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Paper Abstract and Keywords
Presentation 2019-03-15 14:25
Estimation of dislocation regions in multicrystalline silicon photoluminescence image by transfer learning with convolutional neural network
Hiroaki Kudo, Tetsuya Matsumoto (Nagoya Univ.), Kentaro Kutsukake (RIKEN), Noritaka Usami (Nagoya Univ.) IMQ2018-69 IE2018-153 MVE2018-100
Abstract (in Japanese) (See Japanese page) 
(in English) In this report, we studied a specified method of regions including dislocations which are crystallographic defects in a photoluminescence (PL) image of multicrystalline silicon wafers. We applied transfer learning of a convolutional neural network to archive it. The network outputs the category which includes dislocation regions or one which does not include them. High accuracies of more than 0.94 are realized. It outperformed methods of multilayer perceptron which has up to 3 hidden layers or of bag of features algorithm as an index by Youden's index. It also obtained better results than the method using non-negative matrix factorization in the condition that the image has larger in depth of learning images in the silicon ingot.
Keyword (in Japanese) (See Japanese page) 
(in English) convolutional neural network / transfer learning / multicrystalline silicon / photoluminescence image / dislocation / / /  
Reference Info. IEICE Tech. Rep., vol. 118, no. 500, IMQ2018-69, pp. 257-262, March 2019.
Paper # IMQ2018-69 
Date of Issue 2019-03-07 (IMQ, IE, MVE) 
ISSN Print edition: ISSN 0913-5685    Online edition: ISSN 2432-6380
Copyright
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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)
Download PDF IMQ2018-69 IE2018-153 MVE2018-100

Conference Information
Committee IMQ IE MVE CQ  
Conference Date 2019-03-14 - 2019-03-15 
Place (in Japanese) (See Japanese page) 
Place (in English) Kagoshima University 
Topics (in Japanese) (See Japanese page) 
Topics (in English) media of five senses, multimedia, media experience, picture codinge, image media quality, network,quality and reliability, etc 
Paper Information
Registration To IMQ 
Conference Code 2019-03-IMQ-IE-MVE-CQ 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Estimation of dislocation regions in multicrystalline silicon photoluminescence image by transfer learning with convolutional neural network 
Sub Title (in English)  
Keyword(1) convolutional neural network  
Keyword(2) transfer learning  
Keyword(3) multicrystalline silicon  
Keyword(4) photoluminescence image  
Keyword(5) dislocation  
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Keyword(8)  
1st Author's Name Hiroaki Kudo  
1st Author's Affiliation Nagoya University (Nagoya Univ.)
2nd Author's Name Tetsuya Matsumoto  
2nd Author's Affiliation Nagoya University (Nagoya Univ.)
3rd Author's Name Kentaro Kutsukake  
3rd Author's Affiliation RIKEN (RIKEN)
4th Author's Name Noritaka Usami  
4th Author's Affiliation Nagoya University (Nagoya Univ.)
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Speaker Author-1 
Date Time 2019-03-15 14:25:00 
Presentation Time 25 minutes 
Registration for IMQ 
Paper # IMQ2018-69, IE2018-153, MVE2018-100 
Volume (vol) vol.118 
Number (no) no.500(IMQ), no.501(IE), no.502(MVE) 
Page pp.257-262 
#Pages
Date of Issue 2019-03-07 (IMQ, IE, MVE) 


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