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Interactive presentation: Improving the fault tolerance of nanometric PLA designs
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Source Design, Automation, and Test in Europe archive
Proceedings of the conference on Design, automation and test in Europe table of contents
Nice, France
SESSION: Future design challenges table of contents
Pages: 570 - 575  
Year of Publication: 2007
ISBN:978-3-9810801-2-4
Authors
Federico Angiolini  University of Bologna, Bologna, Italy
M. Haykel Ben Jamaa  LSI, EPFL, Lausanne, Switzerland
David Atienza  LSI, EPFL, Lausanne, Switzerland and Complutense University, Madrid, Spain
Luca Benini  University of Bologna, Bologna, Italy
Giovanni De Micheli  LSI, EPFL, Lausanne, Switzerland
Sponsors
: IEEE Council on Electronic Design Automation (CEDA)
SIGDA: ACM Special Interest Group on Design Automation
: The EDA Consortium
EDAA : European Design and Automation Association
RAS : RAS
: The IEEE Computer Society TTTC
: ECSI
Publisher
EDA Consortium  San Jose, CA, USA
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Downloads (6 Weeks): 4,   Downloads (12 Months): 29,   Citation Count: 1
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ABSTRACT

Several alternative building blocks have been proposed to replace planar transistors, among which a prominent spot belongs to nanometric filaments such as Silicon Nano Wires (SiNWs) and Carbon Nano Tubes (CNTs). However, chips leveraging these nanoscale structures are expected to be affected by a large amount of manufacturing faults, way beyond what chip architects have learned to counter. In this paper, we show a design flow, based on software mapping algorithms, to improve the yield of nanometric Programmable Logic Arrays (PLAs). While further improvements to the manufacturing technology will be needed to make these devices fully usable, our flow can significantly shrink the gap between current and desired yield levels. Also, our approach does not need post-fabrication functional analysis and mapping, therefore dramatically cutting on verification costs. We check PLA yields by means of an accurate analyzer after Monte Carlo fault injection. We show that, compared to a baseline policy of wire replication, we achieve equal or better yields (8% over a set of designs) depending on the underlying defect assumptions.


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:
Federico Angiolini: colleagues
M. Haykel Ben Jamaa: colleagues
David Atienza: colleagues
Luca Benini: colleagues
Giovanni De Micheli: colleagues