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Defect tolerant probabilistic design paradigm for nanotechnologies
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Source Annual ACM IEEE Design Automation Conference archive
Proceedings of the 41st annual Design Automation Conference table of contents
San Diego, CA, USA
SESSION: New technologies in system design table of contents
Pages: 596 - 601  
Year of Publication: 2004
ISBN:1-58113-828-8
Authors
Margarida Jacome  The University of Texas at Austin, Austin, TX
Chen He  The University of Texas at Austin, Austin, TX
Gustavo de Veciana  The University of Texas at Austin, Austin, TX
Stephen Bijansky  The University of Texas at Austin, Austin, TX
Sponsors
ACM: Association for Computing Machinery
SIGDA: ACM Special Interest Group on Design Automation
Publisher
ACM  New York, NY, USA
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ABSTRACT

Recent successes in the development and self-assembly of nanoelectronic devices suggest that the ability to manufacture dense nanofabrics is on the near horizon. However, the tremendous increase in device density of nanoelectronics will be accompanied by a substantial increase in hard and soft faults, posing a major challenge to current design methodologies and tools. In this paper we propose a novel probabilistic design paradigm for defective but reconfigurable nanofabrics. The new design goal is to devise an appropriate structural/behavioral decomposition which improves scalability by constraining the reconfiguration process, while meeting a desired probability of successful instantiation, i.e, yield. Our approach not only addresses the scalability problem in configuring dense nanofabrics subject to defects, but gives a rich framework in which critical trade-offs among performance, yield, and per chip cost can be explored. We present a concrete instance of the approach and show extensive experimental results supporting these claims.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
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Collaborative Colleagues:
Margarida Jacome: colleagues
Chen He: colleagues
Gustavo de Veciana: colleagues
Stephen Bijansky: colleagues