Multilevel resistive switching and synaptic plasticity of nanoparticulated cobaltite oxide memristive device

• Published in: Journal of materials science & technology 2021-07, Vol.78, p.81-91
• Main Authors: Dongale, Tukaram D., Khot, Atul C., Takaloo, Ashkan V., Son, Kyung Rock, Kim, Tae Geun
• Format: Article
• Language: English
• Subjects: Cobaltite oxide; Memristive device; Multilevel resistive switching; STDP; Synaptic plasticity
• ISSN: 1005-0302; 1941-1162
• DOI: 10.1016/j.jmst.2020.10.046

•Fabricated nanoparticulated cobaltite oxide memristive device for multilevel resistive switching and synaptic learning.•Device showed forming-free resistive switching and two-valued charge-flux properties.•Demonstrated the compliance current- and RESET voltage-dependent resistive switching memory properties.•Mimicked the potentiation-depression and four complex spike time-dependent plasticity rules. 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.