An Accurate and Efficient Yield Analysis for SRAM dynamic metrics Using Differential Evolution Algorithm Accurately and efficiently estimate the extremely small failure rate of the SRAM cell is a challenging issue. In this paper, we develop an importance sampling with the differential evolution algorithm to extend the minimized norm importance sampling and mixture importance sampling. The key idea is searching the optimal shift vector (OSV) in the complex and nonconvex failure region using the improved differential algorithm efficiently. Then we construct a mixture of an original distribution and uniform distribution and a distort distribution based the searched OSV to estimate the failure rate of SRAM cell accurately. Our experiment results of a 40nm SRAM Cell show that the proposed method has achieved 1.31x1.44x speedup than stateofart methods and 7196x than Monte Carlo (MC) method with less than 2% error compared with MC result when the high accuracy is required (e.g., the relative error defined by the 99% confidence interval reaches 5%).

Adaptive LowRank Tensor Approximation for SRAM Yield Analysis using Bootstrap Resampling With increasing design complexity and reliability requirement, performance failure has become a main threat for a robust circuit design. The major challenge to develop a highsigma yield analysis method is xe2x80x9cCurse of Dimensionalityxe2x80x9d. In this paper, we propose a novel Adaptive LowRank Tensor Approximation (Adaptive LRTA) method. It starts with a Latin Hypercube Sampling set to build the initial LRTA metamodel. Our adaptive framework has multiple iterations of metamodel refinement. In each iteration, we evaluate the confidence interval by constructing a set of bootstrap resampled LRTA metamodels, and develop a learning function which determines our experimental design enrichment criteria. The experiment results on highdimensional SRAM column validate that our method has better efficiency and higher accuracy. Our adaptive LRTA uses 26X fewer samples than stateoftheart methods without compromising accuracy.