Journal
MATERIALS TODAY PHYSICS
Volume 23, Issue -, Pages -Publisher
ELSEVIER
DOI: 10.1016/j.mtphys.2022.100650
Keywords
WOx memristor; Flexible synaptic device; Neuromorphic computing system
Funding
- National Natural Science Foundation of China (NSFC) [51702055, 12172093, 62073084, 11904056, 11704079]
- Guangzhou Basic and Applied Basic Research Foundation [202102021035]
- Open Foundation of Guangdong Provincial Key Laboratory of Electronic Functional Materials and Devices [EFMD2021008 M]
- Special Funds for the Cultivation of Guangdong College Students' Scientific and Technological Innovation (Climbing Program Special Funds) [pdjh2020a0174]
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This paper presents a feasible approach for the realization of flexible neuromorphic computing systems by fabricating flexible Au/WOx/Pt/Mica memristors and demonstrating their highly adjustable resistance states and the functionality of biological synapses and neurons in different states.
Neuromorphic computing, composed of artificial synapses and neural network algorithms, is expected to replace the traditional von Neumann computer architecture to build the next-generation intelligent systems due to its more energy-efficient features. In this work, the flexible Au/WOx/Pt/Mica memristor with simple structure is fabricated by RF magnetron sputtering, and the highly adjustable resistance states, the function of biological synapses and neurons in different states, such as short/long-term plasticity, paired-pulse facilitation, and spike-time-dependent plasticity, were demonstrated in flexible WOx memristor. Furthermore, we established a convolutional neural networks (CNNs) architecture for the Mixed National Institute of Standards and Technology (MNIST) pattern categorization and demonstrated that the recognition performance is comparable to that of a software-based neural network. These results provide a feasible approach for the realization of flexible neuromorphic computing systems in the future. (C) 2022 Elsevier Ltd. All rights reserved.
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