Journal of Beijing University of Posts and Telecommunications

  • EI核心期刊

Journal of Beijing University of Posts and Telecommunications ›› 2020, Vol. 43 ›› Issue (3): 11-18,31.doi: 10.13190/j.jbupt.2019-170

• Papers • Previous Articles     Next Articles

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

ZHOU Yi-lin, YANG Lu, LU Wen-rui   

  1. Automation School, Beijing University of Posts and Telecommunications, Beijing 100876, China
  • Received:2019-08-13 Online:2020-06-28 Published:2020-06-24
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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

CLC Number: