Paper Abstract and Keywords |
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 |
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 |
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) |
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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) |
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Keyword(1) |
ReRAM |
Keyword(2) |
reliability |
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machine learning |
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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 |
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Chuo University (Chuo Univ.) |
5th Author's Name |
Shuhei Tanakamaru |
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Chuo University (Chuo Univ.) |
6th Author's Name |
Ken Takeuchi |
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Chuo University (Chuo Univ.) |
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Speaker |
Author-1 |
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 |
6 |
Date of Issue |
2016-04-07 (ICD) |
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