Seeking the Yield Barrier: High-Dimensional SRAM Evaluation Through
  Optimal Manifold
Yanfang Liu Guohao Dai Wei W. Xing
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.