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

Abstract

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).