Interfacial AlOx-insertion-driven synaptic enhancement and tunneling control in HfO2-based ferroelectric MIFS memristorsopen access
- Authors
- Chae, Hyojeong; Ko, Minsu; Kim, Sungjun
- Issue Date
- Feb-2026
- Publisher
- Elsevier Ltd
- Keywords
- Aluminum oxide; Ferroelectric; Hafnium; Memristor; Neuromorphic computing; Synaptic device
- Citation
- Journal of Alloys and Compounds, v.1055, pp 1 - 12
- Pages
- 12
- Indexed
- SCIE
SCOPUS
- Journal Title
- Journal of Alloys and Compounds
- Volume
- 1055
- Start Page
- 1
- End Page
- 12
- URI
- https://scholarworks.dongguk.edu/handle/sw.dongguk/63671
- DOI
- 10.1016/j.jallcom.2026.186549
- ISSN
- 0925-8388
1873-4669
- Abstract
- Hafnia-based ferroelectric memristors are promising candidates for next-generation neuromorphic computing owing to their ability to emulate biological synaptic functions with excellent scalability, non-volatility, and CMOS compatibility. In this work, we investigate a metal–insulator–ferroelectric–semiconductor (MIFS) structure and compare it with a conventional metal–ferroelectric–semiconductor (MFS) device. Electrical characterization reveals that the MIFS device exhibits a wider memory window, a high tunneling electro-resistance (TER) ratio of ∼538 %, and superior array scalability (up to 253 × 253, as estimated by array level simulation) enabled by effective suppression of sneak-path currents. Moreover, the MIFS structure successfully reproduces essential synaptic behaviors such as paired-pulse facilitation (PPF), potentiation/depression, and excitatory postsynaptic current (EPSC), demonstrating its suitability for neuromorphic operation. When integrated as a reservoir layer in a reservoir computing (RC) system, the device achieved 97.48 % classification accuracy on the MNIST dataset, validating its potential for hardware-based neuromorphic computing. Additionally, the gradual and symmetric current modulation with stable retention enables controllable analog synaptic functionalities, highlighting the versatility of the enhanced MIFS architecture for next-generation neuromorphic systems. © 2026 Elsevier B.V.
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