Predicting Failure Distributions of SRAM Arrays by Using Extreme Value Statistic, Bit Cell Simulation, and Machine Learning

We present a successful methodology for accurately predicting failure distributions of SRAM arrays based on simulation results of a single bit cell. This was achieved by using extreme value statistic and a data-centric approach of supervised machine learning. The purpose of such predictions is to enable assessments of the trade-off between optimizing power, performance, and area (PPA) and safeguarding reliability in an early phase of development.