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

• Published in: IEEE electron device letters Vol. 41; no. 8; pp. 1245 - 1248
• Main Authors: Lu, Yi-Fan, Li, Yi, Li, Haoyang, Wan, Tian-Qing, Huang, Xiaodi, He, Yu-Hui, Miao, Xiangshui
• Format: Journal Article
• Language: English
• Published: New York IEEE 01-08-2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: artificial neuron; Buffer layers; Circuits; leaky-integrate-and-fire; low power; Memristors; Neuromorphic computing; Neurons; Optical switches; Platinum; Power management; RC circuits; Selectivity; Silver; Switching; Threshold switch; Tin; Voltage measurement
• ISSN: 0741-3106; 1558-0563
• DOI: 10.1109/LED.2020.3006581

Threshold switching (TS) devices are promising candidates to build highly compact and energy efficient artificial neurons. Here, we present a Pt/Ag/TiN/HfAlO x /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.