Being able to efficiently obtain an accurate estimate of the failure probability of SRAM components has become a central issue as model circuits shrink their scale to submicrometer with advanced technology nodes. In this work, we revisit the classic norm minimization method. We then generalize it with infinite components and derive the novel optimal manifold concept, which bridges the surrogate-based and importance sampling (IS) yield estimation methods. We then derive a sub-optimal manifold, optimal hypersphere, which leads to an efficient sampling method being aware of the failure boundary called onion sampling. Finally, we use a neural coupling flow (which learns from samples like a surrogate model) as the IS proposal distribution. These combinations give rise to a novel yield estimation method, named Optimal Manifold Important Sampling (OPTIMIS), which keeps the advantages of the surrogate and IS methods to deliver state-of-the-art performance with robustness and consistency, with up to 3.5x in efficiency and 3x in accuracy over the best of SOTA methods in High-dimensional SRAM evaluation.

翻译：随着先进工艺节点下电路尺寸缩小至亚微米级，高效获取SRAM元件失效概率的精确估计已成为核心问题。本文重新审视经典范数最小化方法，将其推广至无限分量场景，并推导出连接代理模型与重要性采样（IS）良率估计方法的新型最优流形概念。接着我们推导出次优流形——最优超球面，并由此提出一种能够感知失效边界的采样方法——洋葱采样。最后，采用神经耦合流（类似代理模型从样本中学习）作为重要性采样的提议分布。上述方法融合形成名为最优流形重要性采样（OPTIMIS）的新型良率估计方法，该方法兼具代理模型与重要性采样的优势，在高维SRAM评估中实现了鲁棒性与一致性兼具的先进性能：相较于最优现有方法，效率最高提升3.5倍，精度最高提升3倍。