尘土颗粒影响下电路板电化学迁移失效寿命建模探索

doi:10.13190/j.jbupt.2019-170

北京邮电大学学报 ›› 2020, Vol. 43 ›› Issue (3): 11-18,31.doi: 10.13190/j.jbupt.2019-170

尘土颗粒影响下电路板电化学迁移失效寿命建模探索

周怡琳, 杨璐, 鲁文睿

北京邮电大学自动化学院, 北京 100876

收稿日期:2019-08-13 出版日期:2020-06-28 发布日期:2020-06-24
作者简介:周怡琳(1972-),女,教授,E-mail:ylzhou@bupt.edu.cn.
基金资助:
国家自然科学基金项目（61674017）

Exploring the Life Modeling Methods for Electrochemical Migration Failure of Printed Circuit Board under Dust Particles

ZHOU Yi-lin, YANG Lu, LU Wen-rui

Automation School, Beijing University of Posts and Telecommunications, Beijing 100876, China

Received:2019-08-13 Online:2020-06-28 Published:2020-06-24
Supported by:

摘要/Abstract

摘要： 针对离散尘土颗粒与温度、湿度、电场强度交互作用的复杂条件下，难以有效建立电路板电化学迁移的失效物理模型的问题，通过温湿偏置加速实验，模拟不同积尘密度下电路板的电化学迁移失效，分析颗粒分布密度对电路板绝缘失效时间的作用特性.采用正交实验获取不同尘土颗粒密度、温度、湿度、电场条件下电路板绝缘失效的寿命数据.基于数据驱动的方法，探讨电路板在尘土颗粒污染下的电化学迁移失效寿命建模.对比了多项式回归、机器学习中的梯度提升回归树和随机森林3种方法在尘土分布密度的高低区间内的寿命预测效果.讨论了尘土颗粒污染下利用机器学习建立电路板电化学迁移失效寿命模型的有效性.

关键词: 尘土污染, 电化学迁移, 寿命模型, 机器学习

Abstract: Facing the complex conditions that the discrete dust particles interact with the temperature, the humidity, and the electric field intensity, it is difficult to effectively establish the life model of electrochemical migration (ECM) of printed circuit board (PCB) based on failure physics. Through the temperature humidity bias tests, the ECM process under different dust density is simulated. The effect of particle distribution density on time to failure (TTF) of PCB is analyzed. The TTF data of PCB under different particle distribution density, temperature, relative humidity and electric field intensity are obtained by an orthogonal experiment. Based on the data driven method, the ECM life modeling of PCB under dust particle pollution is discussed. The life prediction effects of polynomial regression, gradient boosting regression tree and random forest in machine learning for high and low dust distribution density are compared. The effectiveness of machine learning to establish ECM life model of PCB under dust particle contamination is discussed.

Key words: dust contamination, electrochemical migration, life model, machine learning

中图分类号:

TN406

周怡琳, 杨璐, 鲁文睿. 尘土颗粒影响下电路板电化学迁移失效寿命建模探索[J]. 北京邮电大学学报, 2020, 43(3): 11-18,31.

ZHOU Yi-lin, YANG Lu, LU Wen-rui. Exploring the Life Modeling Methods for Electrochemical Migration Failure of Printed Circuit Board under Dust Particles[J]. Journal of Beijing University of Posts and Telecommunications, 2020, 43(3): 11-18,31.

参考文献

[1] Kohman G T, Hermance H W, Downes G H. Silver migration in electrical insulation[J]. Bell System Technical Journal, 1955, 34(6):1115-1147.
[2] Zhang Jigao. Effect of dust contamination on electrical contact failure[C]//53th IEEE Holm Conference on Electrical Contacts. Pittsburgh:[s. n.], 2007:21-30.
[3] 周怡琳, 韦霞霞. 尘土污染对电路板表面湿度的影响[J]. 电工技术学报, 2015, 30(23):163-168. Zhou Yilin, Wei Xiaxia. Effects of dust contamination on surface relative humidity of printed circuit board[J]. Transactions of China Electrotechnical Society, 2015, 30(23):163-168.
[4] 周怡琳, 朱蒙, 霍雨佳. 尘土颗粒的介电特性对电路板电化学迁移的影响[J]. 电子学报, 2017, 45(7):1758-1763. Zhou Yilin, Zhu Meng, Huo Yujia. The influence of the dielectric properties of dust particles on electrochemical migration of printed circuit board[J]. Acta Electronica Sinica, 2017, 45(7):1758-1763.
[5] Giulio D. Metal migration(Ag, Cu, Pb) in encapsulated modules and time-to-fail model as a function of the environment and package properties[C]//20^th International Reliability Physics Symposium. San Diego:[s. n.], 1982:27-33.
[6] Yang S, Christou A. Failure model for silver electrochemical migration[J]. IEEE Transactions on Device and Materials Reliability, 2007, 7(1):188-196.
[7] Zhou Yilin, Chen Yanyu, Xie Qi, et al. Modeling research of electrochemical migration failure on printed circuit board[C]//Prognostics and System Health Management Conference. Chongqing: [s. n. ], 2018: 26-28.
[8] Tremblay G, Legendre P, Jean François Doyon, et al. The use of polynomial regression analysis with indicator variables for interpretation of mercury in fish data[J]. Biogeochemistry, 1998, 40(2/3): 189-201.
[9] Zhou Sanyu. An exact method for the multiple comparison of several polynomial regression models with applications in dose-response study [J]. Advances in Statistical Analysis, 2018, 102(3): 413-429.
[10] 谢琪. 尘土中可溶性盐导致高密度电路板电化学迁移失效的建模研究[D]. 北京: 北京邮电大学, 2018.
[11] Wu Dazhong, Connor Jennings, Janis Terpenny, et al. A comparative study on machine learning algorithms for smart manufacturing: tool wear prediction using random forests[J]. Journal of Manufacturing Science & Engineering, 2017, 139(7): 071018-1-071018-9.
[12] Wang Jidong, Li Peng, Ran Ran, et al. A short-term photovoltaic power prediction model based on the gradient boost decision tree[J]. Applied Sciences, 2018, 8(5): 689-702.
[13] IPC. Electrochemical migration resistance test: IPC TM-6502.6.14.1-2000[S]. [S.l.]: US-IPC, 2000.
[14] Zhao Yanchang. R语言与数据挖掘最佳实践和经典案例[M]. 陈健, 黄琰, 译. 北京: 机械工业出版社, 2014: 267-289.
[15] 李航. 统计学习方法[M]. 北京: 清华大学出版社, 2012: 151-152.
[16] 谢晓龙. 航空发动机性能评价与衰退预测方法研究[D]. 哈尔滨: 哈尔滨工业大学, 2016.

尘土颗粒影响下电路板电化学迁移失效寿命建模探索

Exploring the Life Modeling Methods for Electrochemical Migration Failure of Printed Circuit Board under Dust Particles

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