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Owing to increase in parametric variations with technology scaling, accurate estimation of bit-cell failure probability in nano-scale static random access memory (SRAM) has become an extremely challenging task. In this study, the authors propose a method to detect the SRAM bit-cell failure, named `critical point sampling`. Using this technique, read and hold failure probability of an SRAM bit-cell can be efficiently estimated in a simulation-based way. Simulation results show that our estimation method provides high accuracy, while being `50` faster in computational speed compared to transient Monte-Carlo simulation. The method can be applied to optimise SRAM design for better yield and contributes significantly in reducing the overall design time.