Sequential importance sampling for low-probability and high-dimensional SRAM yield analysis

In this paper, a significant acceleration of estimating low-failure rate in a high-dimensional SRAM yield analysis is achieved using sequential importance sampling. The proposed method systematically, autonomously, and adaptively explores failure region of interest, whereas all previous works needed to resort to brute-force search. Elimination of brute-force search and adaptive trial distribution significantly improves the efficiency of failure-rate estimation of hitherto unsolved high-dimensional cases wherein a lot of variation sources including threshold voltages, channel-length, carrier mobility, etc. are simultaneously considered. The proposed method is applicable to wide range of Monte Carlo simulation analyses dealing with high-dimensional problem of rare events. In SRAM yield estimation example, we achieved 10⁶ times acceleration compared to a standard Monte Carlo simulation for a failure probability of 3 × 10^-9 in a six-dimensional problem. The example of 24-dimensional analysis on which other methods are ineffective is also presented.