Stability Estimation of a 6T-SRAM Cell Using a Nonlinear Regression

Abstract

Static noise margin is one of the key metrics to estimate the likelihood of failure of a 6T-static random-access memory (SRAM) cell. This paper proposes a technique to accurately estimate the stability of a conventional SRAM cell without modifying the cell structure. The main idea is to measure the specific cell's currents with variant supply levels via the bit lines. The measured currents are used to estimate the read stability and the write ability through a nonlinear regression. The R2 (coefficient of determination) of the stability estimation is as high as 0.95 when applied to an arbitrary data set. As typical stability definitions require an access to the internal node of a 6T-SRAM cell, alternative measurable stability metrics for read and write are surveyed and modified to improve the correlation with the conventional stability definition. With this alternative stability and the cell currents, the conversion rules from currents to the stability can be found from the measurement data. Simulation results show that the estimation error sigma is as small as 2.44% and 3% for the read stability and write-ability estimation, respectively. Validity of the idea is verified by Monte Carlo simulations by using SRAM models in a 45-nm CMOS technology.