All-in-one metal-oxide heterojunction artificial synapses for visual sensory and neuromorphic computing systems


Liu, Qihan, Yin, Li, Zhao, Chun, Wu, Ziang, Wang, Jingyi, Yu, Xiaoran, Wang, Zixin, Wei, Wenxi, Liu, Yina, Mitrovic, Ivona Z ORCID: 0000-0003-4816-8905 et al (show 3 more authors) (2022) All-in-one metal-oxide heterojunction artificial synapses for visual sensory and neuromorphic computing systems. NANO ENERGY, 97. p. 107171.

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Abstract

An all-in-one artificial synapse integrating central nervous and sensory nervous functions utilizing low-dimensional metal-oxide heterojunction is demonstrated in this work. With an ion-electrolyte gate, synaptic emulations modulated by electrical and photonic stimulus have been integrated into one high-performance three-terminal artificial synapse. Various long-term and short-term synaptic plasticity functions have been achieved by altering the electrolyte-gate stimulus amplitude/width/frequency/number. The emulated synaptic plasticity and maintained synaptic weight states enable artificial synapses for neuromorphic computing. Simulated artificial neural network based on the artificial synapses achieved Covid-19 chest image recognition (>85%). The photo-sensitive metal-oxide heterojunction enables the synaptic functions mimicking the biological visual sensory functions responding to optical and UV stimulus. Photonic synaptic plasticity modulations responding to photonic stimulus wavelength/power/width/number are investigated, and short-term/long-term synaptic plasticity transition was achieved. Dual-mode synaptic modulation combining photonic stimulus and gate stimulus was examined. Finally, an artificial neural network was demonstrated based on the synapses with dual-mode synaptic weight modulation, indicating the potential of the artificial synapse for compact artificial intelligence systems combing neuromorphic computing and visual sensory nervous functions.

Item Type: Article
Uncontrolled Keywords: Artificial synapse, Synaptic transistor, Metal -oxide semiconductor, Photonic Synapse, All-in-one device
Divisions: Faculty of Science and Engineering > School of Electrical Engineering, Electronics and Computer Science
Faculty of Science and Engineering > School of Physical Sciences
Depositing User: Symplectic Admin
Date Deposited: 12 Apr 2022 13:21
Last Modified: 23 Mar 2023 02:30
DOI: 10.1016/j.nanoen.2022.107171
Related URLs:
URI: https://livrepository.liverpool.ac.uk/id/eprint/3152793
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