Method for constructing simulation data set based on silicon carbide chip heat dissipation structure design

Abstract

The present invention discloses a method for constructing a simulation data set based on the design of a silicon carbide chip heat dissipation structure, which specifically relates to the field of chip heat dissipation technology. First, a heat dissipation structure design database is constructed, data required for heat dissipation structure design simulation is collected, and various parameter combinations and corresponding experimental data are determined. Then, a multi-dimensional simulation data set is constructed and processed through a simulation model of the constructed silicon carbide chip heat dissipation structure. Secondly, a heat dissipation structure design simulation data set is constructed based on the processed simulation data set, and at the same time, abnormal processing results are fed back to a human-computer side for analysis and evaluation, and human-computer interaction is performed based on the abnormal evaluation results. The present invention performs data analysis and mining based on the data set, and can discover the intrinsic relationship between data generated in the heat dissipation structure design process and thermal performance, thereby helping design managers to formulate more scientific optimization strategies and improve the heat dissipation efficiency and performance stability of the heat dissipation structure.

Description

Technical Field

[0001] The present invention relates to the field of chip heat dissipation technology, and in particular to a method for constructing a simulation data set based on the design of a silicon carbide chip heat dissipation structure. Background Art

[0002] Silicon carbide chips, due to their excellent resistance to high temperatures, high voltages, and high frequencies, are widely used in power electronics, new energy vehicles, and other fields. However, silicon carbide chips generate a large amount of heat during operation, making efficient heat dissipation structure design crucial for their proper operation. Simulation technology is an important auxiliary tool in the heat dissipation structure design process. Through simulation, the performance of the heat dissipation structure can be predicted during the design phase, reducing the number of experiments and shortening R&D costs and cycles.

[0003] In the design of silicon carbide chip heat dissipation structures, traditional methods mainly rely on empirical design and repeated experiments. Engineers use past design experience to preliminarily determine the form and parameters of the heat dissipation structure, and then verify the rationality of the design by actually manufacturing samples and conducting thermal tests. With the development of computer technology, numerical simulation technology has gradually been applied to chip heat dissipation structure design. Existing simulation models are often based on simplified assumptions and idealized conditions, and there is a certain gap between them and the actual heat dissipation conditions of silicon carbide chips.

[0004] High-quality simulation datasets are the basis for accurate simulation. Currently, existing simulation models still have the following problems: on the one hand, empirical design is not only time-consuming and labor-intensive, increasing R&D costs, but also difficult to fully obtain detailed thermal performance data of the heat dissipation structure under various working conditions due to the limitations of actual test conditions; on the other hand, the datasets constructed by existing methods rely on single-parameter experiments and lack multi-parameter coupling analysis; therefore, the existing silicon carbide chip heat dissipation structure design methods are insufficient in accuracy, efficiency and data support. There is an urgent need for an innovative simulation dataset construction method based on silicon carbide chip heat dissipation structure design to improve the scientificity and effectiveness of heat dissipation structure design. Summary of the Invention

[0005] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides a method for constructing a simulation data set based on the design of a silicon carbide chip heat dissipation structure to solve the problems raised in the above-mentioned background technology.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a method for constructing a simulation data set based on the design of a silicon carbide chip heat dissipation structure, comprising:

[0007] S1: Using big data technology, we collect, store, and update the factors affecting the heat dissipation process of silicon carbide chips, receive data generated during the heat dissipation structure design of silicon carbide chips, and build a heat dissipation structure design database.

[0008] S2: Based on the heat dissipation structure design database obtained in S1, collect the data required for heat dissipation structure design simulation, and determine the various parameter combinations and corresponding experimental data for constructing the simulation data set;

[0009] S3: Using simulation model building technology, based on the first heat dissipation structure design text and the second heat dissipation structure design text obtained in S2, a simulation model of the heat dissipation structure of the silicon carbide chip is constructed;

[0010] S4: Input various parameter combinations obtained in S2 into the simulation model of the silicon carbide chip heat dissipation structure obtained in S3 to construct a multi-dimensional simulation data set, including a chip geometry parameter and heat dissipation performance data set, a heat dissipation structure parameter and heat dissipation efficiency data set, a material property and thermal interface performance data set, and a working condition and dynamic heat dissipation response data set;

[0011] S5: Using big data processing technology, the multi-dimensional simulation data set obtained in S4 is processed to obtain a processed heat dissipation structure design simulation data set, including processing pass results and processing abnormal results, and the processing abnormal results are fed back to the design administrator;

[0012] S6: Use big data analysis technology to evaluate the abnormal processing results, and feedback the abnormal evaluation results to the design administrator for human-computer interaction.

