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Paper Abstract and Keywords
Presentation 2024-02-28 13:40
Test Point Selection Method for Multi-Cycle BIST Using Deep Reinforcement Learning
Kohei Shiotani, Tatsuya Nishikawa, Shaoqi Wei, Senling Wang, Hiroshi Kai, Yoshinobu Higami, Hiroshi Takahashi (Ehime Univ.) DC2023-98
Abstract (in Japanese) (See Japanese page) 
(in English) Multi-cycle BIST is a test method that performs multiple captures for each scan pattern, proving effective in reducing test patterns in in-system testing. However, multi-cycle capture can lead to a decrease in the testability (controllability and observability) of the temporally unfolded logic circuit, potentially causing a reduction in fault detection capability and, consequently, hindering the reduction of test patterns. This study aims to improve the testability of multi-cycle BIST by proposing a control point selection method that combines spatio-temporal graph neural networks with deep reinforcement learning. The proposed method selects optimal control points by considering the testability and logical structure of the signal lines in the temporally unfolded logic circuit in terms of their spatio-temporal relationships. Specifically, it uses spatio-temporal graph neural networks to model the testability of signal lines from the structural and temporal characteristics of the logic circuit and employs deep reinforcement learning to efficiently search for control point positions that maximize the testability of the temporally unfolded circuit. Evaluation results on benchmark circuits have confirmed the effectiveness of the proposed method.
Keyword (in Japanese) (See Japanese page) 
(in English) Multi-cycle Test / Time-Expansion Circuit / Time-Series Variables / Graph Convolutional Neural Networks / Test Point insertion / / /  
Reference Info. IEICE Tech. Rep., vol. 123, no. 389, DC2023-98, pp. 23-28, Feb. 2024.
Paper # DC2023-98 
Date of Issue 2024-02-21 (DC) 
ISSN 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)
Download PDF DC2023-98

Conference Information
Committee DC  
Conference Date 2024-02-28 - 2024-02-28 
Place (in Japanese) (See Japanese page) 
Place (in English) Kikai-Shinko-Kaikan Bldg. 
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To DC 
Conference Code 2024-02-DC 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Test Point Selection Method for Multi-Cycle BIST Using Deep Reinforcement Learning 
Sub Title (in English)  
Keyword(1) Multi-cycle Test  
Keyword(2) Time-Expansion Circuit  
Keyword(3) Time-Series Variables  
Keyword(4) Graph Convolutional Neural Networks  
Keyword(5) Test Point insertion  
Keyword(6)  
Keyword(7)  
Keyword(8)  
1st Author's Name Kohei Shiotani  
1st Author's Affiliation Ehime University (Ehime Univ.)
2nd Author's Name Tatsuya Nishikawa  
2nd Author's Affiliation Ehime University (Ehime Univ.)
3rd Author's Name Shaoqi Wei  
3rd Author's Affiliation Ehime University (Ehime Univ.)
4th Author's Name Senling Wang  
4th Author's Affiliation Ehime University (Ehime Univ.)
5th Author's Name Hiroshi Kai  
5th Author's Affiliation Ehime University (Ehime Univ.)
6th Author's Name Yoshinobu Higami  
6th Author's Affiliation Ehime University (Ehime Univ.)
7th Author's Name Hiroshi Takahashi  
7th Author's Affiliation Ehime University (Ehime Univ.)
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Speaker Author-1 
Date Time 2024-02-28 13:40:00 
Presentation Time 25 minutes 
Registration for DC 
Paper # DC2023-98 
Volume (vol) vol.123 
Number (no) no.389 
Page pp.23-28 
#Pages 6 
Date of Issue 2024-02-21 (DC) 

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