Machine Learning-Based Read Access Yield Estimation and Design Optimization for High-Density SRAM

This article presents an efficient yield estimation method for the compensated-most probable failure point (C-MPFP) with the probability density function (PDF) estimation. Bayesian optimization (BO) is used to estimate the PDF. The computational cost can be significantly reduced using this method, while maintaining the accuracy. Our experimental results demonstrate that the proposed yield estimation method can reduce the simulation time by more than 20 times compared with the Brute force Monte Carlo (BMC). In addition, the BO-based automated static random-access memory read access design optimization method is proposed. The optimized design found by the proposed method demonstrates a 10% faster speed than arbitrarily selected designs while satisfying the target yield constraint. Consequently, the proposed design optimization process can efficiently reduce the computational cost of yield estimation for a given design as well as effectively reducing the number of times yield estimation is invoked with BO.