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Unknown-tolerance analysis and test-quality control for test response compaction using space compactors
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Source Annual ACM IEEE Design Automation Conference archive
Proceedings of the 43rd annual conference on Design automation table of contents
San Francisco, CA, USA
SESSION: Session 61: test response compaction and ATPG table of contents
Pages: 1083 - 1088  
Year of Publication: 2006
ISBN:1-59593-381-6
Authors
Mango C.-T. Chao  UC Santa Barbara, Santa Barbara, CA
Kwang-Ting Cheng  UC Santa Barbara, Santa Barbara, CA
Seongmoon Wang  NEC Labs. America, Princeton, NJ
Srimat Chakradhar  NEC Labs. America, Princeton, NJ
Wen-Long Wei  NEC Labs. America, Princeton, NJ
Sponsors
SIGDA: ACM Special Interest Group on Design Automation
ACM: Association for Computing Machinery
Publisher
ACM  New York, NY, USA
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ABSTRACT

For a space compactor, degradation of fault detection capability caused by the masking effects from unknown values is much more serious than that caused by error masking (i.e. aliasing). In this paper, we first propose a mathematical framework to estimate the percentage of observable responses under unknown-induced masking for a space compactor. We further develop a prediction scheme which can correlate the percentage of observable responses with the modeled-fault coverage and with a n-detection metric for a given test set. As a result, the quality of a space compactor can be measured directly based on its test quality, instead of based on indirect metrics such as the number of tolerated unknowns or the aliasing probability. With the prediction scheme above, we propose a construction flow for space compactors to achieve the desired level of test quality while maximizing the compaction ratio.


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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E. H. Volkerink, and S. Mitra, Response Compaction with any Number of Unknowns Using a New LFSR Architecture, ACM/IEEE Design Automation Conference, pp.1031--1040, 2003.


Collaborative Colleagues:
Mango C.-T. Chao: colleagues
Kwang-Ting Cheng: colleagues
Seongmoon Wang: colleagues
Srimat Chakradhar: colleagues
Wen-Long Wei: colleagues