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Communication Dans Un Congrès Année : 2023

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

Résumé

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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Dates et versions

hal-04164855 , version 1 (18-07-2023)

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Citer

Xhesila Xhafa, Patrick Girard, Arnaud Virazel. Learning-Based Characterization Models for Quality Assurance of Emerging Memory Technologies. ETS 2023 - 28th IEEE European Test Symposium, May 2023, Venezia, Italy. pp.1-2, ⟨10.1109/ETS56758.2023.10174202⟩. ⟨hal-04164855⟩
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