神经形态工程学
摩擦电效应
数码产品
瓶颈
冯·诺依曼建筑
计算机科学
高效能源利用
人工神经网络
人工智能
工程类
电气工程
嵌入式系统
材料科学
复合材料
操作系统
作者
Guanglong Ding,Su‐Ting Han,Vellaisamy A. L. Roy,Chi‐Ching Kuo,Ye Zhou
出处
期刊:Energy reviews
[Elsevier BV]
日期:2023-01-07
卷期号:2 (1): 100014-100014
被引量:22
标识
DOI:10.1016/j.enrev.2023.100014
摘要
Building the brain-inspired neural network computing system based neuromorphic electronics is an effective approach to break the von Neumann bottleneck on the hardware level and realize the information processing with high efficiency and low energy consumption in this big data explosion age. Triboelectric nanogenerator (TENG) has two functions of sensing and energy conversion, which promote the application as sensor and/or power supply in self-powered neuromorphic electronics for data storage and biological synapse/neuron behaviors mimicking. This article highlights the relevant works of TENGs for memory devices, artificial synapses and artificial neurons, performs a systematic comparison, and puts forward the future research possibilities and challenges, with the hope of attracting more researchers into this field and promoting the development of TENG based neuromorphic electronics.
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