By Topic

Sequential importance sampling for low-probability and high-dimensional SRAM yield analysis

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

5 Author(s)
Kentaro Katayama ; Department of Communications and Computer Engineering, Kyoto University, Yoshida-hon-machi, Sakyo, Kyoto, 606-8501 Japan ; Shiho Hagiwara ; Hiroshi Tsutsui ; Hiroyuki Ochi
more authors

In this paper, a significant acceleration of estimating low-failure rate in a high-dimensional SRAM yield analysis is achieved using sequential importance sampling. The proposed method systematically, autonomously, and adaptively explores failure region of interest, whereas all previous works needed to resort to brute-force search. Elimination of brute-force search and adaptive trial distribution significantly improves the efficiency of failure-rate estimation of hitherto unsolved high-dimensional cases wherein a lot of variation sources including threshold voltages, channel-length, carrier mobility, etc. are simultaneously considered. The proposed method is applicable to wide range of Monte Carlo simulation analyses dealing with high-dimensional problem of rare events. In SRAM yield estimation example, we achieved 106 times acceleration compared to a standard Monte Carlo simulation for a failure probability of 3 × 10-9 in a six-dimensional problem. The example of 24-dimensional analysis on which other methods are ineffective is also presented.

Published in:

2010 IEEE/ACM International Conference on Computer-Aided Design (ICCAD)

Date of Conference:

7-11 Nov. 2010