[0013] The technical effects and advantages of the present invention are as follows:

[0014] 1. This invention comprehensively analyzes the key factors affecting the performance of the silicon carbide chip heat dissipation structure, rationally determines the parameter value range, and uses orthogonal experimental design to generate four types of parameter combinations. This can cover a wide range of actual working conditions with fewer samples, ensuring that the constructed simulation data set is comprehensive and representative, making the simulation results more in line with actual conditions;

[0015] 2. This invention conducts data analysis and mining based on data sets, and can discover the inherent relationship and regularity between the data generated during the heat dissipation design process and thermal performance, helping design managers to formulate more scientific optimization strategies and improve the heat dissipation efficiency and performance stability of the heat dissipation structure. This eliminates the need for extensive actual testing, thereby significantly shortening the R&D cycle of silicon carbide chip heat dissipation structures.

[0016] 3. Through the quality assessment and feedback adjustment mechanism of the data set, the present invention can timely discover and solve problems in the data set, further improve the quality of the data set, meet the needs of simulation data in different application scenarios, help improve the efficiency and reliability of the silicon carbide chip heat dissipation structure design, and reduce R&D costs and cycles. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1It is a schematic diagram of the overall process of the present invention.

[0018] Figure 2 Schematic diagram of the method of the present invention. DETAILED DESCRIPTION

[0019] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative efforts shall fall within the scope of protection of the present invention.

[0020] See also Figure 1 As shown, the present invention provides a simulation data set construction system based on the heat dissipation structure design of silicon carbide chips, including a heat dissipation structure design data management center, a heat dissipation structure design simulation data set determination module, a heat dissipation structure design simulation model construction module, a heat dissipation structure design simulation data generation module, a heat dissipation structure design simulation data set processing and construction module, and a heat dissipation structure design simulation data set construction human-machine module.

[0021] The heat dissipation structure design data management center is connected to the remaining modules, the heat dissipation structure design simulation data set determination module is respectively connected to the heat dissipation structure design simulation model construction module and the heat dissipation structure design simulation data generation module, the heat dissipation structure design simulation data generation module is respectively connected to the heat dissipation structure design simulation model construction module and the heat dissipation structure design simulation data set processing and construction module, and the heat dissipation structure design simulation data set construction human-machine module is connected to the heat dissipation structure design simulation data set processing and construction module.

[0022] Heat dissipation structure design data management center: uses big data technology to collect, store and update factors affecting the heat dissipation process of silicon carbide chips, and receives data generated in the heat dissipation structure design of silicon carbide chips to build a heat dissipation structure design database;

[0023] Heat dissipation structure design simulation data set determination module: Based on the heat dissipation structure design database, it collects the data required for heat dissipation structure design simulation, determines various parameter combinations and corresponding experimental data for constructing the heat dissipation structure design, and transmits them to the heat dissipation structure design simulation data generation module;

[0024] Heat dissipation structure design simulation model building module: Through simulation model building technology, a simulation model of the silicon carbide chip heat dissipation structure is built based on the data required for heat dissipation structure design simulation, and the model is transmitted to the heat dissipation structure design simulation data generation module;

[0025] Heat dissipation structure design simulation data generation module: used to input various parameter combinations into the constructed simulation model of the silicon carbide chip heat dissipation structure, construct a multi-dimensional simulation data set, and transmit it to the heat dissipation structure design simulation data set processing and construction module;

[0026] Heat dissipation structure design simulation data set processing and construction module: This module processes multi-dimensional simulation data sets through big data processing technology, constructs a heat dissipation structure design simulation data set based on the processed simulation data sets, and feeds back abnormal processing results to the heat dissipation structure design simulation data set construction human-machine module;

[0027] The heat dissipation structure design simulation data set constructs a human-machine module: through big data analysis technology, the abnormal processing results are evaluated and human-machine interaction is carried out based on the evaluation abnormal results.

