Optically excited threshold switching synapse characteristics on nitrogen-doped graphene oxide quantum dots (N-GOQDs)
- Authors
- Ali, Mumtaz; Sokolov, Andrey; Ko, Min Jae; Choi, 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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