Rapid estimation of the probability of SRAM failure via adaptive multi-level sliding-window statistical method

Yield 1 analysis of SRAM is a challenging issue, because the failure rates of SRAM cells are extremely small. In this article, an efficient non-Gaussian sampling method of cross entropy optimization is proposed for estimating the high sigma SRAM yield. Instead of sampling with the Gaussian distribution in existing methods, a non-Gaussian distribution, i.e., a joint one-dimensional generalized Pareto distribution and ( n -1)-dimensional Gaussian distribution, is taken as the function family of practical distribution, which is proved to be more suitable to fit the ideal distribution in the view of extreme failure event. To minimize the cross entropy between practical and ideal distributions, a sequential quadratic programing solver with multiple starting points strategy is applied for calculating the optimal parameters of practical distributions. Experimental results show that the proposed non-Gaussian sampling is a 2.2--4.1× speedup over the Gaussian sampling, on the whole, it is about a 1.6--2.3× speedup over state-of-the-art methods with low- and high-dimensional cases without loss of accuracy

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An efficient Bayesian yield estimation method for high dimensional and high sigma SRAM circuits

Zhai

Yan

Wang

et al. 2018

Proceedings of the 55th Annual Design Automation Conference

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Rapid estimation of the probability of SRAM failure via adaptive multi-level sliding-window statistical method

Cited by 3 publications

References 25 publications

An Efficient Bayesian Yield Estimation Method for High Dimensional and High Sigma SRAM Circuits

An Efficient Bayesian Yield Estimation Method for High Dimensional and High Sigma SRAM Circuits

An Efficient Non-Gaussian Sampling Method for High Sigma SRAM Yield Analysis

An efficient Bayesian yield estimation method for high dimensional and high sigma SRAM circuits

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