Uniform multilevel switching and synaptic properties in RF-sputtered InGaZnO-based memristor treated with oxygen plasmaopen access
- Authors
- Mahata, Chandreswar; So, Hyojin; Yang, Seyeong; Ismail, Muhammad; Kim, Sungjun; Cho, 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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- Appears in
Collections - College of Engineering > Department of Electronics and Electrical Engineering > 1. Journal Articles

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