Graphene oxide based synaptic memristor device for neuromorphic computing

TL;DR

This paper presents a graphene oxide-based memristor that mimics biological synapses, demonstrating key learning behaviors and properties suitable for neuromorphic computing applications.

Contribution

The work develops a graphene oxide memristor exhibiting synaptic behaviors and spike-timing-dependent plasticity, advancing neuromorphic device technology.

Findings

Exhibits analog memory, potentiation, and depression behaviors.

Mimics spike-timing-dependent plasticity learning rule.

Shows non-volatile endurance, retentivity, and multilevel switching.

Abstract

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