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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.
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
Optical and optoelectronic approaches of performing matrix-vector multiplication (MVM) operations have shown the great promise of accelerating machine learning (ML) algorithms with unprecedented performance. The incorporation of nanomaterials into th
The development of memristive device technologies has reached a level of maturity to enable the design of complex and large-scale hybrid memristive-CMOS neural processing systems. These systems offer promising solutions for implementing novel in-memo
Neuromorphic computing takes inspiration from the brain to create energy efficient hardware for information processing, capable of highly sophisticated tasks. In this article, we make the case that building this new hardware necessitates reinventing
Despite neuromorphic engineering promises the deployment of low latency, adaptive and low power systems that can lead to the design of truly autonomous artificial agents, the development of a fully neuromorphic artificial agent is still missing. Whil