Xiao, M 
ORCID: 0000-0003-2018-4807, Hellenbrand, M, Strkalj, N, Bakhit, B, Sun, Z, Barmpatsalos, N, Joksas, D, Dou, H, Hu, Z, Lu, P et al (show 8 more authors)
(2025)
Ultra-Fast Non-Volatile Resistive Switching Devices with Over 512 Distinct and Stable Levels for Memory and Neuromorphic Computing
Advanced Functional Materials, 35 (29).
ISSN 1616-301X, 1616-3028
Abstract
Low-current multilevel programmability with inherent non-volatility and high stability of resistance states is required for both multi-bit memory storage and deep learning accelerators but is difficult to achieve. Here, in a resistive switching system, this work realizes >512 (>9 bits) distinct non-volatile conductance levels with stable retention for each state with current levels down to the nanoampere range, highly promising for potential integration with small processing nodes with ultra-low power consumption requirements. This is achieved by demonstrating a new thin film design concept that encompasses three key features: an ultra-thin epitaxial oxygen ionic switching layer that provides a tunable energy barrier at the bottom electrode, an overcoat amorphous layer that acts as an ion migration barrier for stable state retention, and a partial conductive filament as a localized electronic transport channel to the epitaxial switching layer. A large dynamic resistance range of up to seven orders of magnitude is achieved with reset-free transitions among intermediate states, and programmability is demonstrated with ultra-fast (20 ns) pulses. Artificial neural network (ANN) simulations, based on the experimental performance and its non-idealities, demonstrate close-to-ideal inference accuracies for various Modified National Institute of Standards and Technology (MNIST) data sets.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | 40 Engineering, 4016 Materials Engineering, 51 Physical Sciences, Machine Learning and Artificial Intelligence, 7 Affordable and Clean Energy |
| Divisions: | Faculty of Science & Engineering Faculty of Science & Engineering > School of Physical Sciences Faculty of Science & Engineering > School of Physical Sciences > Physics |
| Depositing User: | Symplectic Admin |
| Date Deposited: | 09 Dec 2025 10:25 |
| Last Modified: | 22 Jan 2026 16:10 |
| DOI: | 10.1002/adfm.202418980 |
| Open Access URL: | https://www.nature.com/nature-index/article/10.100... |
| Related Websites: | |
| URI: | https://livrepository.liverpool.ac.uk/id/eprint/3196027 |
| Disclaimer: | The University of Liverpool is not responsible for content contained on other websites from links within repository metadata. Please contact us if you notice anything that appears incorrect or inappropriate. |
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