Low-Power Artificial Neurons Based on Ag/TiN/HfAlOx/Pt Threshold Switching Memristor for Neuromorphic Computing

被引：95

作者：

Lu, Yi-Fan ^{[1
]}

Li, Yi ^{[1
]}

Li, Haoyang ^{[1
]}

Wan, Tian-Qing ^{[1
]}

Huang, Xiaodi ^{[1
]}

He, Yu-Hui ^{[1
]}

Miao, Xiangshui ^{[1
]}

机构：

[1] Huazhong Univ Sci & Technol, Wuhan Natl Lab Optoelect, Sch Opt & Elect Informat, Wuhan 430074, Peoples R China

来源：

IEEE ELECTRON DEVICE LETTERS | 2020年 / 41卷 / 08期

基金：

中国国家自然科学基金;

关键词：

Threshold switch; low power; artificial neuron; leaky-integrate-and-fire; SPIKING NEURONS; NEURAL-NETWORKS;

D O I：

10.1109/LED.2020.3006581

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

Threshold switching (TS) devices are promising candidates to build highly compact and energy efficient artificial neurons. Here, we present a Pt/Ag/TiN/HfAlOx/Pt (PATHP) device with excellent TS characteristics, including a large selectivity(10(10)), a wide range of operation current from 10 nA to 1 mA, an extremely steep slope (0.63 mV/dec) and fast turn-on speed (50 ns). The stable TS performance can be ascribed to the introduction of TiN buffer layer and the alternate atomic layer deposited HfAlOx layer. Further, we experimentally demonstrate the functions of leaky-integrate-and-fire neurons with low power feature based on a RC circuit and a single device, respectively, which are essential for constructing spiking neuromorphic systems.

引用

页码：1245 / 1248

页数：4

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