[0028] See also Figure 2 As shown, the method for constructing a simulation data set based on the heat dissipation structure design of a silicon carbide chip includes: S1: using big data technology to collect, store and update the influencing factors of the heat dissipation process of the silicon carbide chip, and receiving the data generated in the heat dissipation structure design of the silicon carbide chip to construct a heat dissipation structure design database; S2: based on the heat dissipation structure design database obtained in S1, collecting the data required for the heat dissipation structure design simulation, and determining various parameter combinations and corresponding experimental data for constructing the simulation data set; S3: using the simulation model construction technology, based on the first heat dissipation structure design text and the second heat dissipation structure design text obtained in S2, constructing a simulation model of the heat dissipation structure of the silicon carbide chip; S4: combining the various parameters obtained in S2 The combined input is fed into the simulation model of the silicon carbide chip heat dissipation structure obtained in S3 to construct a multi-dimensional simulation data set, including a chip geometry parameter and heat dissipation performance data set, a heat dissipation structure parameter and heat dissipation efficiency data set, a material property and thermal interface performance data set, and a working condition and dynamic heat dissipation response data set; S5: Through big data processing technology, the multi-dimensional simulation data set obtained in S4 is processed to obtain a heat dissipation structure design simulation data set after data processing, including a processing pass result and a processing abnormality result, and the processing abnormality result is fed back to the design administrator; S6: Through big data analysis technology, the processing abnormality result is evaluated, and the abnormal evaluation result is fed back to the design administrator for human-computer interaction.

[0029] S1: Using big data technology, we collect, store, and update the factors affecting the heat dissipation process of silicon carbide chips, receive data generated during the heat dissipation structure design of silicon carbide chips, and build a heat dissipation structure design database.

[0030] Specifically, this embodiment uses industry references, extensive academic literature, and preliminary experimental research to identify factors influencing the heat dissipation of silicon carbide chips. Starting from the characteristics of the chip itself, the effects of the chip's geometric dimensions on thermal conductivity and specific heat capacity are considered, and the chip's geometric parameters and material properties are determined. For the heat dissipation structure, the effects of the heat dissipation substrate's material and thickness on heat conduction are analyzed to determine the heat dissipation structure's geometric parameters and material parameters. Furthermore, various operating conditions of the chip in actual operation are considered, such as varying power input levels, ambient temperature variations, and operating condition parameters such as the flow rate and temperature of the cooling medium (e.g., air or liquid coolant).

[0031] S2: Based on the heat dissipation structure design database obtained in S1, collect the data required for heat dissipation structure design simulation, and determine the various parameter combinations and corresponding experimental data for constructing the simulation data set, including the following steps:

[0032] S2.1: Based on the heat dissipation structure design database obtained in S1, collect the data required for the heat dissipation structure design simulation to obtain the first text of the heat dissipation structure design, including the length, width and thickness of the chip; the height, spacing, and shape of the heat dissipation fins; the thickness and area of ​​the heat dissipation substrate; the thermal conductivity and specific heat capacity of the silicon carbide chip; the thermal conductivity and emissivity of the heat dissipation structure material (such as ordinary silicone grease, phase change material, liquid metal, etc.); and the thermal conductivity and interface thermal resistance of the filling material between the chip and the heat dissipation structure; the chip's input power, ambient temperature, and the flow rate and temperature of the cooling medium;

[0033] S2.2: Based on industry standards, determine the range of the data required for the heat dissipation structure design simulation obtained in S2.1 to obtain the second text of the heat dissipation structure design, including the range of chip geometry parameters, the range of heat dissipation structure geometry parameters, the range of material property parameters, and the range of operating condition parameters;

[0034] What needs to be specifically explained in this embodiment is the parameter range determination. For example, referring to the common silicon carbide chip products on the market, the length and width parameter ranges of the chip size are set to 1mm to 10mm, and the thickness range is set to 0.1mm to 1mm; for the heat dissipation fins, the fin height range is set to 5mm to 50mm, the fin spacing range is set to 1mm to 5mm, and the fin shape can be selected from common shapes such as rectangle and needle; for the heat dissipation substrate, the thickness range is set to 1mm to 10mm, and the area is appropriately enlarged according to the chip size; in terms of material properties, according to the performance indicators of existing materials, the thermal conductivity range of the silicon carbide chip is set to 300W / (m·K) to 500W / (m·K), the thermal conductivity range of the heat dissipation structure material (such as copper, aluminum, etc.) is set to 200W / (m·K) to 400W / (m·K), the thermal conductivity range of the filling material between the chip and the heat dissipation structure is 1W / (m·K) to 10W / (m·K), and the interface thermal resistance range is 1×10 -4 m 2 K / W to 1×10-3m 2 In terms of operating conditions, the chip input power range is set to 1W to 100W, the ambient temperature range is set to 20℃ to 80℃, and the cooling air flow rate range is set to 1m / s to 10m / s for air cooling; for liquid cooling, the coolant temperature range is set to 10℃ to 50℃, and the flow rate range is set to 0.1m / s to 1m / s.

