Learning-Based Characterization Models for Quality Assurance of Emerging Memory Technologies

Xhesila Xhafa; Patrick Girard; Arnaud Virazel

doi:10.1109/ETS56758.2023.10174202

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Learning-Based Characterization Models for Quality Assurance of Emerging Memory Technologies

Xhesila Xhafa, Patrick Girard et Arnaud Virazel

ETS 2023 - 28th IEEE European Test Symposium, pp.1-2

ETS 2023 - 28th IEEE European Test Symposium (Venezia, Italy, 22/05/2023–26/05/2023)

12/07/2023

DOI: https://doi.org/10.1109/ETS56758.2023.10174202

Résumé

Memory test

Emerging memories

Cell-Aware test

The shrinking of technology nodes has led to high-density memories containing large amounts of transistors which are prone to defects and reliability issues. Their test is generally based on the use of well-known March algorithms targeting Functional Fault Models (FFMs). This Ph.D. thesis aims to introduce a novel approach for advanced and emerging memory testing that relies on the Cell-Aware (CA) methodology to further improve the yield of System on Chips (SoCs).

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Titre: Learning-Based Characterization Models for Quality Assurance of Emerging Memory Technologies
Créateurs - sans rôle: Xhesila Xhafa - Laboratoire d'Informatique, de Robotique et de Microélectronique de Montpellier
Patrick Girard - Laboratoire d'Informatique, de Robotique et de Microélectronique de Montpellier
Arnaud Virazel - Laboratoire d'Informatique, de Robotique et de Microélectronique de Montpellier
Détails de publication: ETS 2023 - 28th IEEE European Test Symposium, pp.1-2
Colloque: ETS 2023 - 28th IEEE European Test Symposium (Venezia, Italy, 22/05/2023–26/05/2023)
Identifiants: 9946965709311
Unité académique: Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier - LIRMM
Langue: English
Type de ressource: Conference proceeding
Champs locaux: hal-04164855