Rapid estimation of the probability of SRAM failure via adaptive multi-level sliding-window statistical method

Abstract

Statistical analysis of SRAM has emerged as a challenging issue because the SRAM cell failure probability is extremely small. In this paper, we develop a novel efficient sampling, searching and estimating method to capture the probability of SRAM failure. Particularly, we propose an innovative Adaptive Multi-Level Sliding-Window (AMLSW) method to find the failure boundary in the parameter space with less computational cost. The proposed AMLSW method applies an integrated optimization engine to adaptively explore the failure boundary by sampling a sequence of parameter points and calculating the simulation result in sliding windows. Its key features include ellipsoid transformation, multi-level grid partition and sliding window algorithm to make our method efficient and accurate for finding the SRAM failure probability and failure boundary in parameter space. Furthermore, we provide theoretical analysis on our new AMLSW method. The experimental results of a commercial 65nm SRAM cell demonstrate that the AMLSW method achieves 1.7–40× runtime speed-up over the existing methods without surrendering accuracy, and dramatically high speed-up over normal M-C sampling method. Moreover, this new method provides the failure boundary in the parameter space as shown in experiments.