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

*此作品的通信作者

研究成果: Conference contribution同行評審

14 引文 斯高帕斯(Scopus)

摘要

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.

原文English
主出版物標題2019 IEEE International Electron Devices Meeting, IEDM 2019
發行者Institute of Electrical and Electronics Engineers Inc.
頁數4
ISBN(電子)9781728140315
DOIs
出版狀態Published - 12月 2019
事件65th Annual IEEE International Electron Devices Meeting, IEDM 2019 - San Francisco, United States
持續時間: 7 12月 201911 12月 2019

出版系列

名字Technical Digest - International Electron Devices Meeting, IEDM
2019-December
ISSN(列印)0163-1918

Conference

Conference65th Annual IEEE International Electron Devices Meeting, IEDM 2019
國家/地區United States
城市San Francisco
期間7/12/1911/12/19

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