Understanding SRAM Stability via Bifurcation Analysis

Abstract

In the past decades, aggressive scaling of transistor feature size has been a primary force driving higher Static Random Access Memory (SRAM) integration density. Due to technology scaling, nanometer SRAM designs become increasingly vulnerable to stability challenges. The traditional way of analyzing stability is through the use of Static Noise Margins (SNMs). SNMs are not capable of capturing the key nonlinear dynamics associated with memory operations, leading to imprecise characterization of stability. This work rigorously develops dynamic stability concepts and, more importantly, captures them in physically based analytical models. By leveraging nonlinear stability theory, we develop analytical models that characterize the minimum required amplitude and duration of injected current noises that can flip the SRAM state. These models, which are parameterized in key design, technology, and operating condition parameters, provide important design insights and offer a basis for predicting scaling trends of SRAM dynamic stability.