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Presentation 2016-04-14 14:15
[Invited Lecture] ReRAM reliability characterization and improvement by machine learning
Tomoko Ogura Iwasaki, Sheyang Ning, Hiroki Yamazawa, Chao Sun, Shuhei Tanakamaru, Ken Takeuchi (Chuo Univ.) ICD2016-8 Link to ES Tech. Rep. Archives: ICD2016-8
Abstract (in Japanese) (See Japanese page) 
(in English) The low voltage and fast program capability of ReRAM is very attractive for next-generation memory applications, but there also exist new reliability issues associated with the resistance switching mechanism. In this work, the variable behavior of ReRAM memory cells is studied using machine learning (ML) techniques. Prediction of two types of cell behavior are also evaluated, (i) how quickly will the cell reset in the next cycle, and (ii) how the cell will fail after endurance cycling. Furthermore, a new scheme, called Proactive Bit Redundancy, is included into the SSD controller. Here, a ML trained model predicts fail cells and replaces them by dynamic redundancy. In order to eliminate the need for extra address tables to store the failed cell locations, an Invalid Masking technique is also proposed. Based on measured results from a 50nm AlxOy ReRAM testchip, a bit error rate (BER) reduction of 2.85?~, also equivalent to an endurance improvement of 13?~, is obtained with little circuitry overhead.
Keyword (in Japanese) (See Japanese page) 
(in English) ReRAM / reliability / machine learning / proactive redundancy / / / /  
Reference Info. IEICE Tech. Rep., vol. 116, no. 3, ICD2016-8, pp. 39-44, April 2016.
Paper # ICD2016-8 
Date of Issue 2016-04-07 (ICD) 
ISSN Print edition: ISSN 0913-5685    Online edition: ISSN 2432-6380
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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 ICD2016-8 Link to ES Tech. Rep. Archives: ICD2016-8

Conference Information
Committee ICD  
Conference Date 2016-04-14 - 2016-04-15 
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 ICD 
Conference Code 2016-04-ICD 
Language English 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) ReRAM reliability characterization and improvement by machine learning 
Sub Title (in English)  
Keyword(1) ReRAM  
Keyword(2) reliability  
Keyword(3) machine learning  
Keyword(4) proactive redundancy  
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1st Author's Name Tomoko Ogura Iwasaki  
1st Author's Affiliation Chuo University (Chuo Univ.)
2nd Author's Name Sheyang Ning  
2nd Author's Affiliation Chuo University (Chuo Univ.)
3rd Author's Name Hiroki Yamazawa  
3rd Author's Affiliation Chuo University (Chuo Univ.)
4th Author's Name Chao Sun  
4th Author's Affiliation Chuo University (Chuo Univ.)
5th Author's Name Shuhei Tanakamaru  
5th Author's Affiliation Chuo University (Chuo Univ.)
6th Author's Name Ken Takeuchi  
6th Author's Affiliation Chuo University (Chuo Univ.)
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Date Time 2016-04-14 14:15:00 
Presentation Time 25 minutes 
Registration for ICD 
Paper # ICD2016-8 
Volume (vol) vol.116 
Number (no) no.3 
Page pp.39-44 
#Pages
Date of Issue 2016-04-07 (ICD) 


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