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Bioinspired mechano-photonic artificial synapse based on graphene/MoS2 heterostructure
Jinran Yu1,2, Xixi Yang1, Guoyun Gao1
1Beijing Institute of Nanoenergy and Nanosystems, Chinese Academy of Sciences, Beijing 100083, P. R. China.
Science Advances
|March 18, 2021
Summary
This study introduces a novel mechano-photonic artificial synapse that combines mechanical and optical plasticity for advanced neuromorphic computing. This bioinspired device enhances artificial neural networks, improving image recognition accuracy for interactive artificial intelligence.
Area of Science:
- Materials Science
- Neuroscience
- Computer Engineering
Background:
- Developing artificial neural systems for neuromorphic computation requires integrating multimodal plasticity, memory, and supervised learning.
- Current artificial synapses often lack the complexity to emulate biological nervous systems effectively.
Purpose of the Study:
- To present a bioinspired mechano-photonic artificial synapse with synergistic mechanical and optical plasticity.
- To demonstrate the device's capability in modulating optoelectronic synaptic behaviors and improving artificial neural network performance.
Main Methods:
- Fabrication of an optoelectronic transistor using a graphene/MoS2 heterostructure integrated with a triboelectric nanogenerator.
- Modulation of synaptic behaviors via triboelectric potential controlling charge transfer in the heterostructure.
- Investigation of photonic synaptic plasticity under combined mechanical displacement and light pulses.
Main Results:
- The artificial synapse exhibited controllable optoelectronic synaptic behaviors, including postsynaptic photocurrents, persistent photoconductivity, and photosensitivity.
- Synergistic effects of mechanical and optical stimuli were observed, enhancing photonic synaptic plasticity.
- Simulated artificial neural networks achieved up to 92% image recognition accuracy with mechanical plasticization.
Conclusions:
- The developed mechano-photonic artificial synapse offers a promising platform for mixed-modal interaction and emulating complex biological nervous systems.
- This technology advances the development of interactive artificial intelligence and neuromorphic computing.
- The device showcases the potential of bioinspired designs in creating sophisticated artificial intelligence systems.
