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

Abstract

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.