A light-stimulated synaptic device based on graphene hybrid phototransistor. (2nd August 2017)

• Record Type: Journal Article
• Authors: Qin, Shuchao; Wang, Fengqiu; Liu, Yujie; Wan, Qing; Wang, Xinran; Xu, Yongbing; Shi, Yi; Wang, Xiaomu; Zhang, Rong
• Is Part Of: 2D materials. Volume 4:Number 3(2017)
• Publication Date: 2017-08-02
• Subjects: 2d heterostructure -- artificial synapse -- neural network -- optical signal processing -- graphene-carbon nanotube hybrid; Graphene -- Periodicals; Materials science -- Periodicals; Nanostructured materials -- Periodicals; 620.115
• DOI: 10.1088/2053-1583/aa805e
• Languages: English
• ISSNs: 2053-1583

Abstract

Abstract: Neuromorphic chips refer to an unconventional computing architecture that is modelled on biological brains. They are increasingly employed for processing sensory data for machine vision, context cognition, and decision making. Despite rapid advances, neuromorphic computing has remained largely an electronic technology, making it a challenge to access the superior computing features provided by photons, or to directly process vision data that has increasing importance to artificial intelligence. Here we report a novel light-stimulated synaptic device based on a graphene-carbon nanotube hybrid phototransistor. Significantly, the device can respond to optical stimuli in a highly neuron-like fashion and exhibits flexible tuning of both short- and long-term plasticity. These features combined with the spatiotemporal processability make our device a capable counterpart to today's electrically-driven artificial synapses, with superior reconfigurable capabilities. In addition, our device allows for generic optical spike processing, which provides a foundation for more sophisticated computing. The silicon-compatible, multifunctional photosensitive synapse opens up a new opportunity for neural networks enabled by photonics and extends current neuromorphic systems in terms of system complexities and functionalities.