to the heat source geometry information, the heat source power is analyzed over time to obtain the heat source power time function; Selecting a transient heat source model according to the heat source geometry information to obtain a selected transient heat source model; The transient heat source heat flux density is calculated according to the selected transient heat source model and the heat source power-time function to obtain the transient heat source impact data.

7. The thermal simulation method based on the 5G circuit board according to claim 1 is characterized in that: Step S3 includes the following steps: Step S31: performing initial mesh division according to the key heat dissipation path diagram and the simplified CAD model to obtain an initial mesh structure; Step S32: performing finite element simulation on the initial grid structure according to the enhanced material property matrix, boundary conditions and heat source definition data, and performing error analysis to obtain preliminary temperature field data; Step S33: selecting an encrypted area according to the preliminary temperature field data to obtain an encrypted area; Step S34: selecting a sparse area according to the path entropy value-added data and the path thermal impedance data to obtain a sparse area; Step S35: applying the grid size control parameter to the dense area and the sparse area to obtain the grid size control parameter; Step S36: setting encryption and sparse thresholds according to the grid size control parameter to obtain a grid adjustment strategy; Step S37: performing mesh adjustment and iterative optimization on the simplified CAD model according to the mesh adjustment strategy to obtain an adaptive mesh structure; Step S38: performing mesh iterative optimization according to the enhanced material property matrix and the adaptive mesh structure to obtain an optimized mesh structure.

8. The thermal simulation method based on the 5G circuit board according to claim 1 is characterized in that: Step S4 includes the following steps: Step S41: using the optimized grid structure and the enhanced material property matrix to perform operating condition design and obtain an operating condition parameter table; Step S42: extracting thermal impedance data according to the operating condition parameter table to obtain a thermal impedance data set; Step S43: constructing a thermal impedance prediction model based on a neural network according to the thermal impedance data set to obtain a thermal impedance prediction model; Step S44: obtaining the working condition data of the 5G circuit board; inputting the working condition data of the 5G circuit board into the thermal impedance prediction model, performing rapid temperature field calculation, and obtaining rapid temperature field distribution; Step S45: performing heat dissipation performance analysis on the rapid temperature field distribution to obtain a heat dissipation performance evaluation report; Step S46: performing design iteration and optimization according to the heat dissipation performance evaluation report, the simplified CAD model, and the enhanced material property matrix to obtain an optimized design solution.

9. A thermal simulation system based on a 5G circuit board, characterized in that: Used to perform the thermal simulation method based on the 5G circuit board according to claim 1, the thermal simulation system based on the 5G circuit board comprises: The material database construction module is used to measure and calculate the material properties of the 5G circuit board to obtain the thermal conductivity characteristic data and the interface thermal resistance data; the material database is constructed based on the thermal conductivity characteristic data and the interface thermal resistance data to obtain the enhanced material property matrix; The topological structure entropy analysis module is used to construct a simplified model of the 5G circuit board, and define boundary conditions and heat sources to obtain a simplified CAD model and boundary conditions and heat source definition data; solve the heat transfer equation of the simplified CAD model according to the boundary conditions and heat source definition data, and calculate the temperature field to obtain temperature field distribution data; analyze the influence of phase change latent heat on the simplified CAD model according to the temperature field distribution data to obtain phase change latent heat data; perform entropy increase and thermal impedance analysis based on transient heat sources according to the phase change latent heat data to obtain path entropy value-added data and path thermal impedance data; extract key paths according to the path entropy value-added data and path thermal impedance data, and identify hot spots to obtain a key heat dissipation path diagram; The adaptive grid optimization module is used to perform initial grid division according to the key heat dissipation path diagram, and perform adaptive grid optimization to obtain an adaptive grid structure; perform grid iterative optimization according to the adaptive grid structure to obtain an optimized grid structure; The neural network thermal impedance prediction module is used to extract thermal impedance data under different working conditions using an optimized grid structure to obtain a thermal impedance data set; to construct a thermal impedance prediction model based on the thermal impedance data set to obtain a thermal impedance prediction model; to perform rapid temperature field calculations using the thermal impedance prediction model to obtain a rapid temperature field distribution; to perform heat dissipation performance analysis on the rapid temperature field distribution, and to perform design iterations and optimization to obtain an optimized design solution.

10. A circuit board, characterized in that: The circuit board is manufactured by the thermal simulation method based on the 5G circuit board as described in any one of claims 1 to 8.

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