UCLA

The SRAM cell is an important memory component that is widely used in integrated circuit design. Its performance is crucial to the entire circuit. However, inevitable process variations have introduced significant changes in the performance of fabricated SRAM cells and led to severe circuit failure. Consequently, the failure probability of an SRAM cell must be kept extremely small. These extremely small probability events are considered to be "rare events". The most straightforward method of estimating failure probability is using the classical Monte Carlo method. However, this method is extremely impractical in the case of rare events because of its drastically long run time. Therefore, a method to efficiently estimate the failure probability of rare events is strongly desired.

In this thesis, a novel and fast failure analysis for SRAM cells is proposed to efficiently and accurately estimate the failure probability of a rare event. The proposed approach is based on Probability Collective based Importance Sampling. This approach increases the convergence rate by finding the closest approximation of the optimal distribution used in Importance Sampling. In order to find the closest approximation of the optimal sampling distribution, the proposed method minimizes the Figure of Merit of the estimated probability in each iteration. Experimental results show that the proposed method can have an average of 5x speed up compared to Probability Collective based Importance Sampling. Moreover, multiple trials between these two methods show that the proposed method offers a faster convergence rate and greater stability.