A light-stimulated neuromorphic device based on graphene hybrid phototransistor

TL;DR

This paper introduces a graphene-nanotube hybrid phototransistor functioning as a light-stimulated artificial synapse, enabling optical-to-neural conversion with flexible plasticity and multi-wavelength processing for advanced neuromorphic computing.

Contribution

It presents a novel, silicon-compatible neuromorphic device that directly converts optical stimuli into neural signals with tunable plasticity and multiplexed optical processing capabilities.

Findings

Exhibits flexible short- and long-term plasticity.

Supports wavelength-division multiplexing for multiple stimuli.

Enables complex optical processing via charge-trap-mediated coupling.

Abstract

Neuromorphic chip refers to an unconventional computing architecture that is modelled on biological brains. It is ideally suited for processing sensory data for intelligence computing, decision-making or context cognition. Despite rapid development, conventional artificial synapses exhibit poor connection flexibility and require separate data acquisition circuitry, resulting in limited functionalities and significant hardware redundancy. Here we report a novel light-stimulated artificial synapse based on a graphene-nanotube hybrid phototransistor that can directly convert optical stimuli into a "neural image" for further neuronal analysis. Our optically-driven synapses involve multiple steps of plasticity mechanisms and importantly exhibit flexible tuning of both short- and long-term plasticity. Furthermore, our neuromorphic phototransistor can take multiple pre-synaptic light stimuli via wavelength-division multiplexing and allows advanced optical processing through charge-trap-mediated optical coupling. The capability of complex neuromorphic functionalities in a simple silicon-compatible device paves the way for novel neuromorphic computing architectures involving photonics.