BEOL‐Compatible Liquid‐Metal‐Printing of Ultrathin 2D Oxide Memtransistors and Its Applications in Neuromorphic Computing

ABSTRACT 2D ultrathin oxides derived from liquid metals represent a unique class of 2D materials, offering low‐temperature, scalable, and ambient‐processable alternatives to conventional synthesis methods. Here, 2D ultrathin indium oxide (InO X ) memtransistors are fabricated via a reproducible liquid‐metal‐printing process at 200°C in ambient air, combining touch printing and blade coating. The resulting InO X films exhibit nanoscale thickness (∼4 nm), a wide optical bandgap (∼3.7 eV), and a polycrystalline, oxygen‐deficient structure. Their intrinsically high conductivity is converted into stable semiconducting behavior through dry‐air annealing. The fabricated memtransistors display robust, gate‐tunable bipolar memristive switching with excellent endurance over 500 cycles and strong device‐to‐device uniformity. Notably, they achieve high switching ratios up to 10 3.63 , outperforming most previously reported 2D memtransistors synthesized under high‐temperature or vacuum conditions. The memristive behavior is governed by space‐charge‐limited conduction extending to the trap‐filled limit, driven by carrier trapping/detrapping within exponentially distributed trap states. Moreover, the devices emulate synaptic plasticity and neuromodulation, achieving recognition accuracies up to 88.3% in artificial neural network simulations for handwritten image recognition. These results establish liquid‐metal‐printed ultrathin InO X as a promising, scalable platform for next‐generation 2D neuromorphic and memory device technologies.

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Publication Details

Journal
Advanced Functional Materials
Published
2026-02-16
DOI
https://doi.org/10.1002/adfm.202531701
Primary Topic
Advanced Memory and Neural Computing
Type
article
Field-Weighted Citation Impact
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article

BEOL‐Compatible Liquid‐Metal‐Printing of Ultrathin 2D Oxide Memtransistors and Its Applications in Neuromorphic Computing

Seungyun Lee, Jehwan Park, SungHan Cho, Dongjin Lee et al.
Advanced Functional Materials
Advanced Memory and Neural Computing
article

BEOL‐Compatible Liquid‐Metal‐Printing of Ultrathin 2D Oxide Memtransistors and Its Applications in Neuromorphic Computing

Seungyun Lee, Jehwan Park, SungHan Cho, Dongjin Lee, Jihyun Kim, Seongyun Yang, Sanghyun Moon
article en

Abstract

ABSTRACT 2D ultrathin oxides derived from liquid metals represent a unique class of 2D materials, offering low‐temperature, scalable, and ambient‐processable alternatives to conventional synthesis methods. Here, 2D ultrathin indium oxide (InO X ) memtransistors are fabricated via a reproducible liquid‐metal‐printing process at 200°C in ambient air, combining touch printing and blade coating. The resulting InO X films exhibit nanoscale thickness (∼4 nm), a wide optical bandgap (∼3.7 eV), and a polycrystalline, oxygen‐deficient structure. Their intrinsically high conductivity is converted into stable semiconducting behavior through dry‐air annealing. The fabricated memtransistors display robust, gate‐tunable bipolar memristive switching with excellent endurance over 500 cycles and strong device‐to‐device uniformity. Notably, they achieve high switching ratios up to 10 3.63 , outperforming most previously reported 2D memtransistors synthesized under high‐temperature or vacuum conditions. The memristive behavior is governed by space‐charge‐limited conduction extending to the trap‐filled limit, driven by carrier trapping/detrapping within exponentially distributed trap states. Moreover, the devices emulate synaptic plasticity and neuromodulation, achieving recognition accuracies up to 88.3% in artificial neural network simulations for handwritten image recognition. These results establish liquid‐metal‐printed ultrathin InO X as a promising, scalable platform for next‐generation 2D neuromorphic and memory device technologies.

Advanced Functional Materials
Seoul National University (KR), University of Illinois Urbana-Champaign (US), Samsung (South Korea) (KR)
Openalex Percentile: Top 54%
Advanced Memory and Neural Computing
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