ReS2 Charge Trapping Synaptic Device for Face Recognition Application

Autor: Ze-Hui Fan, Min Zhang, Lu-Rong Gan, Lin Chen, Hao Zhu, Qing-Qing Sun, David Wei Zhang
Jazyk: angličtina
Rok vydání: 2020
Předmět:
Zdroj: Nanoscale Research Letters, Vol 15, Iss 1, Pp 1-8 (2020)
Druh dokumentu: article
ISSN: 1931-7573
1556-276X
DOI: 10.1186/s11671-019-3238-x
Popis: Abstract 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 105 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.
Databáze: Directory of Open Access Journals
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