[0035] S2.3: First, according to the parameter range determined in S2.2, four types of parameter combinations are selected to generate the third text of the heat dissipation structure design, including chip geometry parameter combination, heat dissipation structure parameter combination, material property parameter combination and working condition parameter combination; wherein the chip geometry parameter combination includes the length, width and thickness of the chip, the thermal conductivity of the chip material and the convection heat transfer coefficient; the heat dissipation structure parameter combination includes the convection heat transfer coefficient, fin height, fin spacing, fin thickness and fin material thermal conductivity; the material property parameter combination includes the thermal conductivity of silicon carbide and the thermal conductivity of the filling material, the contact area between the filling material and the chip, and the interface temperature difference between the chip area and the filling material; the working condition parameter combination includes the chip input power, the chip output power, the cooling medium flow rate, the maximum cooling medium flow rate, the ambient temperature and the ambient reference temperature; then, an orthogonal experimental design is used to generate N groups of experimental data for each type of parameter combination; each type of parameter combination includes the number of experiments, the experimental parameters and the number of levels of each experimental parameter, and the number of levels of each parameter is the same;

[0036] The present embodiment should specifically explain that the experimental design methods such as orthogonal experimental design and Latin hypercube sampling are used to generate a series of representative parameter combinations within the determined parameter range; orthogonal experimental design selects some representative points from the comprehensive test for testing based on orthogonality, which is a highly efficient, fast and economical experimental design method; according to L9(3 4 ) Orthogonal table arrangement experiment (9 experiments, 4 factors, 3 levels for each factor), only 9 experiments are needed, press L 15 (3 7 ) orthogonal table was used to conduct 15 experiments (15 experiments, 4 factors, 3 levels for each factor), which obviously greatly reduced the workload; therefore, orthogonal experimental design has been widely used in research in many fields; for example, 9 chip geometric parameter combination experiments were conducted, and the experimental parameters included chip length × width and chip thickness. According to the range of geometric parameters, the number of levels of chip length × width was 3 (1mm × 1mm, 5mm × 5mm, 10mm × 10mm), and the number of levels of chip thickness was 3 (0.1mm, 0.5mm, 1mm).

[0037] S3: Using simulation model building technology, based on the first heat dissipation structure design text and the second heat dissipation structure design text obtained in S2, a simulation model of the heat dissipation structure of the silicon carbide chip is constructed, including the following steps:

[0038] S3.1: First, based on the first heat dissipation structure design text and the second heat dissipation structure design text obtained in S2, obtain chip geometric parameters and corresponding parameter ranges, heat dissipation geometric parameters and corresponding parameter ranges, and material property parameters and corresponding parameter ranges; then, using simulation model construction technology (such as SolidWorks software, ANSYS Fluent software, etc.), construct a silicon carbide chip heat dissipation structure; secondly, combining the working condition parameters and the corresponding parameter ranges, set simulation boundary conditions based on the silicon carbide chip heat dissipation structure to construct a silicon carbide chip heat dissipation structure simulation model;

[0039] It should be specifically explained in this embodiment that SolidWorks is a three-dimensional mechanical design software, which is widely used in scenarios such as mechanical parts design and industrial equipment assembly; ANSYS Fluent is a computational fluid dynamics (CFD) simulation software, which is used to simulate physical phenomena such as fluid flow, heat transfer, and chemical reactions. It is part of the ANSYS engineering simulation suite. In the process of building the model of the present invention application, complex physical processes such as heat source distribution, heat conduction, convective heat transfer, and radiation heat transfer inside the chip can be realized; the geometric model defines the "structure" and the boundary conditions define the "environment", and the two together constitute a complete mathematical description of the simulation problem; based on the basic principles of heat transfer, fluid mechanics, etc., appropriate numerical simulation methods and software platforms (such as finite element analysis software ANSYS, COMSOL, etc.) are selected to establish a simulation model.

[0040] S3.2: Using big data analysis technology, evaluate the silicon carbide chip heat dissipation structure and simulation boundary condition settings, and obtain the silicon carbide chip heat dissipation structure evaluation index GDMP and the simulation boundary condition setting evaluation index TCRM, respectively.

