An Efficient Yield Estimation Method for Layouts of High Dimensional and High Sigma SRAM Arrays

This paper firstly focuses on yield estimation problem on post-layout-simulation of high dimensional SRAM arrays. Post-layout-simulation is much more credible than pre-simulation. However, it introduces strong relationship among SRAM columns. The Multi-Fidelity Gaussian Process model between the small and the large SRAM arrays near Optimal Shift Vector (OSV) is built. An iterative strategy is proposed and Multi-Modal method is applied to obtain more prior knowledge of the small SRAM arrays and further accelerate convergence. Experimental results show that the proposed method can gain 5-7x speedup with less relative errors than the state-of-the-art method for 384D cases.