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 |
Copyright and reproduction |
All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034) |
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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) |
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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 |
6 |
Date of Issue |
2017-03-02 (CPSY, DC) |
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