[0041]

[0042] , n represents the number of geometric dimension measurements, n1 and n2 represent the number of types of geometric parameters and material characteristic parameters, respectively, D sim,i j1 and D exp,i j1 They represent the geometric dimensions of the j1th geometric parameter in the i-th measurement simulation model and the geometric dimensions in the design, MA sim,i j2 and MA exp,i j2 They represent the material characteristic parameters set in the j2-th simulation model and the material characteristic parameters obtained from actual tests;

[0043] n3 represents the number of types of working parameters, B sim,i j3 and B exp,i j3 They represent the working condition parameters set in the j3rd simulation model and the working condition parameters obtained from actual tests respectively;

[0044] S3.3: Using big data analysis technology, if the silicon carbide chip heat dissipation structure evaluation index GDMP is less than the corresponding threshold value GDMP0, then return to S3.1 and rebuild the silicon carbide chip heat dissipation structure. Otherwise, it means the evaluation has passed. If the simulation boundary condition setting evaluation index TCRM is less than the corresponding threshold value TCRM0 for comparison, then return to S3.1 and re-set the simulation boundary conditions. Otherwise, it means the evaluation has passed, and the simulation model of the silicon carbide chip heat dissipation structure is obtained.

[0045] S4: Input various parameter combinations obtained in S2 into the simulation model of the silicon carbide chip heat dissipation structure obtained in S3 to construct a multi-dimensional simulation data set, including a chip geometry parameter and heat dissipation performance data set, a heat dissipation structure parameter and heat dissipation efficiency data set, a material property and thermal interface performance data set, and a working condition and dynamic heat dissipation response data set, including the following steps:

[0046] S4.1: Constructing chip geometry parameters and heat dissipation performance data sets: First, the chip geometry parameters are input into the simulation model of the silicon carbide chip heat dissipation structure, and the chip geometry size and chip thermal resistance R are obtained through big data analysis technology. th The mapping relationship model:

[0047] Thermal resistance R th The unit is K / W, CT represents the chip thickness, L×W represents the length and width of the chip, a1 represents the thermal conductivity of the chip material (unit: W / (m·K)), a2 represents the convection heat transfer coefficient (unit: W / (m 2 K)) Use software (such as ANSYS Fluent or COMSOL) to simulate the flow and temperature fields and directly output the h distribution. This then yields a data set of chip geometry parameters and heat dissipation performance, consisting of chip geometry parameter combinations and corresponding chip thermal resistances.

[0048] It should be specifically explained in this embodiment that the thicker the chip, the greater the thermal resistance, the larger the chip area, the smaller the thermal resistance, and the larger the area, the more heat dissipation paths, and the smaller the thermal resistance. The thermal resistance of the chip reflects the difficulty of the chip in transferring the heat generated inside to the external environment. The smaller the thermal resistance, the better the chip's heat dissipation performance, and the heat can be transferred more smoothly from the inside of the chip to the outside world. Conversely, the greater the thermal resistance, the more difficult it is for the chip to dissipate heat, which can easily cause the chip temperature to rise, potentially affecting the performance and reliability of the chip.

[0049] S4.2: Construct a data set of heat dissipation structure parameters and heat dissipation efficiency: First, the heat dissipation structure parameter combination is input into the simulation model of the silicon carbide chip heat dissipation structure. Through big data analysis technology, a mapping relationship model between the heat dissipation structure parameters and the heat dissipation efficiency η is obtained:

[0050] ηfin represents the fin efficiency, a2 represents the convection heat transfer coefficient (unit: W / (m 2 K), tanh() is the hyperbolic tangent function, H fm Indicates the fin height, S fm Indicates the fin spacing, S opt represents the optimal spacing of fins, th represents the thickness of fins, λ fin represents the thermal conductivity of the fin material (unit: W / (m·K)), and N represents the number of fins; then the heat dissipation structure parameter and heat dissipation efficiency data set consisting of the heat dissipation structure parameter combination and the corresponding heat dissipation efficiency is obtained;

[0051] In this embodiment, it should be specifically explained that the ratio of the heat dissipation fin height to the spacing reflects the heat dissipation area density. The larger the ratio, the higher the efficiency. The shape factor improves efficiency by increasing the surface area or the turbulence effect (such as the wavy fins enhance convection). The heat dissipation structure fin is a common heat dissipation element in electronic equipment, mechanical equipment, and other systems that require heat dissipation. It is usually made of a metal material with high thermal conductivity (such as aluminum, copper, etc.) and improves heat dissipation efficiency by increasing the heat dissipation area. Under natural convection, S opt ≈5-10mm, S under forced convection opt ≈2-5mm.

