High-Dimensional Yield Estimation using Shrinkage Deep Features and Maximization of Integral Entropy Reduction

TL;DR

This paper introduces ASDK, a novel shrinkage deep kernel learning method that effectively addresses the curse of dimensionality in high-sigma yield analysis, significantly improving accuracy and efficiency in circuit simulations.

Contribution

The paper proposes ASDK, an innovative nonlinear-correlated deep kernel model with entropy-based adaptive updates, advancing high-dimensional yield estimation techniques.

Findings

ASDK achieves up to 10.3x speedup over state-of-the-art methods.

ASDK outperforms existing approaches in accuracy and efficiency.

Parallel batch sampling enhances practical deployment.

Abstract

Despite the fast advances in high-sigma yield analysis with the help of machine learning techniques in the past decade, one of the main challenges, the curse of dimensionality, which is inevitable when dealing with modern large-scale circuits, remains unsolved. To resolve this challenge, we propose an absolute shrinkage deep kernel learning, ASDK, which automatically identifies the dominant process variation parameters in a nonlinear-correlated deep kernel and acts as a surrogate model to emulate the expensive SPICE simulation. To further improve the yield estimation efficiency, we propose a novel maximization of approximated entropy reduction for an efficient model update, which is also enhanced with parallel batch sampling for parallel computing, making it ready for practical deployment. Experiments on SRAM column circuits demonstrate the superiority of ASDK over the state-of-the-art (SOTA) approaches in terms of accuracy and efficiency with up to 10.3x speedup over SOTA methods.