Minimum Supply Voltage and Yield Estimation for Large SRAMs Under Parametric Variations

摘要

SRAM cell minimum operation voltage (Vmin) exhibits a skewed distribution in the presence of random parametric variations. Standard Monte Carlo (MC) simulation is prohibitively expensive to estimate the tail of the Vmin distribution for large SRAMs. We propose a fast and accurate method to estimate Vmin based on the statistical trend of static noise margin with ${V}_{rm DD}$ scaling. Our preliminary work has shown its efficiency for standby Vmin estimation. In this work, we extend the method to estimate read and write Vmin and yield. We also generalize it for both symmetric and asymmetric types of cells. With comparable accuracy, the proposed model offers a huge speedup over standard MC. Compared with an alternative fast MC method, importance sampling, it shows a good agreement with less complexity.