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Learning-Based Cell-Aware Defect Diagnosis of Customer Returns

Safa Mhamdi 1 Patrick Girard 1 Arnaud Virazel 1 Alberto Bosio 2 Aymen Ladhar 3
1 TEST - TEST
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier
Abstract : In this paper, we propose a new framework for cellaware defect diagnosis of customer returns based on supervised learning. The proposed method comprehensively deals with static and dynamic defects that may occur in real circuits. A Naive Bayes classifier is used to precisely identify defect candidates. Results obtained on benchmark circuits, and comparison with a commercial cell-aware diagnosis tool, demonstrate the efficiency of the proposed approach in terms of accuracy and resolution.
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https://hal-lirmm.ccsd.cnrs.fr/lirmm-03035669
Contributor : Isabelle Gouat <>
Submitted on : Wednesday, December 2, 2020 - 12:12:14 PM
Last modification on : Thursday, December 3, 2020 - 3:22:25 AM
Long-term archiving on: : Wednesday, March 3, 2021 - 7:08:08 PM

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Safa Mhamdi, Patrick Girard, Arnaud Virazel, Alberto Bosio, Aymen Ladhar. Learning-Based Cell-Aware Defect Diagnosis of Customer Returns. IEEE European Test Symposium (ETS), May 2020, Tallinn, Estonia. pp.1-2, ⟨10.1109/ETS48528.2020.9131601⟩. ⟨lirmm-03035669⟩

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