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Paper Abstract and Keywords
Presentation 2017-03-10 09:30
Pass/Fail Prediction in LSI Test Considering Fail Die Characteristics.
Takazumi Sato, Michiko Inoue (NAIST) CPSY2016-144 DC2016-90
Abstract (in Japanese) (See Japanese page) 
(in English) Various kinds of tests are applied to LSIs in several satages to ship only fully reliable products.However, a lot of kinds of tests for a lot of products is costly. Test costs is said to be approaching a half of manufacturing cost. Therefore, research on Pass / Fail prediction of final test results by data mining has been conducted. This method reduces test cost by omitting some test processes for a part of products.
In this research, we focus on the fail die characteristics and effectively predict Pass/Fail. Specifically, we utilize extraction and clustering methods for fail dies those are sensitive to be predicted for a part of products as fail. We show the AUC is improved by at most 0.05 compared to when not considering the fail die characteristics.
Keyword (in Japanese) (See Japanese page) 
(in English) data mining / burn-in test / LSI test / fail die characteristics / support vector machine / / /  
Reference Info. IEICE Tech. Rep., vol. 116, no. 511, DC2016-90, pp. 291-296, March 2017.
Paper # DC2016-90 
Date of Issue 2017-03-02 (CPSY, DC) 
ISSN Print edition: ISSN 0913-5685    Online edition: ISSN 2432-6380
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Download PDF CPSY2016-144 DC2016-90

Conference Information
Committee CPSY DC IPSJ-SLDM IPSJ-EMB IPSJ-ARC  
Conference Date 2017-03-09 - 2017-03-10 
Place (in Japanese) (See Japanese page) 
Place (in English) Kumejima Island 
Topics (in Japanese) (See Japanese page) 
Topics (in English) ETNET20167 
Paper Information
Registration To DC 
Conference Code 2017-03-CPSY-DC-SLDM-EMB-ARC 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Pass/Fail Prediction in LSI Test Considering Fail Die Characteristics. 
Sub Title (in English)  
Keyword(1) data mining  
Keyword(2) burn-in test  
Keyword(3) LSI test  
Keyword(4) fail die characteristics  
Keyword(5) support vector machine  
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1st Author's Name Takazumi Sato  
1st Author's Affiliation Nara Institute of Science and Technology (NAIST)
2nd Author's Name Michiko Inoue  
2nd Author's Affiliation Nara Institute of Science and Technology (NAIST)
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Speaker Author-1 
Date Time 2017-03-10 09:30:00 
Presentation Time 20 minutes 
Registration for DC 
Paper # CPSY2016-144, DC2016-90 
Volume (vol) vol.116 
Number (no) no.510(CPSY), no.511(DC) 
Page pp.291-296 
#Pages
Date of Issue 2017-03-02 (CPSY, DC) 


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