神经形态工程学
材料科学
铁电性
晶体管
MNIST数据库
图像传感器
突触重量
光电子学
非易失性存储器
钛酸钡
计算机科学
电子工程
人工神经网络
人工智能
电压
电气工程
工程类
电介质
作者
Jianyu Du,Donggang Xie,Qinghua Zhang,Hai Zhong,Fanqi Meng,Xingke Fu,Qinchao Sun,Hao Ni,Tao Li,Er‐Jia Guo,Haizhong Guo,Meng He,Can Wang,Lin Gu,Xiulai Xu,Guangyu Zhang,Guozhen Yang,Kuijuan Jin,Chen Ge
出处
期刊:Nano Energy
[Elsevier]
日期:2021-08-14
卷期号:89: 106439-106439
被引量:129
标识
DOI:10.1016/j.nanoen.2021.106439
摘要
The rapid development of the artificial intelligence field has increased the demand for retina-inspired neuromorphic vision sensors with integrated sensing, memory, and processing functions. Here, we present a neuromorphic vision sensor with an optoelectronic transistor structure consisting of monolayer molybdenum disulfide and barium titanate ferroelectric film. Beyond conventional electrical tuning of ferroelectric polarization, the optoelectronic transistor can exhibit a light-dosage tunable synaptic behavior with a high switching ratio and good non-volatility, enabled by photo-induced ferroelectric polarization reversal. The wavelength-dependent optical sensing and multi-level optical memory properties are utilized to achieve the in-sensor neuromorphic visual pre-processing. A simulated artificial neural network built from the proposed vision sensors with neuromorphic pre-processing function demonstrated that the image recognition rate for the Modified National Institute of Standards and Technology (MNIST) handwritten dataset could be significantly improved by reducing redundant data. The obtained results suggest that 2D semiconductor/ferroelectric optoelectronic transistors can provide a promising hardware implementation towards constructing high-performance neuromorphic visual systems
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