NDLI: An elegant hardware-corroborated statistical repair and test methodology for conquering aging effects

Content Provider	IEEE Xplore Digital Library
Author	Kanj, R. Joshi, R. Adams, C. Warnock, J. Nassif, S.
Copyright Year	2009
Description	Author affiliation: IBM Austin Res. Labs., Austin, TX, USA (Kanj, R.; Nassif, S.) \|\| IBM TJ Watson Labs., Yorktown Heights, NY, USA (Joshi, R.; Warnock, J.) \|\| IBM Syst. & Technol. Group, Rochester, MN, USA (Adams, C.)
Abstract	We propose a new and efficient statistical-simulation-based test methodology for optimally selecting repair elements at beginning-of-life (BOL) to improve the end-of-life (EOL) functionality of memory designs. This is achieved by identifying the best BOL test/repair corner that maximizes EOL yield, thereby exploiting redundancy to optimize EOL operability with minimal BOL yield loss. The statistical approach makes it possible to identify such corners with tremendous savings in terms of test time and hardware. To estimate yields and search for the best repair corner the approach relies on fast conditional importance sampling statistical simulations. The methodology is versatile and can handle complex aging effects with asymmetrical distributions. Results are demonstrated on state-of-the-art dual-supply memory designs subject to statistical negative bias temperature instability (NBTI) effects, and hardware results are shown to match predicted model trends.
Starting Page	497
Ending Page	504
File Size	517707
Page Count	8
File Format	PDF
ISBN	9781605588001
ISSN	10923152
DOI	10.1145/1687399.1687494
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-11-02
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Association for Computing Machinery, Inc. (ACM)
Subject Keyword	Testing Aging Redundancy Niobium compounds Titanium compounds Hardware Negative bias temperature instability Monte Carlo methods Random access memory Permission NBTI Memory repair and test Statistical Performance Yield Prediction SRAM
Content Type	Text
Resource Type	Article
Subject	Computer Graphics and Computer-Aided Design Computer Science Applications Software

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