记忆电阻器
神经形态工程学
可扩展性
材料科学
调制(音乐)
电导
人工神经元
电压
功率(物理)
人工神经网络
集合(抽象数据类型)
能量(信号处理)
光电子学
计算机科学
阈值电压
拓扑(电路)
生物系统
电子工程
电气工程
人工智能
晶体管
物理
凝聚态物理
声学
工程类
生物
数据库
程序设计语言
量子力学
作者
Xiaojuan Lian,Jinke Fu,Zhixuan Gao,Shi‐Pu Gu,Lei Wang
出处
期刊:Chinese Physics B
[IOP Publishing]
日期:2022-04-14
卷期号:32 (1): 017304-017304
被引量:8
标识
DOI:10.1088/1674-1056/ac673f
摘要
Threshold switching (TS) memristors can be used as artificial neurons in neuromorphic systems due to their continuous conductance modulation, scalable and energy-efficient properties. In this paper, we propose a low power artificial neuron based on the Ag/MXene/GST/Pt device with excellent TS characteristics, including a low set voltage (0.38 V) and current (200 nA), an extremely steep slope (< 0.1 mV/dec), and a relatively large off/on ratio (> 10 3 ). Besides, the characteristics of integrate and fire neurons that are indispensable for spiking neural networks have been experimentally demonstrated. Finally, its memristive mechanism is interpreted through the first-principles calculation depending on the electrochemical metallization effect.
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