Thermal reliability prediction and analysis for high-density electronic systems based on the Markov process. (January 2016)

• Record Type: Journal Article
• Authors: Wan, Yi; Huang, Hailong; Das, Diganta; Pecht, Michael
• Is Part Of: Microelectronics and reliability. Volume 56(2016)
• Page Start: 182
• Page End: 188
• Publication Date: 2016-01
• Subjects: Electronic systems -- Thermal reliability estimation and prediction -- Stochastic process -- Markov theory -- The feature parameters of thermal reliability evaluation; Electronic apparatus and appliances -- Reliability -- Periodicals; Miniature electronic equipment -- Periodicals; Appareils électroniques -- Fiabilité -- Périodiques; Équipement électronique miniaturisé -- Périodiques; Electronic apparatus and appliances -- Reliability; Miniature electronic equipment; Periodicals; 621.3815
• DOI: 10.1016/j.microrel.2015.10.006
• Languages: English
• ISSNs: 0026-2714

Abstract

Abstract: Thermal-mechanical fatigue is one of the main failure modes for electronic systems, particularly for high-density electronic systems with high-power components. Thermal reliability estimation and prediction have been an increasing concern for improving the safety and reliability of electronic systems. In this paper, we propose a stochastic process prediction model to estimate the thermal reliability of an electronic system based on Markov theory. We first divided the high-density electronic systems into four modules: the energy transformation and protection module, the electronic control module, the connection module, and the signal transmission and transformation module. By integrating failure and repair characteristics of the four modules, a stochastic model of thermal reliability analysis and prediction for a whole electronic system was built based on the Markov process. The feature parameters of thermal reliability evaluation, including thermal reliability, thermal failure probability, mean time between thermal faults, and thermal stable availability, were derived based on our comprehensive model. Finally, we applied the model to an indoor electronic system of DC frequency conversion conditioning. The thermal reliability was estimated and predicted using tested failure and debugging repair data. Effective methods for improving thermal reliability are presented and analyzed based on the comprehensive Markov model. Highlights: A Markov model of thermal reliability prediction for electronic systems was built. The feature parameters of thermal reliability was derived by the comprehensive model. The model was applied to an actual electronic system, thermal reliability was predicted and analyzed. Effective methods for improving thermal reliability are presented.