Cao, Yixin, Zhao, Tianshi, Liu, Chenguang, Zhao, Chun, Gao, Hao, Huang, Shichen, Li, Xianyao, Wang, Chengbo, Liu, Yina, Lim, Eng Gee et al (show 1 more authors)
(2023)
Neuromorphic visual artificial synapse in-memory computing systems based on GeOx-coated MXene nanosheets.
NANO ENERGY, 112.
p. 108441.
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Abstract
Artificial synapses with light signal perception capability offer the ability to neuromorphic visual signal processing system on demand. In light of the excellent optical and electrical characteristics, the low-dimensional materials have become one of the most favorable candidates of the key component for optoelectronic artificial synapses. Previously, our group originally proposed the synthesis of germanium oxide-coated MXene nanosheets. In this work, we further applied this technology into the optoelectronic synaptic thin-film transistors for the first time. The devices exhibited the adjustable postsynaptic current behaviors under the visible light inputs. Moreover, the potentiation and depression operation modes of the devices further improved the application potential of the devices in mimicking biological synapses. Regulated by the wavelength of incident lights, the proposed artificial synapse could effectively help detect the target area of the image. Eventually, we further showed the results of the devices in the projects of neural network computing task. The long-term potentiation/depression characteristics of the conductance were applied to the synaptic weight matrix for image identification and path recognition tasks. By adding knowledge transfer in the process of recognition, the epoch required for convergence has been greatly reduced. The result of high noise tolerance revealed the great potential of the proposed transistors in establishing high-efficiency and robustness hardware neuromorphic systems for in-memory computing.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | Artificial synapse, In-memory computing, MXene, Neural circuit policies, Visible light detection |
| Divisions: | Faculty of Science and Engineering > School of Electrical Engineering, Electronics and Computer Science |
| Depositing User: | Symplectic Admin |
| Date Deposited: | 15 Jun 2023 08:04 |
| Last Modified: | 15 Jun 2023 08:35 |
| DOI: | 10.1016/j.nanoen.2023.108441 |
| Related URLs: | |
| URI: | https://livrepository.liverpool.ac.uk/id/eprint/3171013 |
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