Ultra-high-efficient Writing in Voltage-Control Spintronics Memory(VoCSM); The Most Promising Embedded Memory for Deep Learning

Y. Ohsawa; H. Yoda; N. Shimomura; S. Shirotori; S. Fujita; K. Koi; B. Altansargai; S. Oikawa; M. Shimizu; Y. Kato; T. Inokuchi; H. Sugiyama; M. Ishikawa; T. Ajay; K. Ikegami; S. Takaya; A. Kurobe

doi:10.1109/EDTM.2018.8421494

Ultra-high-efficient Writing in Voltage-Control Spintronics Memory(VoCSM); The Most Promising Embedded Memory for Deep Learning

Y. Ohsawa, H. Yoda, N. Shimomura, S. Shirotori, S. Fujita, K. Koi, B. Altansargai, S. Oikawa, M. Shimizu, Y. Kato, T. Inokuchi, H. Sugiyama, M. Ishikawa, T. Ajay, K. Ikegami, S. Takaya, A. Kurobe

Toshiba Corporation

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

3 Citations (Scopus)

Abstract

Our new proposal of voltage-control spintronics memory (VoCSM) in which spin-orbit torque (SOT) in conjunction with the voltage-control-magnetic-anisotropy (VCMA) effect works as the writing principle showed small switching current of 37 μ A for about 350 KBT switching energy. This indicates VoCSM's writing efficiency is so high that VoCSM would be applicable for deep learning (DL) memories requiring ultra-low energy consumption.

Original language	English
Title of host publication	2018 IEEE Electron Devices Technology and Manufacturing Conference, EDTM 2018 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	214-216
Number of pages	3
ISBN (Print)	9781538637111
DOIs	https://doi.org/10.1109/EDTM.2018.8421494
Publication status	Published - 26 Jul 2018
Externally published	Yes
Event	2nd IEEE Electron Devices Technology and Manufacturing Conference, EDTM 2018 - Kobe, Japan Duration: 13 Mar 2018 → 16 Mar 2018

Publication series

Name	2018 IEEE Electron Devices Technology and Manufacturing Conference, EDTM 2018 - Proceedings

Conference

Conference	2nd IEEE Electron Devices Technology and Manufacturing Conference, EDTM 2018
Country/Territory	Japan
City	Kobe
Period	13/03/18 → 16/03/18

Keywords

MRAM
VCMA and SOT
VoCSM

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1109/EDTM.2018.8421494

Cite this

Ohsawa, Y., Yoda, H., Shimomura, N., Shirotori, S., Fujita, S., Koi, K., Altansargai, B., Oikawa, S., Shimizu, M., Kato, Y., Inokuchi, T., Sugiyama, H., Ishikawa, M., Ajay, T., Ikegami, K., Takaya, S., & Kurobe, A. (2018). Ultra-high-efficient Writing in Voltage-Control Spintronics Memory(VoCSM); The Most Promising Embedded Memory for Deep Learning. In 2018 IEEE Electron Devices Technology and Manufacturing Conference, EDTM 2018 - Proceedings (pp. 214-216). Article 8421494 (2018 IEEE Electron Devices Technology and Manufacturing Conference, EDTM 2018 - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/EDTM.2018.8421494

Ohsawa, Y. ; Yoda, H. ; Shimomura, N. et al. / Ultra-high-efficient Writing in Voltage-Control Spintronics Memory(VoCSM); The Most Promising Embedded Memory for Deep Learning. 2018 IEEE Electron Devices Technology and Manufacturing Conference, EDTM 2018 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2018. pp. 214-216 (2018 IEEE Electron Devices Technology and Manufacturing Conference, EDTM 2018 - Proceedings).

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title = "Ultra-high-efficient Writing in Voltage-Control Spintronics Memory(VoCSM); The Most Promising Embedded Memory for Deep Learning",

abstract = "Our new proposal of voltage-control spintronics memory (VoCSM) in which spin-orbit torque (SOT) in conjunction with the voltage-control-magnetic-anisotropy (VCMA) effect works as the writing principle showed small switching current of 37 μ A for about 350 KBT switching energy. This indicates VoCSM's writing efficiency is so high that VoCSM would be applicable for deep learning (DL) memories requiring ultra-low energy consumption.",

keywords = "MRAM, VCMA and SOT, VoCSM",

author = "Y. Ohsawa and H. Yoda and N. Shimomura and S. Shirotori and S. Fujita and K. Koi and B. Altansargai and S. Oikawa and M. Shimizu and Y. Kato and T. Inokuchi and H. Sugiyama and M. Ishikawa and T. Ajay and K. Ikegami and S. Takaya and A. Kurobe",

note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 2nd IEEE Electron Devices Technology and Manufacturing Conference, EDTM 2018 ; Conference date: 13-03-2018 Through 16-03-2018",

year = "2018",

month = jul,

day = "26",

doi = "10.1109/EDTM.2018.8421494",

language = "English",

isbn = "9781538637111",

series = "2018 IEEE Electron Devices Technology and Manufacturing Conference, EDTM 2018 - Proceedings",

