Predictive Modeling Sheds Useful Light on Burn-In Testing (BIT): Brief Review and Recent Extension

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Microelectronics Reliability

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In this brief-review some important published work on burn-in-testing (BIT) in electronics and photonics (E&P) manufacturing is considered. The emphasis is on the role and significance of modeling and, particularly, on analytical (“mathematical”) modeling. All the results and conclusions are equally applicable to micro-electronics systems (MEMS) and to photonic MEMS (MOEMS). Three predictive models are addressed: 1) The model based on the analysis of the infant mortality portion (IMP) of the (non-random) bathtub curve (BTC) suggests that the time derivative of the failure rate at the beginning of this portion could be viewed as a suitable criterion to answer the basic question of the BIT endeavor: “to BIT or not to BIT?”. If this derivative is zero, the IMP of the BTC is parallel to the horizontal, time, axis, so that the IMP simply does not exist, and therefore no BIT is necessary. In another extreme case, when this derivative is significant (with a “minus” sign, of course), the IMP of the BTC clings to the vertical, failure-rate, axis, and although the undesirable “freaks” do exist, they could be easily eliminated by a short and low level BIT; the unfavorable, materials degradation and, hence, physics of failure related failure rate (PFR), does not play a role during this initial stage of the IMP of the BTC and is not considered in this model; 2) The model based on the analysis of the random failure rate (RFR) of the numerous mass-produced components that the manufactured product of interest is comprised of suggests that the above derivative is, in effect, the variance of the RFR of these components; their actual failure rates are, as a rule, unknown, and could very well vary in a very wide range, from zero to infinity; and 3) The model based on the use of the multi-parametric Boltzmann-Arrhenius-Zhurkov (BAZ) constitutive equation can be effectively employed to establish the BIT's adequate duration and level, if this failure-oriented-accelerated-testing (FOAT) is found to be necessary. The findings obtained using these models 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 be focused on the experimental validation and possible extension of the results and recommendations addressed in this analysis.


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