神经形态工程学
人工智能
卷积神经网络
计算机视觉
预处理器
计算机科学
材料科学
机器视觉
图像传感器
夜视
人工神经网络
图像(数学)
图像处理
亮度
月光
闪烁
模式识别(心理学)
视觉处理
机器人学
光电探测器
作者
Zhaoying Xi,Yu Liu,Sihan Yan,Jia‐Han Zhang,Min Li,Kai Tang,Cheng Wu,Z. Wu,Shan Li,Xueqiang Ji,Shaohui Zhang,Daoyou Guo,Yong Li,Yingtang Zhou,Mingming Jiang,Zhilai Fang,Weihua Tang,Zeng Liu
标识
DOI:10.1002/adfm.202528620
摘要
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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