Electrical synaptic devices with a high recognition rate based on eco-friendly nanocomposites of a poly(methyl methacrylate) matrix embedded with graphene quantum dots for neuromorphic computing
Artificial synapse devices are currently the subjects of great attention as next-generation hardware for data processing to overcome the problem of data explosion due to the rapid advances in artificial intelligence and cloud computing technology. Nanocomposite-based devices enable unique applicatio...
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Published in | Organic electronics Vol. 126; p. 106997 |
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Main Authors | , , , , , , , |
Format | Journal Article |
Language | English |
Published |
Elsevier B.V
01.03.2024
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Subjects | |
Online Access | Get full text |
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Summary: | Artificial synapse devices are currently the subjects of great attention as next-generation hardware for data processing to overcome the problem of data explosion due to the rapid advances in artificial intelligence and cloud computing technology. Nanocomposite-based devices enable unique applications and have several advantages that cannot be achieved in single material-based devices. This study presents binary electrical synapses with digital data storing and analog data processing through a nanocomposite-based active layer composed of poly(methyl methacrylate) (PMMA) with embedded chlorine-functionalized graphene quantum dots (fGQDs) on an indium tin oxide (ITO) substrate. The Al/PMMA-fGQD/ITO devices with an fGQD concentrations of 5 wt% exhibited excellent memory performance with RON/ROFF ratio of 103. Moreover, we demonstrated that our device can successfully emulate biological synaptic functions such as potentiation/depression, short-term/long-term memory, paired-pulse facilitation, learning experience, and spike-timing-dependent plasticity. Furthermore, on the basis of the synaptic behaviors of the devices, they achieved about a 90 % recognition capability when a learning algorithm was used in a single-layer neural network.
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•A chlorine-functionalized graphene quantum dots (fGQDs) was introduced as tapping centers of an active layer in an artificial synapse.•The Al/PMMA-fGQDs/ITO device showed excellent electrical characteristics and successfully mimicked various synaptic functions.•Based on the conductance of the Al/PMMA-fGQDs/ITO device, the MNIST pattern recognition algorithm achieved about a 90 % recognition rate. |
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ISSN: | 1566-1199 1878-5530 |
DOI: | 10.1016/j.orgel.2024.106997 |