An Efficient SRAM Yield Analysis Method using Multi-Fidelity Neural Network

As microelectronic fabrication technology advances rapidly, the yield of static random access memory (SRAM) blocks has to be guaranteed at a high level due to the large number of replicated cells. Accurate and efficient yield analysis methods are in great demand to reduce manufacturing costs induced by process variations. In this article, we integrate multi-fidelity (MF) neural networks as surrogate models into the importance sampling (IS) method, which expedites the search process for optimal shift vectors (OSV). Compared to the conventional OSV searching methods, the proposed method significantly reduces the number of simulations required for model training while maintaining accuracy. Finally, the failure rates are estimated using IS process until convergence. The experimental results on the 64-bit SRAM column show that preserves the advantages of IS-based methods, achieving up to 2.1x to 14.3x the efficiency and accuracy compared to the state-of-the-art methods for high-dimensional circuits.