A methodology to improve linearity of analog RRAM for neuromorphic computing

Wei Wu; Huaqiang Wu; Bin Gao; Peng Yao; Xiang Zhang; Xiaochen Peng; Shimeng Yu; He Qian

doi:10.1109/VLSIT.2018.8510690

A methodology to improve linearity of analog RRAM for neuromorphic computing

Wei Wu, Huaqiang Wu, Bin Gao, Peng Yao, Xiang Zhang, Xiaochen Peng, Shimeng Yu, He Qian

Arizona State University

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

160 Scopus citations

Abstract

The conductance tuning linearity is an important parameter of analog RRAM for neuromorphic computing. This work presents a novel methodology to improve the conductance tuning linearity of the filamentary RRAM. An electro-thermal modulation layer is designed and introduced to control the distribution of electric field and temperature in the filament region. For the first time, a HfO_x based RRAM is demonstrated with linear analog SET, linear analog RESET, 50ns speed, 10× analog tuning window, 100kω on-state resistance, and high temperature retention for multilevel states. The excellent performances of the analog RRAM devices enable high accuracy online learning in a neural network.

Original language	English (US)
Title of host publication	2018 IEEE Symposium on VLSI Technology, VLSI Technology 2018
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	103-104
Number of pages	2
ISBN (Electronic)	9781538642160
DOIs	https://doi.org/10.1109/VLSIT.2018.8510690
State	Published - Oct 25 2018
Event	38th IEEE Symposium on VLSI Technology, VLSI Technology 2018 - Honolulu, United States Duration: Jun 18 2018 → Jun 22 2018

Publication series

Name	Digest of Technical Papers - Symposium on VLSI Technology
Volume	2018-June
ISSN (Print)	0743-1562

Other

Other	38th IEEE Symposium on VLSI Technology, VLSI Technology 2018
Country/Territory	United States
City	Honolulu
Period	6/18/18 → 6/22/18

Keywords

Analog RRAM
Online learning
Synapse

ASJC Scopus subject areas

Electrical and Electronic Engineering

Access to Document

10.1109/VLSIT.2018.8510690

Cite this

Wu, W., Wu, H., Gao, B., Yao, P., Zhang, X., Peng, X., Yu, S., & Qian, H. (2018). A methodology to improve linearity of analog RRAM for neuromorphic computing. In 2018 IEEE Symposium on VLSI Technology, VLSI Technology 2018 (pp. 103-104). Article 8510690 (Digest of Technical Papers - Symposium on VLSI Technology; Vol. 2018-June). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/VLSIT.2018.8510690

A methodology to improve linearity of analog RRAM for neuromorphic computing. / Wu, Wei; Wu, Huaqiang; Gao, Bin et al.
2018 IEEE Symposium on VLSI Technology, VLSI Technology 2018. Institute of Electrical and Electronics Engineers Inc., 2018. p. 103-104 8510690 (Digest of Technical Papers - Symposium on VLSI Technology; Vol. 2018-June).

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

Wu, W, Wu, H, Gao, B, Yao, P, Zhang, X, Peng, X, Yu, S & Qian, H 2018, A methodology to improve linearity of analog RRAM for neuromorphic computing. in 2018 IEEE Symposium on VLSI Technology, VLSI Technology 2018., 8510690, Digest of Technical Papers - Symposium on VLSI Technology, vol. 2018-June, Institute of Electrical and Electronics Engineers Inc., pp. 103-104, 38th IEEE Symposium on VLSI Technology, VLSI Technology 2018, Honolulu, United States, 6/18/18. https://doi.org/10.1109/VLSIT.2018.8510690

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abstract = "The conductance tuning linearity is an important parameter of analog RRAM for neuromorphic computing. This work presents a novel methodology to improve the conductance tuning linearity of the filamentary RRAM. An electro-thermal modulation layer is designed and introduced to control the distribution of electric field and temperature in the filament region. For the first time, a HfOx based RRAM is demonstrated with linear analog SET, linear analog RESET, 50ns speed, 10× analog tuning window, 100kω on-state resistance, and high temperature retention for multilevel states. The excellent performances of the analog RRAM devices enable high accuracy online learning in a neural network.",

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AB - The conductance tuning linearity is an important parameter of analog RRAM for neuromorphic computing. This work presents a novel methodology to improve the conductance tuning linearity of the filamentary RRAM. An electro-thermal modulation layer is designed and introduced to control the distribution of electric field and temperature in the filament region. For the first time, a HfOx based RRAM is demonstrated with linear analog SET, linear analog RESET, 50ns speed, 10× analog tuning window, 100kω on-state resistance, and high temperature retention for multilevel states. The excellent performances of the analog RRAM devices enable high accuracy online learning in a neural network.

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A methodology to improve linearity of analog RRAM for neuromorphic computing

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