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.
Text
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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 |
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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 |