An Approach to the Design of Optimal Test Scheduling for System-On-Chip Based on Genetic Algorithm

Sakthivel, P.; Narayanasamy, P.

doi:10.1007/978-1-4020-6268-1_5

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“…Work on test optimization is found in the statistics, operations research, electronic engineering, software engineering and production management literature; recent papers from these fields include , , , and . A wide variety of models and optimization algorithms have been proposed; ant colony and genetic algorithms are currently popular for optimization. Ideally, some sensitivity analysis is also conducted.…”

Section: Introductionmentioning

confidence: 99%

Estimating production test properties from test measurement data

Wilson

Goyal²

2011

Appl Stoch Models Bus & Ind

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A complex sequence of tests on components and the system is a part of many manufacturing processes. Statistical imperfect test and repair models can be used to derive the properties of such test sequences but require model parameters to be specified. We describe a technique for estimating such parameters from typical data that are available from past testing. A Gaussian mixture model is used to model the wide variety of statistical properties of test data, including outliers, multi-modality and skewness, from which test properties are derived. Model fitting is through a Bayesian approach, implented by Markov chain Monte Carlo.

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