期刊
NANOSCALE RESEARCH LETTERS
卷 15, 期 1, 页码 -出版社
SPRINGER
DOI: 10.1186/s11671-019-3238-x
关键词
Charge trapping memory; Synaptic device; Two-dimension material; Artificial neural network
资金
- NSFC [61704030, 61522404]
- Shanghai Rising-Star Program [19QA1400600]
- Program of Shanghai Subject Chief Scientist [18XD1402800]
- Support Plans for the Youth Top-Notch Talents of China
Synaptic devices are necessary to meet the growing demand for the smarter and more efficient system. In this work, the anisotropic rhenium disulfide (ReS2) is used as a channel material to construct a synaptic device and successfully emulate the long-term potentiation/depression behavior. To demonstrate that our device can be used in a large-scale neural network system, 165 pictures from Yale Face database are selected for evaluation, of which 120 pictures are used for artificial neural network (ANN) training, and the remaining 45 pictures are used for ANN testing. A three-layer ANN containing more than 10(5) weights is proposed for the face recognition task. Also 120 continuous modulated conductance states are selected to replace weights in our well-trained ANN. The results show that an excellent recognition rate of 100% is achieved with only 120 conductance states, which proves a high potential of our device in the artificial neural network field.
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