[0052] S4.3: Constructing a data set of material properties and thermal interface performance: First, the material property parameters are combined and input into the simulation model of the silicon carbide chip heat dissipation structure. Through big data analysis technology, the material property parameters and the thermal interface heat flux density q (unit: W / m 2 )’s mapping relationship model:

[0053] λ sic and λ fil Represents the thermal conductivity of silicon carbide and the thermal conductivity of the filler material (unit: W / (m·K)), A real Indicates the contact area between the filling material and the chip, A nom represents the chip area, which is obtained by multiplying the chip length and width, d represents the thickness of the filling material (unit: m), and ΔT represents the interface temperature difference of the filling material, in Kelvin K. Then, a material property and thermal interface performance data set consisting of a combination of material property parameters and the corresponding thermal interface heat flux density is obtained;

[0054] S4.4: Constructing a dataset of working conditions and dynamic heat dissipation response: First, the working condition parameter combination is input into the simulation model of the silicon carbide chip heat dissipation structure. Using big data analysis technology, a mapping relationship model between the working condition parameters and the heat dissipation response index Hdr is obtained:

[0055] V flow Indicates the cooling medium flow rate, max_V flow represents the maximum cooling medium flow rate, γ represents the flow rate influence coefficient, 0.6≤γ≤0.8, β represents the heat dissipation response influence factor, which can be fitted by simulation software through CFD simulation or experimental data to β and exponent γ (such as the 0.7th power of the flow rate), 0.5≤β≤0.9, T ab Indicates the ambient temperature, T chip Indicates the temperature of the chip during stable operation, with the temperature unit being Kelvin K. Then, a working condition and dynamic heat dissipation response data set consisting of a combination of working condition parameters and a corresponding heat dissipation response index is obtained;

[0056] In this embodiment, it should be specifically explained that increasing the flow rate enhances convective heat transfer and accelerates temperature stabilization; the ambient temperature drives heat dissipation by affecting the temperature difference, but the effect is relatively weak; the absolute temperature (Kelvin, K) is a standard practice in thermodynamic calculations; it represents the energy conversion efficiency of the chip. Low energy conversion efficiency indicates high heat generation and requires high heat dissipation efficiency.

[0057] S5: Using big data processing technology, the multi-dimensional simulation data set obtained in S4 is processed to obtain a processed heat dissipation structure design simulation data set, including processing pass results and processing abnormal results, and the abnormal results are fed back to the design administrator, including the following steps:

[0058] S5.1: Set the chip thermal resistance R th With the reference value R th 0 For comparison, if R th ≤R th 0 , then the corresponding chip geometric parameter combination is marked as chip heat dissipation efficiency data, and the processing is passed. Otherwise, the difference ΔR is judged. th Whether it is within the allowable range, if so, the corresponding chip geometric parameter combination is marked as good chip heat dissipation data, and the processing is successful; if not, it means that the chip geometric parameter combination processing is abnormal;

[0059] S5.2: Compare the heat dissipation efficiency η with the reference value η 0 Compare, if η≥η 0 , the corresponding heat dissipation structure parameter combination is marked as heat dissipation high-efficiency data of the heat dissipation structure, and the processing is passed. Otherwise, it is judged whether the difference Δη is within the allowable range. If so, the heat dissipation structure parameter combination is marked as heat dissipation good data of the heat dissipation structure, and the processing is passed. Otherwise, it means that the heat dissipation structure parameter combination processing is abnormal;

[0060] S5.3: Compare the thermal interface heat flux q with the reference value q 0 Compare, if q≤q 0, then the corresponding material characteristic parameter combination is marked as material heat dissipation efficient data, and the processing is passed. Otherwise, it is judged whether the difference Δq is within the allowable range. If so, the corresponding material characteristic parameter combination is marked as material heat dissipation good data, and the processing is passed. If not, it means that the material characteristic parameter combination processing is abnormal;

[0061] S5.4: Compare the heat dissipation response index Hdr with the reference value Hdr 0 Compare, if Hdr≥Hdr 0 , then the corresponding working condition parameter combination is marked as working condition heat dissipation efficient data, and the processing is passed. Otherwise, it is judged whether the difference ΔHdr is within the allowable range. If so, the corresponding working condition parameter combination is marked as working condition heat dissipation good data, and the processing is passed. If not, it means that the working condition parameter combination processing is abnormal;

[0062] S5.5: Using big data technology, store the processed simulation data set in a database according to the annotation results to form a heat dissipation structure design simulation data set;

[0063] It should be specifically noted that in this embodiment, abnormal data, such as data with negative heat flux values, has been removed through data preprocessing technology.

