Fast statistical analysis of rare circuit failure events via scaled-sigma sampling for high-dimensional variation space

Authors:
Shupeng Sun;Xin Li;Hongzhou Liu;Kangsheng Luo;Ben Gu
Affiliations:
Carnegie Mellon University, Pittsburgh, PA;Carnegie Mellon University, Pittsburgh, PA;Cadence Design Systems, Pittsburgh, PA;Cadence Design Systems, Pittsburgh, PA;Cadence Design Systems, Austin, TX
Venue:
Proceedings of the International Conference on Computer-Aided Design
Year:
2013

Citing 13
Cited 0

Convex Optimization

Convex Optimization
Variability in sub-100nm SRAM designs

Proceedings of the 2004 IEEE/ACM International conference on Computer-aided design
Mixture importance sampling and its application to the analysis of SRAM designs in the presence of rare failure events

Proceedings of the 43rd annual Design Automation Conference
An efficient, fully nonlinear, variability-aware non-Monte-Carlo yield estimation procedure with applications to SRAM cells and ring oscillators

Proceedings of the 2008 Asia and South Pacific Design Automation Conference
A methodology for statistical estimation of read access yield in SRAMs

Proceedings of the 45th annual Design Automation Conference
Breaking the simulation barrier: SRAM evaluation through norm minimization

Proceedings of the 2008 IEEE/ACM International Conference on Computer-Aided Design
SRAM parametric failure analysis

Proceedings of the 46th Annual Design Automation Conference
Statistical blockade: very fast statistical simulation and modeling of rare circuit events and its application to memory design

IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
A statistical simulation method for reliability analysis of SRAM core-cells

Proceedings of the 47th Design Automation Conference
CMOS VLSI Design: A Circuits and Systems Perspective

CMOS VLSI Design: A Circuits and Systems Perspective
Loop flattening & spherical sampling: highly efficient model reduction techniques for SRAM yield analysis

Proceedings of the Conference on Design, Automation and Test in Europe
Sequential importance sampling for low-probability and high-dimensional SRAM yield analysis

Proceedings of the International Conference on Computer-Aided Design
Yield estimation via multi-cones

Proceedings of the 49th Annual Design Automation Conference

Quantified Score

Hi-index	0.00

Visualization

Abstract

Accurately estimating the rare failure rates for nanoscale circuit blocks (e.g., SRAM, DFF, etc.) is a challenging task, especially when the variation space is high-dimensional. In this paper, we propose a novel scaled-sigma sampling (SSS) method to address this technical challenge. The key idea of SSS is to generate random samples from a distorted distribution for which the standard deviation (i.e., sigma) is scaled up. Next, the failure rate is accurately estimated from these scaled random samples by using an analytical model derived from the theorem of "soft maximum". Several circuit examples designed in nanoscale technologies demonstrate that the proposed SSS method achieves superior accuracy over the traditional importance sampling technique when the dimensionality of the variation space is more than a few hundred.