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

作者：Zongkun Zhang, Teng Zhang, Zong-Peng Zhang, Ming-Zhe Chong, Xiao Ming-qing, Pu Peng, Peijie Feng, Haonan Sun, Zhipeng Zheng, Xiaofei Zang, Zheyu Fang, Ming‐Yao Xia · 发表于：Nature Communications · 年份：2025 · DOI：10.1038/s41467-025-63364-8 · 被引用次数：15 · 研究领域：Terahertz technology and applications、Photonic and Optical Devices、Advanced Semiconductor Detectors and Materials

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 <5.1%. Our technology also showcases application potential as OAM-mediated information encryption, offering impetus for next-generation high-dimensional photodetectors and information security.