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Ohsawa, Y, Yoda, H, Shimomura, N, Shirotori, S, Fujita, S, Koi, K, Altansargai, B, Oikawa, S, Shimizu, M, Kato, Y, Inokuchi, T, Sugiyama, H, Ishikawa, M, Ajay, T, Ikegami, K, Takaya, S & Kurobe, A 2018, Ultra-high-efficient Writing in Voltage-Control Spintronics Memory(VoCSM); The Most Promising Embedded Memory for Deep Learning. in 2018 IEEE Electron Devices Technology and Manufacturing Conference, EDTM 2018 - Proceedings., 8421494, 2018 IEEE Electron Devices Technology and Manufacturing Conference, EDTM 2018 - Proceedings, Institute of Electrical and Electronics Engineers Inc., pp. 214-216, 2nd IEEE Electron Devices Technology and Manufacturing Conference, EDTM 2018, Kobe, Japan, 13/03/18. https://doi.org/10.1109/EDTM.2018.8421494

Ultra-high-efficient Writing in Voltage-Control Spintronics Memory(VoCSM); The Most Promising Embedded Memory for Deep Learning. / Ohsawa, Y.; Yoda, H.; Shimomura, N. et al.
2018 IEEE Electron Devices Technology and Manufacturing Conference, EDTM 2018 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2018. p. 214-216 8421494 (2018 IEEE Electron Devices Technology and Manufacturing Conference, EDTM 2018 - Proceedings).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Ultra-high-efficient Writing in Voltage-Control Spintronics Memory(VoCSM); The Most Promising Embedded Memory for Deep Learning

AU - Ohsawa, Y.

AU - Yoda, H.

AU - Shimomura, N.

AU - Shirotori, S.

AU - Fujita, S.

AU - Koi, K.

AU - Altansargai, B.

AU - Oikawa, S.

AU - Shimizu, M.

AU - Kato, Y.

AU - Inokuchi, T.

AU - Sugiyama, H.

AU - Ishikawa, M.

AU - Ajay, T.

AU - Ikegami, K.

AU - Takaya, S.

AU - Kurobe, A.

PY - 2018/7/26

Y1 - 2018/7/26

N2 - Our new proposal of voltage-control spintronics memory (VoCSM) in which spin-orbit torque (SOT) in conjunction with the voltage-control-magnetic-anisotropy (VCMA) effect works as the writing principle showed small switching current of 37 μ A for about 350 KBT switching energy. This indicates VoCSM's writing efficiency is so high that VoCSM would be applicable for deep learning (DL) memories requiring ultra-low energy consumption.

AB - Our new proposal of voltage-control spintronics memory (VoCSM) in which spin-orbit torque (SOT) in conjunction with the voltage-control-magnetic-anisotropy (VCMA) effect works as the writing principle showed small switching current of 37 μ A for about 350 KBT switching energy. This indicates VoCSM's writing efficiency is so high that VoCSM would be applicable for deep learning (DL) memories requiring ultra-low energy consumption.

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T3 - 2018 IEEE Electron Devices Technology and Manufacturing Conference, EDTM 2018 - Proceedings

SP - 214

EP - 216

BT - 2018 IEEE Electron Devices Technology and Manufacturing Conference, EDTM 2018 - Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2nd IEEE Electron Devices Technology and Manufacturing Conference, EDTM 2018

Y2 - 13 March 2018 through 16 March 2018

ER -

Ohsawa Y, Yoda H, Shimomura N, Shirotori S, Fujita S, Koi K et al. Ultra-high-efficient Writing in Voltage-Control Spintronics Memory(VoCSM); The Most Promising Embedded Memory for Deep Learning. In 2018 IEEE Electron Devices Technology and Manufacturing Conference, EDTM 2018 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2018. p. 214-216. 8421494. (2018 IEEE Electron Devices Technology and Manufacturing Conference, EDTM 2018 - Proceedings). doi: 10.1109/EDTM.2018.8421494

Ultra-high-efficient Writing in Voltage-Control Spintronics Memory(VoCSM); The Most Promising Embedded Memory for Deep Learning

Abstract

Publication series

Conference

Keywords

UN SDGs

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