Identification of Multiple Failure Mechanisms for Device Reliability Using Differential Evolution

Abstract

Assessing the reliability of electronic devices, circuits and packages requires accurate lifetime predictions and identification of failure modes. This paper demonstrates a new approach to the extraction of underlying failure mechanism distribution parameters from data corresponding to a combined distribution of two distinct mechanisms. Specifically, a differential evolution approach is developed for parameter identification in competing-risks and mixture models. Use of multiple metrics for performance evaluation shows that our approach outperforms the best-known methods in the literature. Numerical results are shown for simulated data and also for package-level and device-level real failure data. On the modeling of industrial package failure data, our approach provides up to 92% reduction in mean squared error, up to 7% increase in log-likelihood and up to 61% decrease in the maximum Kolmogorov-Smirnov distance. On ring oscillator data obtained from our laboratory experiments, the corresponding improvements are 94%, 5% and 77%, respectively. For both simulated and real datasets, the improvement in performance is validated through statistical tests of significance. An application of the approach is demonstrated for empirical extraction of the temperature-dependence of parameters from lifetime data at different test temperatures.