Robust importance sampling for efficient SRAM yield analysis

Abstract

Monte Carlo simulations have been widely adopted for analyzing circuit properties, such as SRAM yield, under strong influence of process variations. Enormous calculation time is required in such a simulation due to the low defect probabilities. In this paper, we propose a robust shift-vector determination for mean-shift importance sampling, by which efficiency and stability of the Monte Carlo simulation is improved. In the proposed method, the hypersphere sampling is developed to autonomously find the optimal shift-vector. The sampling is also limited to the regions where meaningful contribution to the yield is recognized. Simulation examples reveal that the proposed technique stably and efficiently estimates yield of noise stabilities of an SRAM cell. At the failure probability of 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-10</sup> , the number of calculation trials has been reduced by six orders magnitude compared with a conventional Monte Carlo simulation.