Worst Case Sampling Method with Confidence Ellipse for Estimating the Impact of Random Variation on Static Random Access Memory (SRAM)

As semiconductor devices are being scaled down, random variation becomes a critical issue, especially in the case of static random access memory (SRAM). Thus, there is an urgent need for statistical methodologies to analyze the impact of random variations on the SRAM. In this paper, we propose a nov...

Full description

Saved in:
Bibliographic Details
Published in:Journal of semiconductor technology and science 2015, Vol.15 (3), p.374-380
Main Authors: Oh, Sangheon, Jo, Jaesung, Lee, Hyunjae, Lee, Gyo Sub, Park, Jung-Dong, Shin, Changhwan
Format: Article
Language:Korean
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:As semiconductor devices are being scaled down, random variation becomes a critical issue, especially in the case of static random access memory (SRAM). Thus, there is an urgent need for statistical methodologies to analyze the impact of random variations on the SRAM. In this paper, we propose a novel sampling method based on the concept of a confidence ellipse. Results show that the proposed method estimates the SRAM margin metrics in high-sigma regimes more efficiently than the standard Monte Carlo (MC) method.
ISSN:1598-1657