神经形态工程学
晶体管
纳米纤维
计算机科学
油藏计算
材料科学
能源消耗
光电子学
计算机体系结构
电子工程
电气工程
纳米技术
人工神经网络
工程类
循环神经网络
人工智能
电压
作者
Chuanyu Fu,Hangyuan Cui,Shuo Ke,Yixin Zhu,Xiangjing Wang,Yang� Yang,Changjin Wan,Qing Wan
出处
期刊:IEEE Electron Device Letters
[Institute of Electrical and Electronics Engineers]
日期:2023-08-01
卷期号:44 (8): 1364-1367
被引量:1
标识
DOI:10.1109/led.2023.3290998
摘要
In this letter, we propose neuromorphic transistors employing indium oxide (In2O3) nanofibers as the channel layers. Basic synaptic function, such as short-term memory can be emulated by one nanofiber neuromorphic transistor. Nonlinear synaptic function and short-term memory characteristic of such neuromorphic transistors are favorable for reservoir computing (RC) system with high energy-efficiency. Ultra-low energy consumption (15 pJ per reservoir state) and ultra-high accuracy (100%) of speech digital recognition are realized based on such nanofiber neuromorphic transistors, proving a great potential of the RC system for intelligent processing tasks.
科研通智能强力驱动
Strongly Powered by AbleSci AI