Sub-nA Low-Current HZO Ferroelectric Tunnel Junction for High-Performance and Accurate Deep Learning Acceleration

Tzu Yun Wu; Tian-Sheuan Chang; Heng Yuan Lee; Shyh Shyuan Sheu; Wei Chung Lo; Tuo-Hung Hou; Hsin Hui Huang; Yueh Hua Chu; Chih Cheng Chang; Ming Hung Wu; Chien Hua Hsu; Chien Ting Wu; Min Ci Wu; Wen-Wei Wu

doi:10.1109/IEDM19573.2019.8993565

Sub-nA Low-Current HZO Ferroelectric Tunnel Junction for High-Performance and Accurate Deep Learning Acceleration

Tzu Yun Wu, Tian-Sheuan Chang, Heng Yuan Lee, Shyh Shyuan Sheu, Wei Chung Lo, Tuo-Hung Hou^*, Hsin Hui Huang, Yueh Hua Chu, Chih Cheng Chang, Ming Hung Wu, Chien Hua Hsu, Chien Ting Wu, Min Ci Wu, Wen-Wei Wu

^*Corresponding author for this work

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

37 Scopus citations

Abstract

This paper presents a unique opportunity of HZO ferroelectric tunnel junction (FTJ) for in-memory computing. The device operates at an extremely low sub-nA current while simultaneously achieving 50-ns fast switching, > 10⁷ cycling endurance, > 10-yr retention, minimal variability, and analog state modulation. We analyze an FTJ-based deep binary neural network. It achieves better accuracy and remarkable 702, 101, and 7×10⁴ times improvements in power, area, and energy-area product efficiency compared with those using NVMs with a typical μA cell current designed for fast memory access.

Original language	English
Title of host publication	2019 IEEE International Electron Devices Meeting, IEDM 2019
Publisher	Institute of Electrical and Electronics Engineers Inc.
Number of pages	4
ISBN (Electronic)	9781728140315
DOIs	https://doi.org/10.1109/IEDM19573.2019.8993565
State	Published - Dec 2019
Event	65th Annual IEEE International Electron Devices Meeting, IEDM 2019 - San Francisco, United States Duration: 7 Dec 2019 → 11 Dec 2019

Publication series

Name	Technical Digest - International Electron Devices Meeting, IEDM
Volume	2019-December
ISSN (Print)	0163-1918

Conference

Conference	65th Annual IEEE International Electron Devices Meeting, IEDM 2019
Country/Territory	United States
City	San Francisco
Period	7/12/19 → 11/12/19

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10.1109/IEDM19573.2019.8993565

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Wu, T. Y., Chang, T.-S., Lee, H. Y., Sheu, S. S., Lo, W. C., Hou, T.-H., Huang, H. H., Chu, Y. H., Chang, C. C., Wu, M. H., Hsu, C. H., Wu, C. T., Wu, M. C., & Wu, W.-W. (2019). Sub-nA Low-Current HZO Ferroelectric Tunnel Junction for High-Performance and Accurate Deep Learning Acceleration. In 2019 IEEE International Electron Devices Meeting, IEDM 2019 Article 8993565 (Technical Digest - International Electron Devices Meeting, IEDM; Vol. 2019-December). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IEDM19573.2019.8993565

@inproceedings{6da43cd130d6408db141f555752f915b,

title = "Sub-nA Low-Current HZO Ferroelectric Tunnel Junction for High-Performance and Accurate Deep Learning Acceleration",

abstract = "This paper presents a unique opportunity of HZO ferroelectric tunnel junction (FTJ) for in-memory computing. The device operates at an extremely low sub-nA current while simultaneously achieving 50-ns fast switching, > 107 cycling endurance, > 10-yr retention, minimal variability, and analog state modulation. We analyze an FTJ-based deep binary neural network. It achieves better accuracy and remarkable 702, 101, and 7×104 times improvements in power, area, and energy-area product efficiency compared with those using NVMs with a typical μA cell current designed for fast memory access.",

author = "Wu, {Tzu Yun} and Tian-Sheuan Chang and Lee, {Heng Yuan} and Sheu, {Shyh Shyuan} and Lo, {Wei Chung} and Tuo-Hung Hou and Huang, {Hsin Hui} and Chu, {Yueh Hua} and Chang, {Chih Cheng} and Wu, {Ming Hung} and Hsu, {Chien Hua} and Wu, {Chien Ting} and Wu, {Min Ci} and Wen-Wei Wu",

