Implementing model redundancy in predictive alternate test to improve test confidence

Haithem Ayari 1 Florence Azaïs 1 Serge Bernard 1 Mariane Comte 1 Vincent Kerzérho 1 Olivier Potin 1 Michel Renovell 1
1 SysMIC - Conception et Test de Systèmes MICroélectroniques
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier
Abstract : This work investigates new implementations of the predictive alternate test strategy that exploit model redundancy in order to improve test confidence. The key idea is to build during the training phase, not only one regression model for each specification as in the classical implementation, but several regression models. We explore various options for implementing model redundancy, based on the use of different indirect measurement combinations and/or different partitions of the training set.
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Poster communications
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https://hal-lirmm.ccsd.cnrs.fr/lirmm-00820077
Contributor : Florence Azais <>
Submitted on : Friday, May 3, 2013 - 9:40:04 AM
Last modification on : Saturday, January 19, 2019 - 1:20:17 AM

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Haithem Ayari, Florence Azaïs, Serge Bernard, Mariane Comte, Vincent Kerzérho, et al.. Implementing model redundancy in predictive alternate test to improve test confidence. ETS: European Test Symposium, May 2013, Avignon, France. 18th IEEE European Test Symposium, 2013, ⟨10.1109/ETS.2013.6569386⟩. ⟨lirmm-00820077⟩

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