基于机器学习电子封装互连材料研究现状

doi:10.3901/JME.2023.22.222

机械工程学报 ›› 2023, Vol. 59 ›› Issue (22): 222-233.doi: 10.3901/JME.2023.22.222

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基于机器学习电子封装互连材料研究现状

张墅野^1,2, 段晓康¹, 罗克宇¹, 许孙武¹, 张志昊³, 陈捷狮⁴, 何鹏¹

1. 哈尔滨工业大学先进焊接与连接国家重点实验室哈尔滨 150001;
2. 哈尔滨工业大学重庆研究院重庆 401135;
3. 厦门大学材料学院厦门 361005;
4. 上海工程技术大学材料工程学院上海 201602

收稿日期:2022-10-18 修回日期:2023-05-30 出版日期:2023-11-20 发布日期:2024-02-19
通讯作者: 张墅野(通信作者)，男，1988年出生，博士，副教授。主要研究方向为微连接与可靠性。E-mail：syzhang@hit.edu.cn
作者简介:何鹏，男，1972年出生，博士，长聘教授。主要研究方向为钎焊与微连接。E-mail：hithepeng@hit.edu.cn
基金资助:
重庆市自然科学基金(cstc2021jcyj-msxmX1002)、中央高校基本科研业务费专项资金(AUGA5710051221)和国家重点研发计划(2020YFE0205300)资助项目。

Current Status of Electronic Packaging Materials Using Machine Learning

ZHANG Shuye^1,2, DUAN Xiaokang¹, LUO Keyu¹, XU Sunwu¹, ZHANG Zhihao³, CHEN Jieshi⁴, HE Peng¹

1. State Key Laboratory of Advanced Welding and Joining, Harbin Institute of Technology, Harbin 150001;
2. Chongqing Research Institute, Harbin Institute of Technology, Chongqing 401135;
3. Department of Materials Science and Engineering, Xiamen University, Xiamen 361005;
4. School of Materials Engineering, Shanghai University of Engineering Science, Shanghai 201602

Received:2022-10-18 Revised:2023-05-30 Online:2023-11-20 Published:2024-02-19

摘要/Abstract

摘要： 随着“后摩尔时代”到来，先进封装技术使芯片性能不断提升，传统的材料开发制备以及封装互连研究难以适应时代需要。根据工业4.0的愿景，采用机器学习的算法在封装材料开发预测、封装工艺优化、焊点可靠性等方面进行高效预测工作，是未来发展的趋势之一。材料领域应用机器学习的过程，包括收集数据、数据预处理、调参优化三方面。封装领域中大多数预测问题可以归类为分类问题，传统监督学习应用最为广泛，常用算法有支持向量机、人工神经网络等。深度学习在封装问题的预测中大放异彩，如卷积神经网络模型等深度学习算法已经逐渐应该用于预测中，并且获得极佳的预测效果。尽管机器学习辅助电子封装研究尚处于萌芽状态，但仍然能够看到机器学习在电子封装领域具有巨大研究及实用价值。

关键词: 机器学习, 电子封装, 高熵合金, 可靠性, 金属间化合物

Abstract: With the advent of “post-Moore era”, advanced packaging technology improves the performance of chips, and traditional materials development and preparation and research of packaging interconnect are difficult to meet the needs of the era. According to the vision of industry 4.0, it is one of the future development trends to use machine learning algorithm to efficiently predict packaging material development, packaging process optimization, solder joint reliability. The process of applying machine learning in material field includes data collection, data preprocessing and parameter tuning optimization. Deep learning shines in the prediction of packaging problems. Deep learning algorithms such as convolutional neural network model have gradually been applied to prediction and achieved excellent prediction results. Although the research of machine learning-assisted electronic packaging is still in its infancy, machine learning still has great research and practical value.

Key words: machine learning, electronic packaging, material development, reliability, intermetallic compound

中图分类号:

TG156

张墅野, 段晓康, 罗克宇, 许孙武, 张志昊, 陈捷狮, 何鹏. 基于机器学习电子封装互连材料研究现状[J]. 机械工程学报, 2023, 59(22): 222-233.

ZHANG Shuye, DUAN Xiaokang, LUO Keyu, XU Sunwu, ZHANG Zhihao, CHEN Jieshi, HE Peng. Current Status of Electronic Packaging Materials Using Machine Learning[J]. Journal of Mechanical Engineering, 2023, 59(22): 222-233.

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基于机器学习电子封装互连材料研究现状

Current Status of Electronic Packaging Materials Using Machine Learning

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