Ferroelectric‐Insulator‐Engineered Organic p‐n Heterojunction Optoelectronic Synaptic Transistors for Energy‐Efficient Neuromorphic Computing and Dynamic Machine Vision

ABSTRACT Organic p‐n heterojunctions provide a powerful and broad application for optoelectronics by converting photoexcited charges into mobile carriers via charge transfer. However, the charge‐separation efficiency is frequently constrained by exciton binding and interfacial recombination, which impair the photosynaptic gain of transistors and the resolution of bionic vision hardware. Here, a ferroelectric dielectric is employed as an active electrostatic regulator that establishes a switchable, polarization‐induced built‐in electric field to reshape the heterojunction energetics. This internal field effectively drives exciton dissociation, expedites charge transfer across the interface, and suppresses recombination, thereby substantially amplifying the photoresponse. Leveraging this concept, we present a ferroelectric‐insulator‐modulated organic p‐n heterojunction synaptic transistor (OHST) featuring enhanced and tunable optoelectronic synaptic behavior for low‐power neuromorphic sensing‐computing and the field of dynamic machine vision. Furthermore, the built‐in electric field generated by ferroelectric polarization effectively reduces the working voltages (| V g | ≤ 5 V) and energy consumption (the single‐pulse energy consumption is only 22 aJ). Benefiting from its excellent photosensitivity ( p = 3.19 × 10 4 ), the paired‐pulse facilitation index reaches as high as 247%, making it highly competitive among similar devices. This device demonstrates significant potential for enabling wide‐spectrum visual perception, neuromorphic computing, and advancing next‐generation artificial neuromorphic vision systems.

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Publication Details

Journal
Advanced Functional Materials
Published
2026-06-16
DOI
https://doi.org/10.1002/adfm.76541
Primary Topic
Advanced Memory and Neural Computing
Type
article
Field-Weighted Citation Impact
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Ferroelectric‐Insulator‐Engineered Organic p‐n Heterojunction Optoelectronic Synaptic Transistors for Energy‐Efficient Neuromorphic Computing and Dynamic Machine Vision

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Advanced Functional Materials
Advanced Memory and Neural Computing
article

Ferroelectric‐Insulator‐Engineered Organic p‐n Heterojunction Optoelectronic Synaptic Transistors for Energy‐Efficient Neuromorphic Computing and Dynamic Machine Vision

X G Wang, Yu Zhao, Laju Bu, Jingpeng Wu, Yumin Ren, S. Li, Guanghao Lu, D H Zhang, Zechen Liang, Xi Zhang, Zirui Wang, Bingyu Han, Xian Tang, Bohao Song, Pengyu Du
article en

Abstract

ABSTRACT Organic p‐n heterojunctions provide a powerful and broad application for optoelectronics by converting photoexcited charges into mobile carriers via charge transfer. However, the charge‐separation efficiency is frequently constrained by exciton binding and interfacial recombination, which impair the photosynaptic gain of transistors and the resolution of bionic vision hardware. Here, a ferroelectric dielectric is employed as an active electrostatic regulator that establishes a switchable, polarization‐induced built‐in electric field to reshape the heterojunction energetics. This internal field effectively drives exciton dissociation, expedites charge transfer across the interface, and suppresses recombination, thereby substantially amplifying the photoresponse. Leveraging this concept, we present a ferroelectric‐insulator‐modulated organic p‐n heterojunction synaptic transistor (OHST) featuring enhanced and tunable optoelectronic synaptic behavior for low‐power neuromorphic sensing‐computing and the field of dynamic machine vision. Furthermore, the built‐in electric field generated by ferroelectric polarization effectively reduces the working voltages (| V g | ≤ 5 V) and energy consumption (the single‐pulse energy consumption is only 22 aJ). Benefiting from its excellent photosensitivity ( p = 3.19 × 10 4 ), the paired‐pulse facilitation index reaches as high as 247%, making it highly competitive among similar devices. This device demonstrates significant potential for enabling wide‐spectrum visual perception, neuromorphic computing, and advancing next‐generation artificial neuromorphic vision systems.

Advanced Functional Materials
Zhengzhou University (CN), Xi'an Jiaotong University (CN)
National Natural Science Foundation of China, China Postdoctoral Science Foundation
Affordable and clean energy
Openalex Percentile: Top 11%
Advanced Memory and Neural Computing
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