Adaptive Clustering and Sampling for High-Dimensional and Multi-Failure-Region SRAM Yield Analysis

Statistical circuit simulation is exhibiting increasing importance for memory circuits under process variation. It is challenging to accurately estimate the extremely low failure probability as it becomes a high-dimensional and multi-failure-region problem. In this paper, we develop an Adaptive Clustering and Sampling (ACS) method. ACS proceeds iteratively to cluster samples and adjust sampling distribution, while most existing approaches pre-decide a static sampling distribution. By adaptively searching in multiple cone-shaped subspaces, ACS obtains better accuracy and efficiency. This result is validated by our experiments. For SRAM bit cell with single failure region, ACS requires 3-5X fewer samples and achieves better accuracy compared with existing approaches. For 576-dimensional SRAM column circuit with multiple failure regions, ACS is 2050X faster than MC without compromising accuracy, while other methods fail to converge to correct failure probability in our experiment.