학술논문
Sub-nA Low-Current HZO Ferroelectric Tunnel Junction for High-Performance and Accurate Deep Learning Acceleration
Document Type
Conference
Author
Source
2019 IEEE International Electron Devices Meeting (IEDM) Electron Devices Meeting (IEDM), 2019 IEEE International. :6.3.1-6.3.4 Dec, 2019
Subject
Language
ISSN
2156-017X
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, > 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 7×10 4 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.