Efficient Carbon‐Based Optoelectronic Synapses for Dynamic Visual Recognition

The human visual nervous system excels at recognizing and processing external stimuli, essential for various physiological functions. Biomimetic visual systems leverage biological synapse properties to improve memory encoding and perception. Optoelectronic devices mimicking these synapses can enhance wearable electronics, with layered heterojunction materials being ideal materials for optoelectronic synapses due to their tunable properties and biocompatibility. However, conventional synthesis methods are complex and environmentally harmful, leading to issues such as poor stability and low charge transfer efficiency. Therefore, it is imperative to develop a more efficient, convenient, and eco-friendly method for preparing layered heterojunction materials. Here, a one-step ultrasonic method is employed to mix fullerene (C60) with graphene oxide (GO), yielding a homogeneous layered heterojunction composite film via self-assembly. The biomimetic optoelectronic synapse based on this film achieves 97.3% accuracy in dynamic visual recognition tasks and exhibits capabilities such as synaptic plasticity. Experiments utilizing X-ray photoelectron spectroscopy (XPS), X-ray diffraction spectroscopy (XRD), Fourier-transform infrared spectroscopy (FTIR), ultraviolet-visible spectroscopy (UV-vis), scanning electron microscopy (SEM), and transmission electron microscopy (TEM) confirms stable π-π interactions between GO and C60, facilitating electron transfer and prolonging carrier recombination times. The novel approach leveraging high-density π electron materials advances artificial intelligence and neuromorphic systems.

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Institutions

Publication Details

Journal
Advanced Science
Published
2025-01-22
DOI
https://doi.org/10.1002/advs.202414319
Citations
11
Primary Topic
Advanced Memory and Neural Computing
Type
article
Field-Weighted Citation Impact
8.03

Funders

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article

Efficient Carbon‐Based Optoelectronic Synapses for Dynamic Visual Recognition

Jihong Wang, Haiping Fang, Zhen Gu, Jiahao Guo et al.
11 citations
Advanced Science
Advanced Memory and Neural Computing
8.03
article

Efficient Carbon‐Based Optoelectronic Synapses for Dynamic Visual Recognition

Jihong Wang, Haiping Fang, Zhen Gu, Jiahao Guo, Lin Wang, Huifeng Wang, Wenhao Liu
article en
11 citations

Abstract

The human visual nervous system excels at recognizing and processing external stimuli, essential for various physiological functions. Biomimetic visual systems leverage biological synapse properties to improve memory encoding and perception. Optoelectronic devices mimicking these synapses can enhance wearable electronics, with layered heterojunction materials being ideal materials for optoelectronic synapses due to their tunable properties and biocompatibility. However, conventional synthesis methods are complex and environmentally harmful, leading to issues such as poor stability and low charge transfer efficiency. Therefore, it is imperative to develop a more efficient, convenient, and eco-friendly method for preparing layered heterojunction materials. Here, a one-step ultrasonic method is employed to mix fullerene (C60) with graphene oxide (GO), yielding a homogeneous layered heterojunction composite film via self-assembly. The biomimetic optoelectronic synapse based on this film achieves 97.3% accuracy in dynamic visual recognition tasks and exhibits capabilities such as synaptic plasticity. Experiments utilizing X-ray photoelectron spectroscopy (XPS), X-ray diffraction spectroscopy (XRD), Fourier-transform infrared spectroscopy (FTIR), ultraviolet-visible spectroscopy (UV-vis), scanning electron microscopy (SEM), and transmission electron microscopy (TEM) confirms stable π-π interactions between GO and C60, facilitating electron transfer and prolonging carrier recombination times. The novel approach leveraging high-density π electron materials advances artificial intelligence and neuromorphic systems.

Advanced ScienceVol. 12(11)
East China University of Science and Technology (CN), Zhejiang Lab (CN)
National Natural Science Foundation of China, Science and Technology Commission of Shanghai Municipality, National Key Research and Development Program of China, Science and Technology Innovation Plan Of Shanghai Science and Technology Commission
Responsible consumption and production
Openalex Percentile: Top 3%
Advanced Memory and Neural Computing
8.03
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