功率模块铜线键合工艺参数优化设计

胡彪; 成兰仙; 李振铃; 戴小平

doi:10.13890/j.issn.1000-128X.2023.02.104

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功率模块铜线键合工艺参数优化设计

大功率器件 | 更新时间：2024-08-02

- 功率模块铜线键合工艺参数优化设计
- Optimization design on process parameters of copper wire bonding for power modules
- 机车电传动 2023年第2期页码：43-49
- 作者机构：
  
  1.华南农业大学电子工程学院（人工智能学院），广东广州 510642
  2.湖南国芯半导体科技有限公司，湖南株洲 412001
  3.湖南省功率半导体创新中心，湖南株洲;412001
- 作者简介：
  
  成兰仙（1981—），女，博士，副教授，硕士生导师，主要从事先进微电子封装材料及其工艺、电力传动及其控制系统方面的研究； E-mail: chenglx@scau.edu.cn
- 基金信息：
  
  国家自然科学基金项目(61804057)
- DOI：10.13890/j.issn.1000-128X.2023.02.104
  中图分类号： TG457.13;TN605
- 纸质出版日期：2023-03-10，
  
  收稿日期：2022-08-01，
  
  修回日期：2022-12-25，
- 稿件说明：
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胡彪, 成兰仙, 李振铃, 等. 功率模块铜线键合工艺参数优化设计[J]. 机车电传动, 2023(2): 43-49.

HU Biao, CHENG Lanxian, LI Zhenling, et al. Optimization design on process parameters of copper wire bonding for power modules[J].Electric Drive for Locomotives, 2023(2): 43-49.
胡彪, 成兰仙, 李振铃, 等. 功率模块铜线键合工艺参数优化设计[J]. 机车电传动, 2023(2): 43-49. DOI： 10.13890/j.issn.1000-128X.2023.02.104.

HU Biao, CHENG Lanxian, LI Zhenling, et al. Optimization design on process parameters of copper wire bonding for power modules[J].Electric Drive for Locomotives, 2023(2): 43-49. DOI： 10.13890/j.issn.1000-128X.2023.02.104.

摘要

为了提高功率模块铜线键合性能，采用6因素5水平的正交试验方法，结合BP（Back Propagation）神经网络与遗传算法，提出了一种铜线键合工艺参数优化设计方案。首先，对选定样品进行正交试验并将结果进行极差分析，得到工艺参数对键合质量的影响权重排序。其次，运用BP神经网络构建了铜线键合性能预测模型，并通过遗传算法对BP神经网络适应度函数求解，得到了工艺参数的最优值。将BP-遗传算法与传统优化方法的优化结果进行对比，发现经BP-遗传算法优化后的铜线键合工艺稳定性提升更加明显。最后，对功率模块进行了功率循环试验，结果表明经BP-遗传算法优化后的模块功率循环能力得到显著提升。

Abstract

In order to improve the copper wire bonding performance of the power modules

an optimization design scheme of process parameters for copper wire bonding was proposed by using the six-factor five-level orthogonal test method

and combining the back propagation (BP) neural network and genetic algorithm (GA). Firstly

the selected samples were orthogonally tested and the results were analyzed by range analysis to generate the influence weight ranking of the process parameters on the bonding quality. Secondly

a prediction model of copper wire bonding performance was constructed using the BP neural network

and the optimal values of process parameters were generated by solving the BP neural network fitness function with GA. Comparing the optimization results from the BP genetic algorithm with those from the traditional methods

the former was found in the stability of the copper wire bonding process improved more significantly. Finally

the power cycle test was carried out on the power module

showing that the power cycle capability of the module optimized by the BP genetic algorithm was significantly improved.

关键词

铜线键合BP神经网络遗传算法工艺参数优化

Keywords

copper wire bondingBP neural networkgenetic algorithmoptimization of process parameters

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