DataVXAdvanced Functional MaterialsPaper
journal article·2025-07Open access

Ultra‐Fast Non‐Volatile Resistive Switching Devices with Over 512 Distinct and Stable Levels for Memory and Neuromorphic Computing

Ming Xiao, Markus Hellenbrand, Nives Strkalj, Babak Bakhit, Zhuotong Sun, Nikolaos Barmpatsalos, Dovydas Joksas, Hongyi Dou, Zedong Hu, Ping Lu, Samip Karki, Sundar Kunwar, Jonathan D. Major, Aiping Chen, Haiyan Wang, Quanxi Jia, Adnan Mehonic, Judith L. MacManus‐Driscoll

Advanced Functional Materials· Volume 35· Issue 29
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Source abstract

Abstract

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.
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Research context

Nanomaterials

OpenAlex topics: Advanced Memory and Neural Computing · Ferroelectric and Negative Capacitance Devices · Electronic and Structural Properties of Oxides

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

Department of Materials Science & Metallurgy University of Cambridge 27 Charles Babbage Road Cambridge CB3 0FS UK

School of Microelectronics Science and Technology Sun Yat‐sen University Zhuhai Guangdong Province 519082 China

Center for Advanced Laser Techniques Institute of Physics Zagreb 10000 Croatia

Electrical Engineering Division Department of Engineering University of Cambridge 9 JJ Thomson Avenue Cambridge CB3 0FA UK

Thin Film Physics Division Department of Physics (IFM) Linköping University Linköping 581 83 Sweden

Department of Electronic and Electrical Engineering University College London Roberts Building, Torrington Place London WC1E 7JE UK

School of Materials Engineering Department of Electrical and Computer Engineering Purdue University West Lafayette IN 47907 USA

Sandia National Laboratories Albuquerque NM 87185 USA

Center for Integrated Nanotechnologies (CINT) Los Alamos National Laboratory Los Alamos New Mexico 87545 USA

Department of Physics/Stephenson Institute for Renewable Energy University of Liverpool Liverpool L69 7ZF UK

Department of Materials Design and Innovation University at Buffalo – the State University of New York Buffalo NY 14260 USA

Funding

Royal Academy of Engineering · European Commission · Engineering and Physical Sciences Research Council

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