Multilevel resistive switching and synaptic plasticity of nanoparticulated cobaltite oxide memristive device
- Authors
- Dongale, T.D.; Khot, A.C.; Takaloo, A.V.; Son, K.R.; Kim, T.G.
- Issue Date
- 10-7월-2021
- Publisher
- Chinese Society of Metals
- Keywords
- Cobaltite oxide; Memristive device; Multilevel resistive switching; STDP; Synaptic plasticity
- Citation
- Journal of Materials Science and Technology, v.78, pp.81 - 91
- Indexed
- SCIE
SCOPUS
- Journal Title
- Journal of Materials Science and Technology
- Volume
- 78
- Start Page
- 81
- End Page
- 91
- URI
- https://scholar.korea.ac.kr/handle/2021.sw.korea/128720
- DOI
- 10.1016/j.jmst.2020.10.046
- ISSN
- 1005-0302
- Abstract
- Multilevel resistive switching (RS) is a key property to embrace the full potential of memristive devices for non-volatile memory and neuromorphic computing applications. In this study, we employed nanoparticulated cobaltite oxide (Co3O4) as a model material to demonstrate the multilevel RS and synaptic learning capabilities because of its multiple and stable redox state properties. The Pt/Co3O4/Pt memristive device exhibited tunable RS properties with respect to different voltages and compliance currents (CC) without the electroforming process. That is, the device showed voltage-dependent RS at a higher CC whereas CC-dependent RS was observed at lower CC. The device showed four different resistance states during endurance and retention measurements and non-volatile memory results indicated that the CC-based measurement had less variation. Besides, we investigated the basic and complex synaptic plasticity properties using the analog current-voltage characteristics of the Pt/Co3O4/Pt device. In particular, we mimicked the potentiation–depression and four-spike time-dependent plasticity (STDP) rules such as asymmetric Hebbian, asymmetric anti-Hebbian, symmetric Hebbian, and symmetric anti-Hebbian learning rules. The results of the present work indicate that the cobaltite oxide is an excellent nanomaterial for both multilevel RS and neuromorphic computing applications. © 2020
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