4.7 Article

Highly-stable memristive devices with synaptic characteristics based on hydrothermally synthesized MnO2 active layers

Journal

JOURNAL OF ALLOYS AND COMPOUNDS
Volume 872, Issue -, Pages -

Publisher

ELSEVIER SCIENCE SA
DOI: 10.1016/j.jallcom.2021.159653

Keywords

Memristive devices; Resistive switching; Synaptic plasticity; Hydrothermal method; MnO2

Funding

  1. Basic Science Research Program through the National Research Foundation of Korea (NRF) - Ministry of Education, Science, and Technology [2019R1A2B5B03069968]
  2. MOTIE (Ministry of Trade, Industry Energy) [10080581]
  3. KSRC (Korea Semiconductor Research Consortium) support program for the development of the future semiconductor device

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This study focused on developing manganese oxide thin films and investigating their resistive switching properties and bio-synaptic characteristics. The results showed that the developed devices had a long resistive switching lifetime and could be valuable for neuromorphic computing applications.
Mimicking synaptic plasticity is a key to harnessing the power of the brain. In the present work, manganese oxide (MnO2) thin films were developed by using the simple, low-cost hydrothermal method, and the hydrothermal deposition-time-dependent resistive switching property of MnO2 thin films was investigated. The current-voltage and the charge-magnetic flux characteristics suggested that the developed devices could be placed into the category of memristive devices. The bio-synaptic properties, such as synaptic weight, potentiation depression, and symmetric Hebbian learning of these devices were demonstrated. The developed devices were able to switch between two distinctly separable resistance states and to retain the resistive switching states for 10(3) s without any significant degradation. In addition, their non-zero current-voltage crossing property suggests that parasitic meminductance coexists with memristive behavior. For these devices, Schottky conduction mechanisms were found to be responsible for the resistive switching effect. The results of the present investigation should be very useful for neuromorphic computing applications. (C) 2021 Published by Elsevier B.V.

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