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An Optogenetics-Inspired Flexible van der Waals Optoelectronic Synapse and its Application to a Convolutional Neural Network

Authors
Seo SeunghwanLee Je-JunLee Ryong-GyuKim Tae HyungPark SangyongJung SooyoungLee Hyun-KyuAndreev MaksimLee Kyeong-BaeJung Kil-SuOh SeyongLee Ho-JunKim Ki SeokYeom Geun YoungKim Yong-HoonPark Jin-Hong
Issue Date
Oct-2021
Publisher
WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim
Keywords
2D van der Waals layered materials; artificial optoelectronic synapses; brain-inspired computing; convolutional neural networks; flexible artificial synapses; persistent photoconductivity effect; rhenium disulfide
Citation
Advanced Materials, v.33, no.40, pp 1 - 12
Pages
12
Indexed
SCIE
SCOPUS
Journal Title
Advanced Materials
Volume
33
Number
40
Start Page
1
End Page
12
URI
https://scholarworks.bwise.kr/erica/handle/2021.sw.erica/112835
DOI
10.1002/adma.202102980
ISSN
0935-9648
1521-4095
Abstract
Optogenetics refers to a technique that uses light to modulate neuronal activity with a high spatiotemporal resolution, which enables the manipulation of learning and memory functions in the human brain. This strategy of controlling neuronal activity using light can be applied for the development of intelligent systems, including neuromorphic and in-memory computing systems. Herein, a flexible van der Waals (vdW) optoelectronic synapse is reported, which is a core component of optogenetics-inspired intelligent systems. This synapse is fabricated on 2D vdW layered rhenium disulfide (ReS2) that features an inherent photosensitive memory nature derived from the persistent photoconductivity (PPC) effect, successfully mimicking the dynamics of biological synapses. Based on first-principles calculations, the PPC effect is identified to originate from sulfur vacancies in ReS2 that have an inherent tendency to form shallow defect states near the conduction band edges and under optical excitation lead to large lattice relaxation. Finally, the feasibility of applying the synapses in optogenetics-inspired intelligent systems is demonstrated via training and inference tasks for the CIFAR-10 dataset using a convolutional neural network composed of vdW optoelectronic synapse devices.
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