Optically excited threshold switching synapse characteristics on nitrogen-doped graphene oxide quantum dots (N-GOQDs)

被引:20
作者
Ali, Mumtaz [1 ]
Sokolov, Andrey [2 ]
Ko, Min Jae [3 ]
Choi, Changhwan [2 ]
机构
[1] Hanyang Univ, Dept Organ & Nano Engn, Seoul 04763, South Korea
[2] Hanyang Univ, Div Mat Sci & Engn, Seoul 04763, South Korea
[3] Hanyang Univ, Dept Chem Engn, Seoul 04763, South Korea
基金
新加坡国家研究基金会;
关键词
Graphene oxide; Light stimulation; Diffusive memristor; Threshold switching; Synapse device; LONG-TERM POTENTIATION; MEMORY; PLASTICITY; NETWORKS; ARRAYS; ENERGY;
D O I
10.1016/j.jallcom.2020.157514
中图分类号
O64 [物理化学(理论化学)、化学物理学];
学科分类号
070304 ; 081704 ;
摘要
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. (C) 2020 Elsevier B.V. All rights reserved.
引用
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页数:14
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