[0064] S6: Evaluate the abnormal processing results through big data analysis technology and obtain the abnormal processing evaluation index AEI of the heat dissipation structure design simulation data set. nc l Indicates the number of exceptions handled by the first type of parameter combination, Tn l represents the number of experiments for the first type of parameter combination, where l = 1 represents the chip geometry parameter combination, l = 2 represents the heat dissipation structure parameter combination, l = 3 represents the material property parameter combination, and l = 4 represents the working condition parameter. If the AEI is greater than or equal to the corresponding threshold, it means that the heat dissipation structure design simulation data set is well constructed. Otherwise, it indicates an abnormality. The abnormal evaluation results are fed back to the design manager for human-computer interaction. For example, if the geometric dimension design is unreasonable or the material property parameters are inaccurate, the relevant parameters are adjusted according to the analysis results, and the simulation and data collection are repeated.

[0065] Secondly: The drawings of the embodiments disclosed in the present invention only involve structures related to the embodiments disclosed in the present invention. Other structures may refer to conventional designs. The same embodiment and different embodiments of the present invention may be combined with each other without conflict.

[0066] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A simulation data set construction method based on silicon carbide chip heat dissipation structure design, characterized by: include: S1: Using big data technology, we collect, store, and update the factors affecting the heat dissipation process of silicon carbide chips, receive data generated during the heat dissipation structure design of silicon carbide chips, and build a heat dissipation structure design database. S2: Based on the heat dissipation structure design database obtained in S1, collect the data required for heat dissipation structure design simulation, and determine the various parameter combinations and corresponding experimental data for constructing the simulation data set; S3: Using simulation model building technology, based on the first heat dissipation structure design text and the second heat dissipation structure design text obtained in S2, a simulation model of the heat dissipation structure of the silicon carbide chip is constructed; S4: Input various parameter combinations obtained in S2 into the simulation model of the silicon carbide chip heat dissipation structure obtained in S3 to construct a multi-dimensional simulation data set, including a chip geometry parameter and heat dissipation performance data set, a heat dissipation structure parameter and heat dissipation efficiency data set, a material property and thermal interface performance data set, and a working condition and dynamic heat dissipation response data set; In S4, a heat dissipation structure parameter and heat dissipation efficiency data set is constructed: first, the heat dissipation structure parameter combination is input into the simulation model of the silicon carbide chip heat dissipation structure, and a mapping relationship model between the heat dissipation structure parameters and the heat dissipation efficiency η is obtained through big data analysis technology: η fin represents the fin efficiency, a2 represents the convective heat transfer coefficient, tanh() is the hyperbolic tangent function, H fm Indicates the fin height, S fm Indicates the fin spacing, S opt represents the optimal spacing of fins, th represents the thickness of fins, λ fin represents the thermal conductivity of the fin material, and N represents the number of fins; then a heat dissipation structure parameter and heat dissipation efficiency data set consisting of a heat dissipation structure parameter combination and the corresponding heat dissipation efficiency is obtained; S5: Using big data processing technology, the multi-dimensional simulation data set obtained in S4 is processed to obtain a processed heat dissipation structure design simulation data set, including processing pass results and processing abnormal results, and the processing abnormal results are fed back to the design administrator; S6: Use big data analysis technology to evaluate the abnormal processing results, and feedback the abnormal evaluation results to the design administrator for human-computer interaction.

2. The method for constructing a simulation data set based on silicon carbide chip heat dissipation structure design according to claim 1, characterized in that: The various parameter combinations and corresponding experimental data in S2 include chip geometry parameter combinations, heat dissipation structure parameter combinations, material property parameter combinations, and working condition parameter combinations; wherein the chip geometry parameter combinations include the length, width, and thickness of the chip, the thermal conductivity of the chip material, and the convection heat transfer coefficient; the heat dissipation structure parameter combinations include the convection heat transfer coefficient, fin height, fin spacing, fin thickness, and thermal conductivity of the fin material; the material property parameter combinations include the thermal conductivity of silicon carbide and the thermal conductivity of the filling material, the contact area between the filling material and the chip, and the interface temperature difference between the chip area and the filling material; the working condition parameter combinations include the chip input power, the chip output power, the cooling medium flow rate, the maximum cooling medium flow rate, the ambient temperature, and the ambient reference temperature; and then an orthogonal experimental design is used to generate several sets of experimental data for each parameter combination; each parameter combination includes the number of experiments, the experimental parameters, and the number of levels of each experimental parameter, and the number of levels of each parameter is the same.

