Paper Info
Title
Gradient importance sampling: An efficient statistical extraction methodology of high-sigma SRAM dynamic characteristics
Abstract
The impact of within-die transistor variability has increased with CMOS technology scaling up to the point where it has emerged as a systematic problem for the designer. Estimating extremely low failure rate, i.e. “high-sigma” probabilities, by the conventional Monte Carlo (MC) approach requires millions of simulation runs, making it an impractical approach for circuit designers. To overcome this problem, alternative failure estimation methodologies, which require a smaller number of runs have been proposed. In this paper, we propose a novel methodology called “gradient importance sampling” (GIS) for fast statistical extraction of high-sigma circuit characteristics. It is based on conventional Importance Sampling combined with a gradient-based approach to find the most probable failure point (MPFP). By applying GIS to extract SRAM dynamic characteristics in 28nm FDSOI CMOS, we show that the proposed methodology is straightfor-ward, computationally efficient and the results are in line with those obtained via standard MC. To the best of our knowledge, the GIS results are the best in their class for low failure rate estimation.
Year
DOI
Venue
2018
10.23919/DATE.2018.8342002
2018 Design, Automation & Test in Europe Conference & Exhibition (DATE)
Keywords
Field
DocType
Gradient,Importance sampling,Monte Carlo,Low failure rate probability,Variability,High-sigma
Monte Carlo method,Importance sampling,Cmos technology scaling,Computer science,Parallel computing,Failure rate,CMOS,Static random-access memory,Sigma,Transistor,Computer engineering
Conference
ISSN
Citations 
PageRank 
1530-1591
0
0.34
References 
Authors
0
4
Name
Order
Citations
PageRank
Thomas Haine101.35
Johan Segers24110.37
Denis Flandre331670.47
David Bol416227.67