Methodology for determining the influencing factors of lifetime variation for power devices

This paper proposes a method for explanation of the lifetime variation of power devices using data from different test stages. Understanding the lifetime variation is very useful in qualification, as well as in the characterization process, in order to improve the robustness of the power devices or to estimate more accurately the minimum guaranteed lifetime. Moreover, it helps design engineers better understand the root causes of the lifetime variation and use this knowledge to improve the performances of new power devices. In the proposed methodology, the variation of the lifetime is explained by the electrical parameters, measured before the stress-test. The Sensitivity Analysis presented here has the advantage of being simple and fast. It can be applied even when the number of test-runs is less than the number of factors. Moreover, it reveals not only linear correlations, but also quadratic effects and 2nd and 3rd order interactions. Eventually, the method provides the top of the most relevant electrical parameters which explain the lifetime variation. The validation of this approach has shown that 72% of the lifetime variation can be explained by the initial values of 5 electrical parameters.

[1]  Andi Buzo,et al.  COMPARISON OF SENSITIVITY ANALYSIS METHODS IN HIGH-DIMENSIONAL VERIFICATION SPACES , 2016 .

[2]  Boying Liu,et al.  Characterization of Initial Parameter Information for Lifetime Prediction of Electronic Devices , 2016, PloS one.

[3]  Kathrin Plankensteiner,et al.  Application of Bayesian networks to predict SMART power semiconductor lifetime , 2013, Proceedings of the 2013 9th Conference on Ph.D. Research in Microelectronics and Electronics (PRIME).

[4]  Andi Buzo,et al.  Application-aware lifetime estimation of power devices , 2017, 2017 22nd IEEE European Test Symposium (ETS).

[5]  Andi Buzo,et al.  A novel entropy-based sensitivity analysis approach for complex systems , 2016, 2016 IEEE Symposium Series on Computational Intelligence (SSCI).