A Single Volatile VO 2 Memristor Enabling Neuromorphic Perception, Learning, and Computing

ABSTRACT Neuromorphic computing requires compact hardware devices capable of supporting multiple brain‐inspired functions. Vanadium dioxide (VO 2 ), with its thermally and electrically driven insulator–metal transition (IMT), offers an attractive materials platform for such applications owing to its intrinsic volatility and fast switching dynamics. Here, we demonstrate a vertical Pt/VO 2 /Pt memristor that integrates multiple neuromorphic functions within a single volatile device platform. VO 2 thin films are deposited by atomic layer deposition (ALD) using the TEMAV precursor, yielding semiconducting insulating and metallic phases with a resistivity contrast of approximately two orders of magnitude. Electrical transport analysis indicates that back‐to‐back Schottky barriers at the Pt/VO 2 interfaces play a dominant role in device conduction and enable reproducible volatile threshold switching. Leveraging the interfacial transport and thermally driven switching dynamics, the same device emulates leakyintegrate‐and‐fire neuronal behavior, Pavlovian‐like associative conditioning, reservoir‐computing‐based temporal signal recognition, and artificial nociceptor responses. In particular, the device‐driven reservoir system is applied to electrocardiogram heartbeat classification, demonstrating its capability for processing real physiological temporal signals. These results establish ALD‐grown vertical VO 2 memristors as compact and multifunctional building blocks for neuromorphic and sensory hardware, highlighting a route toward single‐device platforms that couple perception, learning, and temporal information processing.

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Publication Details

Journal
Advanced Electronic Materials
Published
2026-10-29
DOI
https://doi.org/10.1002/aelm.70628
Primary Topic
Advanced Memory and Neural Computing
Type
article
Field-Weighted Citation Impact
0.00

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article

A Single Volatile VO 2 Memristor Enabling Neuromorphic Perception, Learning, and Computing

Cornelis Hendrik de Groot, Ruomeng Huang, Richard Beanland, Yisong Han et al.
6 links
Advanced Electronic Materials
Advanced Memory and Neural Computing
article

A Single Volatile VO 2 Memristor Enabling Neuromorphic Perception, Learning, and Computing

Cornelis Hendrik de Groot, Ruomeng Huang, Richard Beanland, Yisong Han, Omesh R. Kapur, Kai Sun, Bingkai Ding, Bohao Ding
article en

Abstract

ABSTRACT Neuromorphic computing requires compact hardware devices capable of supporting multiple brain‐inspired functions. Vanadium dioxide (VO 2 ), with its thermally and electrically driven insulator–metal transition (IMT), offers an attractive materials platform for such applications owing to its intrinsic volatility and fast switching dynamics. Here, we demonstrate a vertical Pt/VO 2 /Pt memristor that integrates multiple neuromorphic functions within a single volatile device platform. VO 2 thin films are deposited by atomic layer deposition (ALD) using the TEMAV precursor, yielding semiconducting insulating and metallic phases with a resistivity contrast of approximately two orders of magnitude. Electrical transport analysis indicates that back‐to‐back Schottky barriers at the Pt/VO 2 interfaces play a dominant role in device conduction and enable reproducible volatile threshold switching. Leveraging the interfacial transport and thermally driven switching dynamics, the same device emulates leakyintegrate‐and‐fire neuronal behavior, Pavlovian‐like associative conditioning, reservoir‐computing‐based temporal signal recognition, and artificial nociceptor responses. In particular, the device‐driven reservoir system is applied to electrocardiogram heartbeat classification, demonstrating its capability for processing real physiological temporal signals. These results establish ALD‐grown vertical VO 2 memristors as compact and multifunctional building blocks for neuromorphic and sensory hardware, highlighting a route toward single‐device platforms that couple perception, learning, and temporal information processing.

Advanced Electronic Materials
University of Warwick (GB), University of Southampton (GB)
University of Southampton, Engineering and Physical Sciences Research Council
Openalex Percentile: Top 24%
Advanced Memory and Neural Computing
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