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Cited 10 time in webofscience Cited 10 time in scopus
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Uniform multilevel switching and synaptic properties in RF-sputtered InGaZnO-based memristor treated with oxygen plasmaopen access

Authors
Mahata, ChandreswarSo, HyojinYang, SeyeongIsmail, MuhammadKim, SungjunCho, Seongjae
Issue Date
Nov-2023
Publisher
AIP Publishing
Keywords
Oxygen; Gallium Compounds; Memristors; Oxygen; Oxygen Vacancies; Redox Reactions; A-stable; Data Endurance; Memristor; Multilevel Memory State; Multilevels; Oxygen Plasmas; Property; Resistive Switching; Resistive Switching Devices; Vacancy Formation; Energy Utilization; Oxygen; Animal Tissue; Article; Bipolar Disorder; Depression; Electric Potential; Electric Pulse; Endurance; Energy Consumption; Learning; Long Term Potentiation; Memory; Memristor; Oxidation Reduction Reaction; Plasma; Synapse
Citation
The Journal of Chemical Physics, v.159, no.18, pp 1 - 9
Pages
9
Indexed
SCIE
SCOPUS
Journal Title
The Journal of Chemical Physics
Volume
159
Number
18
Start Page
1
End Page
9
URI
https://scholarworks.dongguk.edu/handle/sw.dongguk/19795
DOI
10.1063/5.0179314
ISSN
0021-9606
1089-7690
Abstract
Bipolar gradual resistive switching was investigated in ITO/InGaZnO/ITO resistive switching devices. Controlled intrinsic oxygen vacancy formation inside the switching layer enabled the establishment of a stable multilevel memory state, allowing for RESET voltage control and non-degradable data endurance. The ITO/InGaZnO interface governs the migration of oxygen ions and redox reactions within the switching layer. Voltage-stress-induced electron trapping and oxygen vacancy formation were observed before conductive filament electroforming. This device mimicked biological synapses, demonstrating short- and long-term potentiation and depression through electrical pulse sequences. Modulation of post-synaptic currents and pulse frequency-dependent short-term potentiation were successfully emulated in the InGaZnO-based artificial synapse. The ITO/InGaZnO/ITO memristor exhibited spike-amplitude-dependent plasticity, spike-rate-dependent plasticity, and potentiation-depression synaptic learning with low energy consumption, making it a promising candidate for large-scale integration. © 2023 Author(s).
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