Facile synthesis of nickel cobaltite quasi-hexagonal nanosheets for multilevel resistive switching and synaptic learning applications

Tukaram D. Dongale, Atul C. Khot, Ashkan Vakilipour Takaloo, Tae Geun Kim

Research output: Contribution to journalArticlepeer-review

47 Citations (Scopus)

Abstract

High-density memory devices are essential to sustain growth in information technology (IT). Furthermore, brain-inspired computing devices are the future of IT businesses such as artificial intelligence, deep learning, and big data. Herein, we propose a facile and hierarchical nickel cobaltite (NCO) quasi-hexagonal nanosheet-based memristive device for multilevel resistive switching (RS) and synaptic learning applications. Electrical measurements of the Pt/NCO/Pt device show the electroforming free pinched hysteresis loops at different voltages, suggesting the multilevel RS capability of the device. The detailed memristive properties of the device were calculated using the time-dependent current–voltage data. The two-valued charge-flux properties indicate the memristive and multilevel RS characteristics of the device. Interestingly, the Pt/NCO/Pt memristive device shows a compliance current (CC)-dependent RS property; compliance-free RS was observed from 10−2 to 10−4 A, and the compliance effect dominated in the range of 10−5–10−6 A. In CC control mode, the device demonstrated three resistance states during endurance and retention measurements. In addition, the device was successful in mimicking biological synaptic properties such as potentiation-depression- and spike-timing-dependent plasticity rules. The results of the present investigation demonstrated that solution-processable NCO nanosheets are potential switching materials for high-density memory and brain-inspired computing applications.

Original languageEnglish
Article number16
JournalNPG Asia Materials
Volume13
Issue number1
DOIs
Publication statusPublished - 2021 Feb

Bibliographical note

Publisher Copyright:
© 2021, The Author(s).

ASJC Scopus subject areas

  • Modelling and Simulation
  • General Materials Science
  • Condensed Matter Physics

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