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Cited 1 time in webofscience Cited 2 time in scopus
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Optically excited threshold switching synapse characteristics on nitrogen-doped graphene oxide quantum dots (N-GOQDs)

Authors
Ali, MumtazSokolov, AndreyKo, Min JaeChoi, Changhwan
Issue Date
Feb-2021
Publisher
ELSEVIER SCIENCE SA
Keywords
Graphene oxide; Light stimulation; Diffusive memristor; Threshold switching; Synapse device
Citation
JOURNAL OF ALLOYS AND COMPOUNDS, v.855, no.2, pp.1 - 14
Indexed
SCIE
SCOPUS
Journal Title
JOURNAL OF ALLOYS AND COMPOUNDS
Volume
855
Number
2
Start Page
1
End Page
14
URI
https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/1567
DOI
10.1016/j.jallcom.2020.157514
ISSN
0925-8388
Abstract
Carbon-based organic material such as nitrogen-doped graphene oxide quantum dots (N-GOQDs) is a new-class material with unique biocompatible, high chemical inertness, and elevated photoluminescence properties. Two-terminal diffusive memristors can faithfully replicate biological synapse function via mutual similarities of in-/out-diffusion of Ag⁺ ions with biological Ca²⁺ migration dynamics for neural network applications. Inspired by hetero-plasticity phenomenon, in which Ca²⁺ dynamics can also be tuned by the 3rd counterpart - neuromodulatory axon, in this study, using an ultra-violet light source, we develop N-GOQDs based diffusive memristor that performs light-modulated synaptic behaviors. Specifically, photo-sensitive N-GOQDs ionic conductor shows n-pi* electron transitions under UV excitation; yet, nitrogen-doping further facilitates the electron transitions, giving out additional conductance induced by light. Further, we demonstrate endurable threshold resistive switching (TS) behavior based on Ag⁺ ions migration and its variety of facilitations via assisted UV illumination. The enhancement of post-synaptic current under assisted UV light, as well as the light stimulated transition from short-to long-term memory potentiation have been achieved. These findings are believed to be a step forward for the realization of higher bandwidth synapse modulation as future hardware-based neural network applications.
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