標題: Sub-nA Low-Current HZO Ferroelectric Tunnel Junction for High-Performance and Accurate Deep Learning Acceleration
作者: Wu, Tzu-Yun
Huang, Hsin-Hui
Chu, Yueh-Hua
Chang, Chih-Cheng
Wu, Ming -Hung
Hsu, Chien-Hua
Wu, Chien -Ting
Wu, Min-Ci
Wu, Wen-Wei
Chang, Tian-Sheuan
Lee, Heng-Yuan
Sheu, Shyh-Shyuan
Lot, Wei-Chung
Hou, Tuo-Hung
材料科學與工程學系
電子工程學系及電子研究所
Department of Materials Science and Engineering
Department of Electronics Engineering and Institute of Electronics
公開日期: 1-一月-2019
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, > 10(7) 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 x 10(4) times improvements in power, area, and energy area product efficiency compared with those using NVMs with a typical mu A cell current designed for fast memory access.
URI: http://hdl.handle.net/11536/155252
ISBN: 978-1-7281-4031-5
ISSN: 2380-9248
期刊: 2019 IEEE INTERNATIONAL ELECTRON DEVICES MEETING (IEDM)
起始頁: 0
結束頁: 0
顯示於類別:會議論文