A new type artificial synapse based on the organic copolymer memcapacitor

We propose an on-chip nanophotonic system that do the neural network computing all in optical domain. Our system is able to give equivalent learning performance, while potentially achieve 3 orders of magnitude faster speed than conventional electronic neural nets.

On-Chip Optical Neuromorphic Computing

In this work, we demonstrate a nanowire phototransistor with synaptic behavior. The device is based on a single crystalline InAs nanowire. The device mimics synaptic neuromorphic functions such as short-term plasticity and long-term plasticity. Moreover, these synaptic behaviors can be controlled by the application of gate voltage, that is, long-term plasticity is restrained/enhanced under the negative/positive gate bias. This synapse-like behavior of the transistor is attributed to the photogating effect.

Contrallable Synaptic Behavior in Photonic Neuromorphic Transistor

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.

A light-stimulated neuromorphic device based on graphene hybrid phototransistor

InGaN/GaN multi-quantum-well (MQW) nanowires and accordingly light-emitting-diodes (LEDs) were fabricated on the n-GaN/sapphire substrate with a nano-patterned SiO 2 film as growth mask. The structural characteristics, optical and electrical properties were investigated. the observed results show that a InGaN/GaN MQW nanowire has smooth surface morphologies and triangular cross sectional structure. A strong cathodoluminescence emission peak related to InGaN/GaN MQW is observed located at 461 nm. In addition, InGaN/GaN MQW nanowire LED shows typical p-n junction characteristics with a low turn-on voltage, and its electroluminescence displays purplish.

InGaN/GaN multi-quantum-well nanowires and light emitting

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A light-stimulated neuromorphic device based on graphene hybrid phototransistor

InGaN/GaN multi-quantum-well nanowires and light emitting