Yu Jinran, Yang Xixi, Gao Guoyun, Xiong Yao, Wang Yifei, Han Jing, Chen Youhui, Zhang Huai, Sun Qijun, Wang Zhong Lin
Beijing Institute of Nanoenergy and Nanosystems, Chinese Academy of Sciences, Beijing 100083, P. R. China.
School of Nanoscience and Technology, University of Chinese Academy of Sciences, Beijing 100049, P. R. China.
Sci Adv. 2021 Mar 17;7(12). doi: 10.1126/sciadv.abd9117. Print 2021 Mar.
Developing multifunctional and diversified artificial neural systems to integrate multimodal plasticity, memory, and supervised learning functions is an important task toward the emulation of neuromorphic computation. Here, we present a bioinspired mechano-photonic artificial synapse with synergistic mechanical and optical plasticity. The artificial synapse is composed of an optoelectronic transistor based on graphene/MoS heterostructure and an integrated triboelectric nanogenerator. By controlling the charge transfer/exchange in the heterostructure with triboelectric potential, the optoelectronic synaptic behaviors can be readily modulated, including postsynaptic photocurrents, persistent photoconductivity, and photosensitivity. The photonic synaptic plasticity is elaborately investigated under the synergistic effect of mechanical displacement and the light pulses embodying different spatiotemporal information. Furthermore, artificial neural networks are simulated to demonstrate the improved image recognition accuracy up to 92% assisted with mechanical plasticization. The mechano-photonic artificial synapse is highly promising for implementing mixed-modal interaction, emulating complex biological nervous system, and promoting the development of interactive artificial intelligence.
开发多功能、多样化的人工神经系统以整合多模态可塑性、记忆和监督学习功能是迈向模拟神经形态计算的一项重要任务。在此，我们展示了一种具有协同机械和光学可塑性的仿生机械光子人工突触。该人工突触由基于石墨烯/MoS异质结构的光电晶体管和集成的摩擦纳米发电机组成。通过利用摩擦电势控制异质结构中的电荷转移/交换，可以轻松调节光电突触行为，包括突触后光电流、持续光电导和光敏性。在机械位移和体现不同时空信息的光脉冲的协同作用下，对光子突触可塑性进行了详细研究。此外，通过模拟人工神经网络来证明，在机械塑化的辅助下，图像识别准确率提高到了92%。这种机械光子人工突触在实现混合模态交互、模拟复杂生物神经系统以及推动交互式人工智能发展方面具有巨大潜力。
