One-Sided Static Noise Margin and Gaussian-Tail-Fitting Method for SRAM

Abstract

In this paper, we propose a method to estimate the read failure rate of a static random access memory (SRAM) cell. Conventional read stability metrics cannot accurately estimate the read failure probability as technology scales down, because some metrics cannot characterize read stability and others can no longer be approximated to a known distribution. We first introduce a one-sided static noise margin (OSNM), whose lower tail region follows a Gaussian distribution, and also propose a Gaussian-tail-fitting method that properly models the distribution of the OSNM at the tail region. It is demonstrated that the OSNM can accurately estimate the SRAM cell yield using the proposed Gaussian-tail-fitting method.