Burn-in-Testing (BIT) Challenge: to BIT or not to BIT?

Abstract

Abstract Burn-in testing (BIT) is a costly undertaking. Predictive modeling enables shading useful light on what and how should be tested, if at all. Three analytical (“mathematical”) predictive models recently suggested by the author are addressed in this mini-review: 1) A model based on the analysis of the infant mortality portion (IMP) of the bathtub curve (BTC) suggests that the non-random time derivative of the expected “statistical” failure rate (SFR) at the beginning of the IMP could be viewed as a suitable criterion (“figure of merit”) to answer the basic question of the BIT undertaking: “to BIT or not to BIT?” 2) A model based on the analysis of the random failure rate (RFR) of the mass-produced components that the manufactured product of interest is comprised of suggests that the above derivative is, in effect, the RFR variance of these components. 3) A model based on the use of the kinetic multi-parametric Boltzmann-Arrhenius-Zhurkov (BAZ) constitutive equation is employed to establish the BIT's adequate duration and level, if this kind of failure-oriented-accelerated-testing (FOAT) is found to be necessary. The theoretical findings are illustrated by calculated data. It is concluded that predictive modeling should always precede the actual BIT, that analytical modeling should always complement computer simulations and that future work should address the experimental validation and possible extension of the obtained results and recommendations.