High-Performance Microwave-Synthesized Graphene Supercapacitive Synapses for Reservoir Preprocessing in Neuromorphic...

Reservoir computing bridges the gap between artificial intelligence and the physical environment. The realization of such systems depends on artificial synapses that emulate neurobiological plasticity in response to external stimuli. Functioning as reservoirs, these synapses convert the input signals into reservoir states, which are then retrieved by the readout layer. However, energy is merely dissipated during the operation of existing artificial synapses. Herein, we propose an energy-storage-involved reservoir architecture based on graphene-based supercapacitors. Reduced graphene oxide electrode, fabricated via metal-assisted microwave reduction, exhibits a mesoporous structure and low defect density. The supercapacitor delivers a specific capacitance of 352 F g-1 at a current density of 1 A g-1. Under current stimuli, the energy-storage device replicates biological synaptic functions through voltage responses. Next, the voltage responses can be used to mimic human perceptual learning, in which sensory systems are enhanced through experience. Moreover, integrated with a diffractive deep neural network in simulation, supercapacitor-based reservoir preprocessing can accurately identify static and dynamic handwritten images, outperforming an artificial neural network and an unprocessed diffractive deep neural network. These results pave the way for neuromorphic computing that integrates energy-storage-involved information processing, offering promising applications for energy-efficient artificial intelligence hardware.