31 May 2007 Life testing of electronic components: a Bayesian approach
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Proceedings Volume 6635, Advanced Topics in Optoelectronics, Microelectronics, and Nanotechnologies III; 66351C (2007) https://doi.org/10.1117/12.742113
Event: Advanced Topics in Optoelectronics, Microelectronics, and Nanotechnologies III, 2006, Bucharest, Romania
Abstract
A common problem of high reliability computing is, on one hand, the magnitude of total testing time required, particularly in the case of high reliability components and, on the other hand, the number of devices under test. In both cases, the objective is to minimize the costs involved in testing without reducing the quality of the data obtained. One solution is based on accelerated life testing techniques which permit to decrease testing time. Another solution is to incorporate prior beliefs, engineering experience, or previous data into the testing framework. It is in this spirit that the use of a Bayesian approach can, in many cases, significantly reduce the amount of devices required. The accelerated life testing (ALT) of electronic components (including the semiconductor devices) under severer than operating conditions involving high temperature, humidity, voltage, a.o. is commonly used to reduce test time and cost. The main problem is to estimate accelerated life model parameters allowing to define the reliability function under operating conditions from only accelerated life data. A difficulty of using the ALT, during design stage, is due to the small sample size to test. In this context, the Bayesian approach can be used to incorporate into the estimation process all available knowledge on accelerated life model (baseline failure rate, activation energy, a.o). This paper presents the study of Exponential-Arrhenius model by an evaluation of parameters using maximum likelihood and Bayesian methods. A Monte Carlo simulation has been performed to examine the asymptotic behavior of these different estimators.
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Sorin Voiculescu, Fabrice Guérin, Ioan C. Bacivarov, "Life testing of electronic components: a Bayesian approach", Proc. SPIE 6635, Advanced Topics in Optoelectronics, Microelectronics, and Nanotechnologies III, 66351C (31 May 2007); doi: 10.1117/12.742113; https://doi.org/10.1117/12.742113
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