Deep learning-enabled ultra-broadband terahertz high-dimensional photodetector

Zong-Kun Zhang, Teng Zhang, Zong-Peng Zhang, Ming-Zhe Chong, Mingqing Xiao, Pu Peng, Peijie Feng, Haonan Sun, Zhipeng Zheng, Xiaofei Zang (), Zheyu Fang () and Ming-Yao Xia ()
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Zong-Kun Zhang: Peking University
Teng Zhang: University of Shanghai for Science and Technology
Zong-Peng Zhang: Peking University
Ming-Zhe Chong: Peking University
Mingqing Xiao: Peking University
Pu Peng: Peking University
Peijie Feng: Peking University
Haonan Sun: Peking University
Zhipeng Zheng: Peking University
Xiaofei Zang: University of Shanghai for Science and Technology
Zheyu Fang: Peking University
Ming-Yao Xia: Peking University

Nature Communications, 2025, vol. 16, issue 1, 1-11

Abstract: Abstract Capturing multi-dimensional optical information is indispensable in modern optics. However, existing photodetectors can at best detect light fields whose wavelengths or polarizations are predefined at several specific values. Integrating broadband high-dimensional continuous photodetection including intensity, polarization, and wavelength within a single device still poses formidable challenges. Here we present a metasurface-mediated high-dimensional detector that projects polarimetric and spectral responses into the Orbital Angular Momentum (OAM) domain via dispersion-driven OAM multiplication. By decoupling the frequency-controlled transmission phase response and polarization-controlled geometric phase response, spectrum and polarization information are encoded into unique polaritonic vortex patterns, which can be accurately deciphered via machine learning technique. Eventually our neural-network assisted metadevice achieves full characterization of intensity-polarization-frequency 3D continuous parametric space, so that light with arbitrarily mixed polarization states across 0.3-1.1 THz can be accurately detected with total error

Date: 2025
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DOI: 10.1038/s41467-025-63364-8

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