3. The method for constructing a simulation data set based on silicon carbide chip heat dissipation structure design according to claim 1, characterized in that: In the S4, the chip geometric parameters and heat dissipation performance data set are constructed: first, the chip geometric parameters are combined and input into the simulation model of the silicon carbide chip heat dissipation structure, and the chip geometric size and chip thermal resistance R are obtained through big data analysis technology. th The mapping relationship model: CT represents the chip thickness, L and W represent the length and width of the chip, a1 represents the thermal conductivity of the chip material, and a2 represents the convection heat transfer coefficient. Then, a chip geometric parameter and heat dissipation performance data set consisting of the chip geometric parameter combination and the corresponding chip thermal resistance is obtained.

4. The method for constructing a simulation data set based on silicon carbide chip heat dissipation structure design according to claim 1, characterized in that: In the S4, the material property and thermal interface performance data set is constructed: first, the material property parameter combination is input into the simulation model of the silicon carbide chip heat dissipation structure, and the thermal conductivity λ of silicon carbide is combined with the big data analysis technology. sic and the thermal conductivity of the filling material λ fil , the contact area A between the filling material and the chip real , chip area A nom , the filling material thickness d is obtained by multiplying the chip length and width, ΔT represents the interface temperature difference of the filling material, and the mapping relationship model between the material characteristic parameters and the thermal interface heat flux density q is obtained; then the material characteristics and thermal interface performance data set composed of the material characteristic parameter combination and the corresponding thermal interface heat flux density is obtained.

5. The method for constructing a simulation data set based on silicon carbide chip heat dissipation structure design according to claim 1, characterized in that: In the S4, the working condition and dynamic heat dissipation response data set is constructed: first, the working condition parameter combination is input into the simulation model of the silicon carbide chip heat dissipation structure, and the cooling medium flow rate V is combined with the big data analysis technology to obtain the working condition parameter combination. flow , represents the maximum cooling medium flow rate max_V flow 、Ambient temperature T ab , the temperature of the chip during stable operation T chip And the flow rate influence coefficient γ, the mapping relationship model between the working condition parameters and the heat dissipation response index Hdr is obtained; then the working condition and dynamic heat dissipation response data set consisting of the working condition parameter combination and the corresponding heat dissipation response index is obtained.

6. The method for constructing a simulation data set based on silicon carbide chip heat dissipation structure design according to claim 1, characterized in that: The heat dissipation structure design simulation data set obtained after data processing in S5 includes: S5.1: Set the chip thermal resistance R th With the reference value R th 0 For comparison, if R th ≤R th 0 , then the corresponding chip geometric parameter combination is marked as chip heat dissipation efficiency data, and the processing is passed. Otherwise, the difference ΔR is judged. th Whether it is within the allowable range, if so, the corresponding chip geometric parameter combination is marked as good chip heat dissipation data, and the processing is successful; if not, it means that the chip geometric parameter combination processing is abnormal; S5.2: Compare the heat dissipation efficiency η with the reference value η 0 Compare, if η≥η 0 , the corresponding heat dissipation structure parameter combination is marked as heat dissipation high-efficiency data of the heat dissipation structure, and the processing is passed. Otherwise, it is judged whether the difference Δη is within the allowable range. If so, the heat dissipation structure parameter combination is marked as heat dissipation good data of the heat dissipation structure, and the processing is passed. Otherwise, it means that the heat dissipation structure parameter combination processing is abnormal.

7. The method for constructing a simulation data set based on silicon carbide chip heat dissipation structure design according to claim 6, characterized in that: The heat dissipation structure design simulation data set obtained after data processing in S5 also includes: S5.3: Compare the thermal interface heat flux q with the reference value q 0 Compare, if q≤q 0 , then the corresponding material characteristic parameter combination is marked as material heat dissipation efficient data, and the processing is passed. Otherwise, it is judged whether the difference Δq is within the allowable range. If so, the corresponding material characteristic parameter combination is marked as material heat dissipation good data, and the processing is passed. If not, it means that the material characteristic parameter combination processing is abnormal; S5.4: Compare the heat dissipation response index Hdr with the reference value Hdr 0 Compare, if Hdr≥Hdr 0 , then the corresponding working condition parameter combination is marked as working condition heat dissipation efficient data, and the processing is passed. Otherwise, it is judged whether the difference ΔHdr is within the allowable range. If so, the corresponding working condition parameter combination is marked as working condition heat dissipation good data, and the processing is passed. If not, it means that the working condition parameter combination processing is abnormal; S5.5: Using big data technology, the processed simulation data set is stored in the database according to the annotation results to form a heat dissipation structure design simulation data set.

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