note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 65th Annual IEEE International Electron Devices Meeting, IEDM 2019 ; Conference date: 07-12-2019 Through 11-12-2019",

year = "2019",

month = dec,

doi = "10.1109/IEDM19573.2019.8993565",

language = "English",

series = "Technical Digest - International Electron Devices Meeting, IEDM",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

booktitle = "2019 IEEE International Electron Devices Meeting, IEDM 2019",

address = "美國",

}

Wu, TY, Chang, T-S, Lee, HY, Sheu, SS, Lo, WC, Hou, T-H, Huang, HH, Chu, YH, Chang, CC, Wu, MH, Hsu, CH, Wu, CT, Wu, MC & Wu, W-W 2019, Sub-nA Low-Current HZO Ferroelectric Tunnel Junction for High-Performance and Accurate Deep Learning Acceleration. in 2019 IEEE International Electron Devices Meeting, IEDM 2019., 8993565, Technical Digest - International Electron Devices Meeting, IEDM, vol. 2019-December, Institute of Electrical and Electronics Engineers Inc., 65th Annual IEEE International Electron Devices Meeting, IEDM 2019, San Francisco, United States, 7/12/19. https://doi.org/10.1109/IEDM19573.2019.8993565

Sub-nA Low-Current HZO Ferroelectric Tunnel Junction for High-Performance and Accurate Deep Learning Acceleration. / Wu, Tzu Yun; Chang, Tian-Sheuan; Lee, Heng Yuan et al.
2019 IEEE International Electron Devices Meeting, IEDM 2019. Institute of Electrical and Electronics Engineers Inc., 2019. 8993565 (Technical Digest - International Electron Devices Meeting, IEDM; Vol. 2019-December).

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

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AU - Wu, Tzu Yun

AU - Chang, Tian-Sheuan

AU - Lee, Heng Yuan

AU - Sheu, Shyh Shyuan

AU - Lo, Wei Chung

AU - Hou, Tuo-Hung

AU - Huang, Hsin Hui

AU - Chu, Yueh Hua

AU - Chang, Chih Cheng

AU - Wu, Ming Hung

AU - Hsu, Chien Hua

AU - Wu, Chien Ting

AU - Wu, Min Ci

AU - Wu, Wen-Wei

PY - 2019/12

Y1 - 2019/12

N2 - This paper presents a unique opportunity of HZO ferroelectric tunnel junction (FTJ) for in-memory computing. The device operates at an extremely low sub-nA current while simultaneously achieving 50-ns fast switching, > 107 cycling endurance, > 10-yr retention, minimal variability, and analog state modulation. We analyze an FTJ-based deep binary neural network. It achieves better accuracy and remarkable 702, 101, and 7×104 times improvements in power, area, and energy-area product efficiency compared with those using NVMs with a typical μA cell current designed for fast memory access.

AB - This paper presents a unique opportunity of HZO ferroelectric tunnel junction (FTJ) for in-memory computing. The device operates at an extremely low sub-nA current while simultaneously achieving 50-ns fast switching, > 107 cycling endurance, > 10-yr retention, minimal variability, and analog state modulation. We analyze an FTJ-based deep binary neural network. It achieves better accuracy and remarkable 702, 101, and 7×104 times improvements in power, area, and energy-area product efficiency compared with those using NVMs with a typical μA cell current designed for fast memory access.

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BT - 2019 IEEE International Electron Devices Meeting, IEDM 2019

PB - Institute of Electrical and Electronics Engineers Inc.

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ER -

Wu TY, Chang TS, Lee HY, Sheu SS, Lo WC, Hou TH et al. Sub-nA Low-Current HZO Ferroelectric Tunnel Junction for High-Performance and Accurate Deep Learning Acceleration. In 2019 IEEE International Electron Devices Meeting, IEDM 2019. Institute of Electrical and Electronics Engineers Inc. 2019. 8993565. (Technical Digest - International Electron Devices Meeting, IEDM). doi: 10.1109/IEDM19573.2019.8993565

Sub-nA Low-Current HZO Ferroelectric Tunnel Junction for High-Performance and Accurate Deep Learning Acceleration

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