Applying Bayesian mixtures-of-experts models to statistical description of smart power semiconductor reliability

Reliability prediction of semiconductor devices gains importance, since demand increases and resources, e.g. time, are restricted. Normally, methods focusing on technology aspects are applied. This work presents a more mathematical approach by using Bayesian statistics. Physical failure inspection and past research indicate that the data follow a bimodal distribution. Therefore, we suggest using a heteroscedastic mixture of two normal distributions to model the given data. To incorporate the dependency on different test settings, linear models are used for the means and the mixing proportion. Gamma distributions are proposed as priors for the model parameters, due to the physical restrictions concerning the sample space. For the variances hierarchical inverse gamma priors are applied. Sampling from the posterior is done by using Monte Carlo Markov Chain methods. The proposed mixtures-of-experts model shows good adaption to the behavior of the measurements as well as good prediction quality. (C) 2011 Elsevier Ltd. All rights reserved.