Mixture importance sampling and its application to the analysis of SRAM designs in the presence of rare failure events

Author

Kanj, Rouwaida ; Joshi, Rajiv ; Nassif, Sani

Author_Institution

IBM Austin Res. Labs, TX

fYear

0

fDate

0-0 0

Firstpage

69

Lastpage

72

Abstract

In this paper, we propose a novel methodology for statistical SRAM design and analysis. It relies on an efficient form of importance sampling, mixture importance sampling. The method is comprehensive, computationally efficient and the results are in excellent agreement with those obtained via standard Monte Carlo techniques. All this comes at significant gains in speed and accuracy, with speedup of more than 100times compared to regular Monte Carlo. To the best of our knowledge, this is the first time such a methodology is applied to the analysis of SRAM designs

Keywords

Monte Carlo methods; SRAM chips; failure analysis; integrated circuit design; logic design; statistical analysis; Monte Carlo techniques; failure events; mixture importance sampling; statistical SRAM design; Algorithm design and analysis; Design methodology; Failure analysis; Integrated circuit yield; Monte Carlo methods; Performance analysis; Random access memory; Sampling methods; Tail; Yield estimation; Algorithms; Design; Performance; Reliability; SRAM; Statistical Performance Analysis; Yield Prediction;

fLanguage

English

Publisher

ieee

Conference_Titel

Design Automation Conference, 2006 43rd ACM/IEEE

Conference_Location

San Francisco, CA

ISSN

0738-100X

Print_ISBN

1-59593-381-6

Type

conf

DOI

10.1109/DAC.2006.229167

Filename

1688762