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Polarization-controlled memristive synapse characteristics of HfZrO2-based ferroelectric switchable diodeopen access

Authors
Lee, YoungminLee, Sejoon
Issue Date
Aug-2025
Publisher
Elsevier Ltd
Keywords
Ferroelectric switchable diode; HfZrO<sub>2</sub>; Schottky barrier modulation; Synaptic device
Citation
Journal of Alloys and Compounds, v.1038, pp 1 - 14
Pages
14
Indexed
SCIE
SCOPUS
Journal Title
Journal of Alloys and Compounds
Volume
1038
Start Page
1
End Page
14
URI
https://scholarworks.dongguk.edu/handle/sw.dongguk/58972
DOI
10.1016/j.jallcom.2025.182700
ISSN
0925-8388
1873-4669
Abstract
To realize artificial synapse functionalities using precisely controllable resistance-switching characteristics in electronic synaptic devices, we demonstrated diverse and efficient synaptic functions on a two-terminal device architecture of the Au/HfZrO2/Pt ferroelectric switchable diode. The ferroelectric properties of the HfZrO2 active layer were enhanced by forming a crystallographic orthorhombic phase, which was associated with an increased oxygen vacancy density. The fabricated device exhibited distinct asymmetric hysteresis behavior, attributed to the switchable diode effect resulting from ferroelectric polarization-induced modulation of the Schottky barrier height. This polarization-mediated barrier modulation enabled systematic tuning of the on-state current values by varying the sweep time duration. These finely tunable resistive-switching characteristics allowed the fabricated device to effectively emulate biological synaptic functions. Controlled time intervals and pulse durations in repetitive pulse schemes provided a straightforward method to improve both the linearity and symmetry of long-term memory characteristics, thereby enhancing learning accuracy and training efficiency. Furthermore, this approach facilitated metaplasticity in spike-timing-dependent plasticity, corresponding to the learning activity of the electronic synapse. These findings underscore the significant potential of the Au/HfZrO2/Pt ferroelectric switchable diode for applications in neuromorphic computing systems. © 2025 Elsevier B.V.
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College of Advanced Convergence Engineering (Division of System Semiconductor)
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