Graphene oxide based synaptic memristor device for neuromorphic computing

Abstract Brain-inspired neuromorphic computing which consist neurons and synapses, with an ability to perform complex information processing has unfolded a new paradigm of overcome the von Neumann bottleneck. Electronic synaptic memristor devices can compete biological synapses are indeed significant for computing. In this work, we demonstrate our efforts develop realize graphene oxide (GO) based device as device, mimic synapse. Indeed, exhibits essential learning behavior including analog memory characteristics, potentiation depression. Furthermore, spike-timing-dependent-plasticity rule is mimicked by engineering pre- post-synaptic spikes. addition, non-volatile properties such endurance, retentivity, multilevel switching explored. These results suggest that Ag/GO/fluorine-doped tin would be potential candidate future applications.