Improvement of power consumption and linearity of integrate/fire characteristics using diffusive memristors with defective graphene for artificial neuron application

Abstract

Diffusive memristors made with conductive metal bridge random access memories (RAMs) have been studied for low power consumption and linearity of integrate/fire characteristics of artificial neurons by using a defective graphene interlayer. Utilizing this approach, a volatile artificial neuron incorporating Ag demonstrates sustained low-power characteristics inherent to Ag-based devices, accompanied by linearity in spike occurrence through precise control of on/off-current ratio and conductive filament dissolution time. This approach enables the precise tuning of the neuron's behavior and offers potential applications in neuromorphic computing and artificial intelligence.