# Sensitivity Analysis for Active Sampling, with Applications to the Simulation of Analog Circuits

arXiv: https://arxiv.org/abs/2405.07971 · HTML: https://mlsanity.com/paper/2405.07971/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 15 references that are in the map.
- First posted on arXiv 13 May 2024.

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

- [High-Dimensional Yield Estimation using Shrinkage Deep Features and Maximization of Integral Entropy Reduction](https://mlsanity.com/paper/2212.02100/) (2022)
- [Seeking the Yield Barrier: High-Dimensional SRAM Evaluation Through Optimal Manifold](https://mlsanity.com/paper/2307.15773/) (2023)

## Abstract (arXiv)

We propose an active sampling flow, with the use-case of simulating the impact of combined variations on analog circuits. In such a context, given the large number of parameters, it is difficult to fit a surrogate model and to efficiently explore the space of design features. By combining a drastic dimension reduction using sensitivity analysis and Bayesian surrogate modeling, we obtain a flexible active sampling flow. On synthetic and real datasets, this flow outperforms the usual Monte-Carlo sampling which often forms the foundation of design space exploration.