• Jinran Yu, Xixi Yang, Guoyun Gao, Yao Xiong, Yifei Wang, Jing Han, Youhui Chen, Huai Zhang, Qijun Sun, and Zhong Lin Wang.
• Beijing Institute of Nanoenergy and Nanosystems, Chinese Academy of Sciences, Beijing 100083, P. R. China.
• Sci Adv. 2021 Mar 1; 7 (12).

AbstractDeveloping 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/MoS2 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.Copyright © 2021 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution NonCommercial License 4.0 (CC BY-NC).

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