High-Dimensional and Multiple-Failure-Region Importance Sampling for SRAM Yield Analysis

Journal Article (Journal Article)

The failure rate of static RAM (SRAM) cells is restricted to be extremely low to ensure sufficient high yield for the entire chip. In addition, multiple performances of interest and influences from peripherals make SRAM failure rate estimation a high-dimensional multiple-failure-region problem. This paper proposes a new method featuring a multistart-point sequential quadratic programming (SQP) framework to extend minimized norm importance sampling (IS) to address this problem. Failure regions in the variation space are first found by the low-discrepancy sampling sequence. Afterward, start points are generated in all identified failure regions and local optimizations based on SQP are invoked from these start points searching for the optimal shift vectors (OSVs). Based on the OSVs, a Gaussian mixture distorted distribution is constructed for IS. To further reduce the computational cost of IS while fully considering the influence of increasing dimensionality, an adaptive model training framework is proposed to keep high efficiency for both low- A nd high-dimensional problems. The experimental results show that the proposed method can not only approximate failure rate with high accuracy and efficiency in low-dimensional cases but also keep these features in high-dimensional ones.

Full Text

Duke Authors

Cited Authors

  • Wang, M; Yan, C; Li, X; Zhou, D; Zeng, X

Published Date

  • March 1, 2017

Published In

Volume / Issue

  • 25 / 3

Start / End Page

  • 806 - 819

International Standard Serial Number (ISSN)

  • 1063-8210

Digital Object Identifier (DOI)

  • 10.1109/TVLSI.2016.2601606

Citation Source

  • Scopus