Owl‐Inspired Neuromorphic Vision Sensor with Highly‐Sensitive Ultraweak Deep‐Ultraviolet Light Detection in Dim Environments

ABSTRACT Neuromorphic vision sensors, which address the shortcomings of traditional artificial vision sensors such as large size and low efficiency, have been widely studied recently. However, the images suffer from low contrast and loss of details when they are applied in dim environments. Inspired by the owl's dual strategy for night vision, a neuromorphic vision sensor for weak‐light applications based on Sn‐doped Ga 2 O 3 polycrystalline thin film was designed and reported here. This sensor demonstrates exceptional UVC detection by using its engineered grain boundaries to achieve both an ultra‐low dark current (88 fA) and high photo‐to‐dark current ratio (7.73 × 10 5 ) under simulated moonlight illumination. The device shows neuromorphic synaptic behaviors, underexposed image retention exceeding 500 s, and long‐term (18 months) stability. Furthermore, the sensor's unique non‐linear photoresponse inherently performs hardware‐level gamma correction, which is leveraged by a new preprocessing system to significantly enhance image contrast, and improving recognition accuracy of underexposed images from 65.20% to 83.44% when integrated with a convolutional neural network. Mimicking the specialized visual adaptations of nocturnal predators, this holistic biomimetic design provides a stabilized device, with low economic cost, and mass production potential, paving the pathway for applications in challenging weak‐light settings.

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Publication Details

Journal
Advanced Functional Materials
Published
2026-01-12
DOI
https://doi.org/10.1002/adfm.202528620
Citations
1
Primary Topic
Advanced Memory and Neural Computing
Type
article
Field-Weighted Citation Impact
21.93
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article

Owl‐Inspired Neuromorphic Vision Sensor with Highly‐Sensitive Ultraweak Deep‐Ultraviolet Light Detection in Dim Environments

Xueqiang Ji, Z. Wu, Cheng Wu, Shaohui Zhang et al.
1 citations
Advanced Functional Materials
Advanced Memory and Neural Computing
21.93
article

Owl‐Inspired Neuromorphic Vision Sensor with Highly‐Sensitive Ultraweak Deep‐Ultraviolet Light Detection in Dim Environments

Xueqiang Ji, Z. Wu, Cheng Wu, Shaohui Zhang, Sihan Yan, Zhilai Fang, Daoyou Guo, Zeng Liu, Jia‐Han Zhang, Yu Liu, Yong Li, Shan Li, Yingtang Zhou, Min Li, Zhaoying Xi, Mingming Jiang, Weihua Tang
article en
1 citations

Abstract

ABSTRACT Neuromorphic vision sensors, which address the shortcomings of traditional artificial vision sensors such as large size and low efficiency, have been widely studied recently. However, the images suffer from low contrast and loss of details when they are applied in dim environments. Inspired by the owl's dual strategy for night vision, a neuromorphic vision sensor for weak‐light applications based on Sn‐doped Ga 2 O 3 polycrystalline thin film was designed and reported here. This sensor demonstrates exceptional UVC detection by using its engineered grain boundaries to achieve both an ultra‐low dark current (88 fA) and high photo‐to‐dark current ratio (7.73 × 10 5 ) under simulated moonlight illumination. The device shows neuromorphic synaptic behaviors, underexposed image retention exceeding 500 s, and long‐term (18 months) stability. Furthermore, the sensor's unique non‐linear photoresponse inherently performs hardware‐level gamma correction, which is leveraged by a new preprocessing system to significantly enhance image contrast, and improving recognition accuracy of underexposed images from 65.20% to 83.44% when integrated with a convolutional neural network. Mimicking the specialized visual adaptations of nocturnal predators, this holistic biomimetic design provides a stabilized device, with low economic cost, and mass production potential, paving the pathway for applications in challenging weak‐light settings.

Advanced Functional Materials
Zhejiang Sci-Tech University (CN), Fudan University (CN), Inner Mongolia University (CN), Inner Mongolia University of Science and Technology (CN), Zhejiang Ocean University (CN), Nanjing University of Posts and Telecommunications (CN), Guangdong Academy of Sciences (CN), Shanghai Academy of Science & Technology (CN), Nanjing University of Aeronautics and Astronautics (CN)
Openalex Percentile: Top 3%
Advanced Memory and Neural Computing
21.93
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