Learning-Based Cell-Aware Defect Diagnosis of Customer Returns

Safa Mhamdi (1) , Patrick Girard (1) , Arnaud Virazel (1) , Alberto Bosio (2, 3) , Aymen Ladhar (4)

1 TEST - Test and dEpendability of microelectronic integrated SysTems
2 ECL - École Centrale de Lyon
3 INL - CSH - INL - Conception de Systèmes Hétérogènes
4 ST-CROLLES - STMicroelectronics [Crolles]

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.

Keywords: Diagnosis; Customer Returns; Machine Learning

Domains: Micro and nanotechnologies/Microelectronics

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⟩