# Seeking the Yield Barrier: High-Dimensional SRAM Evaluation Through Optimal Manifold

arXiv: https://arxiv.org/abs/2307.15773 · HTML: https://mlsanity.com/paper/2307.15773/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 11 references that are in the map.
- First posted on arXiv 28 Jul 2023.

## Builds on (most cited first, 2 of 2)

- [Neural Spline Flows](https://mlsanity.com/paper/1906.04032/) (2019)
- [High-Dimensional Yield Estimation using Shrinkage Deep Features and Maximization of Integral Entropy Reduction](https://mlsanity.com/paper/2212.02100/) (2022)

## Built on by (most cited first, 1 of 1)

- [Sensitivity Analysis for Active Sampling, with Applications to the Simulation of Analog Circuits](https://mlsanity.com/paper/2405.07971/) (2024)

## Abstract (arXiv)

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.