Bioinspired mechano-photonic artificial synapse based on graphene/MoS2 heterostructure

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/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.

Automated summary

The research presents a bioinspired mechano-photonic artificial synapse that can modulate optoelectronic synaptic behaviors by controlling charge transfer/exchange in the heterostructure with triboelectric potential. The artificial synapse shows great potential for implementing mixed-modal interaction and promoting the development of interactive artificial intelligence.