Ferroionic‐Assisted, Charge‐Driven Polymorphic Phase Engineering for Multilevel Memristive Devices

ABSTRACT Memristive device operation can arise from diverse microscopic mechanisms, including charge polarization and trapping, magnetic ordering, ionic migration, and structural phase transitions. Among these, crystalline–crystalline phase transitions in polymorphic materials offer a unique platform for nonvolatile resistance modulation but are often hindered by large energy barriers and limited endurance. Here, we demonstrate a multimodal, charge‐driven phase modulation in an atomically thin polymorphic material, Mo 0.92 W 0.08 Te 2 , enabled by electrostatic coupling to a ferroionic substrate, CuInP 2 S 6 . The polymorphic channel exhibits reversible switching between semiconducting and metallic phases via electrostatically accumulated charges, reflecting the intrinsically low energy barrier between its competing polymorphs. Integration with the ferroionic substrate enables spatially localized, nonvolatile charge accumulation in the channel, which provides distinct, programmable phase‐control pathways under a two‐terminal bias configuration. The phase‐induced resistance modulation through field‐driven ionic processes is governed by the evolution of a phase‐dependent Schottky junction at the electrode–channel interface, yielding multiple, stable conductance states under pulsed electrical excitation. Leveraging these experimentally derived synaptic characteristics, we conducted an MNIST inference simulation with a convolutional neural network, achieving 98.45% accuracy. This ferroionic‐assisted phase‐engineering strategy offers new opportunities beyond traditional phase‐change devices for adaptive electronic and neuromorphic material platforms.

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Publication Details

Journal
Advanced Materials
Published
2026-11-06
DOI
https://doi.org/10.1002/adma.75648
Primary Topic
Advanced Memory and Neural Computing
Type
article
Field-Weighted Citation Impact
0.00

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article

Ferroionic‐Assisted, Charge‐Driven Polymorphic Phase Engineering for Multilevel Memristive Devices

Heejun Yang, Nayeon Kim, Min Seok Jang, Zhongrui Wang et al.
4 links
Advanced Materials
Advanced Memory and Neural Computing
article

Ferroionic‐Assisted, Charge‐Driven Polymorphic Phase Engineering for Multilevel Memristive Devices

Heejun Yang, Nayeon Kim, Min Seok Jang, Zhongrui Wang, Heemyoung Hong, Woohyun Cho, Daeho Noh, Eunji Hwang, Chengqi Wei, Yongjoon Lee
article en

Abstract

ABSTRACT Memristive device operation can arise from diverse microscopic mechanisms, including charge polarization and trapping, magnetic ordering, ionic migration, and structural phase transitions. Among these, crystalline–crystalline phase transitions in polymorphic materials offer a unique platform for nonvolatile resistance modulation but are often hindered by large energy barriers and limited endurance. Here, we demonstrate a multimodal, charge‐driven phase modulation in an atomically thin polymorphic material, Mo 0.92 W 0.08 Te 2 , enabled by electrostatic coupling to a ferroionic substrate, CuInP 2 S 6 . The polymorphic channel exhibits reversible switching between semiconducting and metallic phases via electrostatically accumulated charges, reflecting the intrinsically low energy barrier between its competing polymorphs. Integration with the ferroionic substrate enables spatially localized, nonvolatile charge accumulation in the channel, which provides distinct, programmable phase‐control pathways under a two‐terminal bias configuration. The phase‐induced resistance modulation through field‐driven ionic processes is governed by the evolution of a phase‐dependent Schottky junction at the electrode–channel interface, yielding multiple, stable conductance states under pulsed electrical excitation. Leveraging these experimentally derived synaptic characteristics, we conducted an MNIST inference simulation with a convolutional neural network, achieving 98.45% accuracy. This ferroionic‐assisted phase‐engineering strategy offers new opportunities beyond traditional phase‐change devices for adaptive electronic and neuromorphic material platforms.

Advanced Materials
Korea Advanced Institute of Science and Technology (KR), Southern University of Science and Technology (CN)
National Research Foundation of Korea, Ministry of Science and ICT, South Korea, Samsung
Openalex Percentile: Top 26%
Advanced Memory and Neural Computing
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