基于智能算法的双面散热SiC功率模块多目标优化设计

doi:10.19595/j.cnki.1000-6753.tces.221526

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摘要

双面散热功率模块凭借优异的电热特性,能够满足SiC器件封装的性能需求。但双面散热模块的电学、热学以及力学特性互相制约,难以实现各性能的全部最优,为此,本文提出了一种基于智能算法的多目标优化设计方法。首先通过参数化仿真对功率模块的电热力性能进行了建模,并分析了待优化的尺寸参数对各性能指标的影响规律。然后基于参数化仿真与流程控制方法得到学习样本,通过神经网络训练得到了待优化尺寸变量与各性能指标之间的函数关系,解决了尺寸变量与性能指标之间的函数难以获取的问题。得到目标函数后,基于遗传算法进行多目标优化求解,实现了定制化的多目标优化设计。最后基于优化结果分别制备了优化前后的功率模块,并对其性能指标进行了测试以及对比,结果证明了本文所提出的多目标优化设计方法的正确性。

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	张缙
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	山崎智幸
	王来利

关键词 ： SiC功率模块, 双面散热, 多目标优化, 人工神经网络, 遗传算法

Abstract：

Double-sided cooling power module has low parasitic parameters and excellent heat dissipation performance, which is one of the development directions of power modules. However, the electrical, thermal and mechanical properties of it are contradictory to each other, so it is difficult to achieve the full optimization of all performance. As a new type of packaging structure, double-sided cooling power module lacks systematic multi-objective optimization design method. Therefore, this paperproposes a multi-objective optimization design method based on intelligent algorithm.
Firstly, the structure of the double-sided cooling SiC power module was designed and the material of each key component within the module was determined. The electrical, thermal and mechanical properties of the designed power module were analyzed by parasitic parameter simulation and finite element electro-thermal coupling simulation, the electrical properties, i.e. parasitic inductance,were calculated using ANSYS Q3D. The thermal properties, i.e. junction-to-ambient thermal resistance,were calculated using COMSOL. The mechanical properties, including die attach stress and chip stress, were calculated using COMSOL considering the temperature gradient in the heat conduction process.After that, the influence of the size parameters on the performance indexes of the power module was studied,there are three key points, the point one is that h₂ has a large effect on the parasitic inductance through Eddy current effect, the point two is that a negative relationship between h₁ and the thermal resistance exists because a thicker copper layer can spread more heat horizontally, the point three is that the optimization direction of chip stress and the die attach stress is contradicted to each other.
Secondly, a multi-objective optimization method based on intelligent algorithm was proposed using the method of parametric modeling and simulation. Based on the randomly generated size parameters, hundreds of module samples underwentparametric simulation, then these results were used as training samples for artificial neural network.The functional relationship between size parameters and performance indexes can be obtained, which can speed up the calculation of performance index while ensuring the calculation accuracy. After the objective function is obtained, genetic algorithm is used to solve the multi-objective optimization. A three-objective optimization of die attach stress, parasitic inductance and thermal resistance was conducted, the Pareto front is a curve because thermal resistance and die attach stress have the same optimization direction. Then a three-objective optimization of chip stress, parasitic inductance and thermal resistance was conducted, and the Pareto front is a curved surface, because these three performances index have different optimization direction.To achieve customized optimization, a stricter size constraint was set and a four-objective optimization of die attach stress, chip stress, parasitic inductance and junction-to-ambient thermal resistance was conducted and a solution that can improve all performance exists.
Finally, based onfour-objective optimization,the initial power module before optimization and the power module after optimization weremanufactured respectively, and their performance indexeswere tested and compared, the parasitic inductance was measured using LCR meter, junction-to-ambient thermal resistance was measured using a thermal characteristic test platform. The results showthat the multi-objective optimization method can significantly improve overall performance ofthe double-sided cooling SiC power module, the loop parasitic inductance is reduced from 7.475nH to 6.489nH, the thermal resistance is reduced from 0.373K/W to 0.355K/W.

Key words： SiC power module Double-Sided Cooling Multi-objective optimization Artificial neural network Genetic Algorithm

PACS:

TM23

通讯作者: 王来利男,1982年生,教授,博士生导师,研究方向为电力电子封装集成。E-mail：llwang@mail.xjtu.edu.cn

作者简介: 张缙男,1998年生,博士研究生,研究方向为功率半导体器件的封装与可靠性。E-mail：z062626@stu.xjtu.edu.cn

引用本文:

张缙, 刘智, 刘意, 王见鹏, 刘志红, 山崎智幸, 王来利. 基于智能算法的双面散热SiC功率模块多目标优化设计[J]. 电工技术学报, 0, (): 230621-230621. Zhang Jin, Liu Zhi, Liu Yi, Wang Jianpeng, Liu Zhihong, Yamazaki Tomoyuki, Wang Laili. Research on Multi-Objective Optimization Design of Double-Sided Cooling SiC Power Module Based on Intelligent Algorithm. Transactions of China Electrotechnical Society, 0, (): 230621-230621.

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