Ferroelectric Domain Walls Enable Near-Infrared Visual Synapses in Wide-Bandgap 2D In-Plane Ferroelectric NbOCl <sub>2</sub> -Based Heterojunctions

Ferroelectric domain walls endow ferroelectric materials with sub-bandgap near-infrared (NIR) light absorption properties, holding broad prospects for the development of infrared visual synaptic functions. Their stable behavior in two-dimensional (2D) in-plane ferroelectric materials under external out-of-plane electric fields lays a material foundation for building 2D NIR visual synapses─additionally, the in-plane polarization characteristic further enables optical polarization-sensitive characteristics. Herein, we fabricate a multilayer graphene (Gr)/NbOCl 2 /Gr heterojunction using a wide-bandgap 2D in-plane ferroelectric material and exploit the NIR light absorption properties of ferroelectric domain walls to achieve NIR visual synaptic functions at ultralow voltage. We investigated the heterojunction’s responses to external bias, pulse frequency, and pulse intensity in the 808 nm–2200 nm wavelength range, along with its paired-pulse facilitation/depression (PPF/PPD) and multipulse characteristics. Specifically, the heterojunction can implement synaptic functions at 1 mV, with a single-pulse energy consumption as low as 8.74 fJ, and achieves nearly symmetric PPF/PPD characteristics under positive/negative voltages. Notably, the heterojunction shows excellent optical polarization sensitivity, with a polarization ratio up to 9.12 under 1064 nm light illumination. This work offers key technical and theoretical support for advancing high-performance 2D NIR polarization-sensitive visual synapses and accelerates the application of ferroelectric domain wall materials in next-generation visual neuromorphic computing.

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Publication Details

Journal
ACS Applied Materials & Interfaces
Published
2026-06-26
DOI
https://doi.org/10.1021/acsami.6c04118
Primary Topic
Advanced Memory and Neural Computing
Type
article
Field-Weighted Citation Impact
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article

Ferroelectric Domain Walls Enable Near-Infrared Visual Synapses in Wide-Bandgap 2D In-Plane Ferroelectric NbOCl 2 -Based Heterojunctions

Jun Fang, Pengfei Hou, Xianjun Zhang
ACS Applied Materials & Interfaces
Advanced Memory and Neural Computing
article

Ferroelectric Domain Walls Enable Near-Infrared Visual Synapses in Wide-Bandgap 2D In-Plane Ferroelectric NbOCl 2 -Based Heterojunctions

Jun Fang, Pengfei Hou, Xianjun Zhang
article en

Abstract

Ferroelectric domain walls endow ferroelectric materials with sub-bandgap near-infrared (NIR) light absorption properties, holding broad prospects for the development of infrared visual synaptic functions. Their stable behavior in two-dimensional (2D) in-plane ferroelectric materials under external out-of-plane electric fields lays a material foundation for building 2D NIR visual synapses─additionally, the in-plane polarization characteristic further enables optical polarization-sensitive characteristics. Herein, we fabricate a multilayer graphene (Gr)/NbOCl 2 /Gr heterojunction using a wide-bandgap 2D in-plane ferroelectric material and exploit the NIR light absorption properties of ferroelectric domain walls to achieve NIR visual synaptic functions at ultralow voltage. We investigated the heterojunction’s responses to external bias, pulse frequency, and pulse intensity in the 808 nm–2200 nm wavelength range, along with its paired-pulse facilitation/depression (PPF/PPD) and multipulse characteristics. Specifically, the heterojunction can implement synaptic functions at 1 mV, with a single-pulse energy consumption as low as 8.74 fJ, and achieves nearly symmetric PPF/PPD characteristics under positive/negative voltages. Notably, the heterojunction shows excellent optical polarization sensitivity, with a polarization ratio up to 9.12 under 1064 nm light illumination. This work offers key technical and theoretical support for advancing high-performance 2D NIR polarization-sensitive visual synapses and accelerates the application of ferroelectric domain wall materials in next-generation visual neuromorphic computing.

ACS Applied Materials & Interfaces
Education Department of Hunan Province (CN), Xiangtan University (CN)
National Natural Science Foundation of China, Science and Technology Program of Hunan Province
Affordable and clean energy
Openalex Percentile: Top 10%
Advanced Memory and Neural Computing
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