Learning-Based Cell-Aware Defect Diagnosis of Customer Returns - LIRMM - Laboratoire d’Informatique, de Robotique et de Microélectronique de Montpellier Access content directly
Conference Papers Year : 2020

Learning-Based Cell-Aware Defect Diagnosis of Customer Returns

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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Dates and versions

lirmm-03035669 , version 1 (02-12-2020)

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Safa Mhamdi, Patrick Girard, Arnaud Virazel, Alberto Bosio, Aymen Ladhar. Learning-Based Cell-Aware Defect Diagnosis of Customer Returns. ETS 2020 - 25th IEEE European Test Symposium, May 2020, Tallinn, Estonia. pp.1-2, ⟨10.1109/ETS48528.2020.9131601⟩. ⟨lirmm-03035669⟩
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