Optimization design on process parameters of copper wire bonding for power modules

HU Biao; CHENG Lanxian; LI Zhenling; DAI Xiaoping

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

您当前的位置：

首页 >

文章列表页 >

Optimization design on process parameters of copper wire bonding for power modules

High-power Devices | 更新时间：2024-08-02

- Optimization design on process parameters of copper wire bonding for power modules
- Electric Drive for Locomotives Issue 2, Pages: 43-49(2023)
- 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：
- 基金信息：
- DOI：10.13890/j.issn.1000-128X.2023.02.104
  CLC： TG457.13;TN605
- Published：10 March 2023，
  
  Received：01 August 2022，
  
  Revised：25 December 2022，
- 稿件说明：
扫描看全文
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

references

吴义伯, 戴小平, 王彦刚, 等. IGBT功率模块封装中先进互连技术研究进展[J]. 大功率变流技术, 2015(2): 6-11.

WU Yibo, DAI Xiaoping, WANG Yangang, et al. State-of-the-art progress of advanced interconnection technology for IGBT power module packaging[J]. High Power Converter Technology, 2015(2): 6-11.

LING Jamin, XU Tao, LUECHINGER C, et al. Large-Cu-wire wedge bonding on wafers with Cu pad metallization[C]//VDE-Verlag. PCIM Europe, International Exhibition and Conference for Power Electronics, Intelligent Motion, Renewable Energy and Energy Management. Shanghai: VDE-Verlag, 2012: 767-775.

王福亮, 韩雷, 钟掘. 超声功率对粗铝丝超声引线键合强度的影响[J]. 中国机械工程, 2005, 16(10): 919-923.

WANG Fuliang, HAN Lei, ZHONG Jue. Effects of ultrasonic power on heavy aluminum wire wedge bonding strength[J]. China Mechanical Engineering, 2005, 16(10): 919-923.

王福亮, 李军辉, 韩雷, 等. 键合时间对粗铝丝超声引线键合强度的影响[J]. 焊接学报, 2006, 27(5): 47-51.

WANG Fuliang, LI Junhui, HAN Lei, et al. Effect of bonding time on thick aluminum wire wedge bonding strength[J]. Transactions of the China Welding Institution, 2006, 27(5): 47-51.

高荣芝, 韩雷. 键合压力对粗铝丝引线键合强度的实验研究[J]. 压电与声光, 2007, 29(3): 366-369.

GAO Rongzhi, HAN Lei. Experimental studies of bonding pressure on heavy aluminum wire bonding strength[J]. Piezoelectrics & Acoustooptics, 2007, 29(3): 366-369.

张玉佩, 张茹, 戎光荣. 功率器件引线键合参数研究[J]. 电子与封装, 2021, 21(7): 070202.

ZHANG Yupei, ZHANG Ru, RONG Guangrong. Research on parameters of wire bonding for power devices[J]. Electronics & Packaging, 2021, 21(7): 070202.

程顺昌, 王晓强, 吕玉冰, 等. 试验设计方法在超声楔形焊工艺优化中的应用[J]. 半导体光电, 2013, 34(6): 987-989.

CHENG Shunchang, WANG Xiaoqiang, LYU Yubing, et al. Application of DOE in the optimization of ultrasonic wedge bond process[J]. Semiconductor Optoelectronics, 2013, 34(6): 987-989.

何怡刚, 周健波, 刘嘉诚, 等. 改进IGBT动态模型与电-热-力多物理场耦合失效分析[J]. 电子测量与仪器学报, 2019, 33(8): 46-54.

HE Yigang, ZHOU Jianbo, LIU Jiacheng, et al. Improved IGBT dynamic model and electro-thermal-force multiphysics coupling failure analysis[J]. Journal of Electronic Measurement and Instrumentation, 2019, 33(8): 46-54.

黄柯勋, 吴松荣, 向碧楠, 等. 基于改进小波神经网络的IGBT时间序列预测算法研究[J]. 机车电传动, 2021(5): 161-166.

HUANG Kexun, WU Songrong, XIANG Binan, et al. Research on IGBT sequentially prediction algorithm based on improved wavelet neural network[J]. Electric Drive for Locomotives, 2021(5): 161-166.

彭爱红. Minitab软件在有重复试验的正交试验设计中的应用[J]. 集美大学学报(教育科学版), 2013, 14(1): 111-114.

PENG Aihong. Application of Minitab software in orthogonal experimental design with duplicate test[J]. Journal of Jimei University (Education Science Edition), 2013, 14(1): 111-114.

胡海青, 张琅, 张道宏. 供应链金融视角下的中小企业信用风险评估研究——基于SVM与BP神经网络的比较研究[J]. 管理评论, 2012, 24(11): 70-80.

HU Haiqing, ZHANG Lang, ZHANG Daohong. Research on SMEs credit risk assessment from the perspective of supply chain finance：a comparative study on the SVM model and BP model[J]. Management Review, 2012, 24(11): 70-80.

PANCHAL G, GANATRA A, KOSTA Y P, et al. Behaviour analysis of multilayer perceptrons with multiple hidden neurons and hidden layers[J]. International Journal of Computer Theory and Engineering, 2011, 3(2): 332-337.

左付山, 李政原, 吕晓, 等. 基于BP神经网络的汽油机尾气排放预测[J]. 江苏大学学报(自然科学版), 2020, 41(3): 307-313.

ZUO Fushan, LI Zhengyuan, LYU Xiao, et al. Prediction of gasoline engine exhaust emission based on BP neural network[J]. Journal of Jiangsu University (Natural Science Edition), 2020, 41(3): 307-313.

王雪辉. 基于数据挖掘技术的股票预测研究与应用[D]. 海口: 海南大学, 2020.

WANG Xuehui. Research and application of stock forecast based on data mining technology[D]. Haikou: Hainan University, 2020.

周畅, 米红娟. 深度学习中三种常用激活函数的性能对比研究[J]. 北京电子科技学院学报, 2017, 25(4): 27-32.

ZHOU Chang, MI Hongjuan. A comparative study on the performance of three kinds of activation functions in deep learning[J]. Journal of Beijing Electronic Science and Technology Institute, 2017, 25(4): 27-32.

李岩, 袁弘宇, 于佳乔, 等. 遗传算法在优化问题中的应用综述[J]. 山东工业技术, 2019(12): 242-243.

LI Yan, YUAN Hongyu, YU Jiaqiao, et al. A review on the application of genetic algorithm in optimization problems[J]. Shandong Industrial Technology, 2019(12): 242-243.

Views

下载量

CSCD

CNKI被引量

Alert me when the article has been cited

提交

Tools

Publicity Resources

Research on the prediction algorithm of tread wear for locomotive wheels based on GA-ridge regression analysis

A study on life assessment of metro cowcatcher based on multi-axis vibration environment reconstruction

Train adaptive energy saving strategy based on iterative induced genetic algorithm

Research on IGBT Sequentially Prediction Algorithm Based on Improved Wavelet Neural Network

Research on Silver Sintering Process and Wire Bonding Process of IGBT Module

Related Author

WANG Feier

SUN Yuduo

CHENG Yaping

LIU Tong

FANG Xin

LIANG Shulin

CHI Maoru

LIU Yang

Related Institution

Shanghai Depot, China Railway Shanghai Group Co., Ltd.

Metals and Chemistry Research Institute, China Academy of Railway Sciences Corporation Limited

School of Mechanical Engineering, Southwest Jiaotong University

State Key Laboratory of Rail Transit Vehicle System, Southwest Jiaotong University

Zhuzhou CRRC Times Electric Co., Ltd.

⁰