Fast Statistical Analysis of Rare Failure Events with Truncated Normal Distribution in High-Dimensional Variation Space

Journal Article (Journal Article)

In this article, to accurately estimate the rare failure rates for large-scale circuits (e.g., SRAM) where process variations are modeled as truncated normal distributions in high-dimensional space, we propose a novel truncated scaled-sigma sampling (T-SSS) method. Similar to scaled-sigma sampling (SSS), T-SSS distorts the truncated normal distributions by a scaling factor, resulting in an analytical model for failure rate estimation. By drawing random samples from the distorted distribution and estimating a sequence of scaled failure rates, we can solve all unknown model coefficients and predict the original failure rate by extrapolation. The accuracy of T-SSS is further assessed by estimating its confidence interval (CI) based on resampling. Our numerical results demonstrate that the proposed T-SSS method can achieve superior accuracy over the state-of-the-art method without increasing the computational cost.

Full Text

Duke Authors

Cited Authors

  • Gao, Z; Tao, J; Su, Y; Zhou, D; Zeng, X; Li, X

Published Date

  • March 1, 2022

Published In

Volume / Issue

  • 41 / 3

Start / End Page

  • 789 - 793

Electronic International Standard Serial Number (EISSN)

  • 1937-4151

International Standard Serial Number (ISSN)

  • 0278-0070

Digital Object Identifier (DOI)

  • 10.1109/TCAD.2021.3068107

Citation Source

  • Scopus