神经形态工程学
尖峰神经网络
材料科学
计算机体系结构
计算
晶体管
人工神经网络
高效能源利用
过程(计算)
钥匙(锁)
电子工程
计算机科学
人工智能
电气工程
工程类
电压
算法
操作系统
计算机安全
作者
Yuseong Jang,Junhyeong Park,Jimin Kang,Soo‐Yeon Lee
出处
期刊:ACS applied electronic materials
[American Chemical Society]
日期:2022-02-01
卷期号:4 (4): 1427-1448
被引量:51
标识
DOI:10.1021/acsaelm.1c01088
摘要
Brain-inspired neuromorphic computing emulates the biological functions of the human brain to achieve highly intensive data processing with low power consumption. In particular, spiking neural networks (SNNs) that consist of artificial synapses can process spatiotemporal information while enabling energy-efficient neuromorphic computations. Artificial synapses are a key element of sophisticated neuromorphic hardware, so a significant amount of research has been conducted to develop various materials and device structures. Of these, we assess amorphous InGaZnO (IGZO)-based synaptic transistors that have exhibited properties suitable for emerging hybrid optoelectronic neuromorphic systems. Here, we describe the fundamental principles of neuromorphic computations, neuron circuits, and synaptic devices according to recent studies. IGZO-based transistors are discussed, from their material properties to various device physics for electronic- and/or photonic-neuromorphic systems with extraordinary biological emulations.
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