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无序引导光子芯片实现高维光场探测。

Disordered-guiding photonic chip enabled high-dimensional light field detection.

作者信息

Gu Zhijuan, Zhang Weilun, Yu Yu, Zhang Xinliang

机构信息

Wuhan National Laboratory for Optoelectronics and School of Optical and Electronic Information, Huazhong University of Science and Technology, 430074, Wuhan, China.

Optics Valley Laboratory, 430074, Hubei, China.

出版信息

Nat Commun. 2025 Aug 20;16(1):7741. doi: 10.1038/s41467-025-63130-w.

Abstract

Full characterization of light intensity, polarization, and spectrum is essential for applications in sensing, communication and imaging. However, existing schemes rely on discrete, bulky components to capture polarization and spectrum separately, and suffer from detecting only a few values in each dimension. Here, we implement a compact disordered-guiding photonic chip with a neural network for single-shot high-dimensional light field detection. The disordered region introduces complex interference and scattering among polarized components, while the guiding region efficiently collects the outputs to on-chip photodetectors. This design encodes high-dimensional input into multi-channel intensities with high sensitivity, subsequently decoded by the neural network. Experimentally, the accurate detection of broad spectrum with mixed full-Stokes polarization states is realized with a polarization error of 1.2° and spectral resolution as high as 400 pm. Furthermore, the device demonstrates high-dimensional imaging with superior recognition performance over single-dimensional methods. This innovation offers a compact and high-resolution solution for high-dimensional detection.

摘要

对光强、偏振和光谱进行全面表征对于传感、通信和成像应用至关重要。然而,现有方案依靠离散、笨重的组件分别捕获偏振和光谱,并且在每个维度上只能检测少数几个值。在此,我们实现了一种带有神经网络的紧凑型无序引导光子芯片,用于单次高维光场检测。无序区域在偏振分量之间引入复杂的干涉和散射,而引导区域则有效地将输出收集到片上光电探测器。这种设计将高维输入编码为具有高灵敏度的多通道强度,随后由神经网络进行解码。实验上,实现了对具有混合全斯托克斯偏振态的广谱的精确检测,偏振误差为1.2°,光谱分辨率高达400 pm。此外,该器件展示了高维成像,其识别性能优于单维方法。这一创新为高维检测提供了一种紧凑且高分辨率的解决方案。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4894/12367990/8e938c8b475c/41467_2025_63130_Fig1_HTML.jpg

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