Deep-learning-based colorimetric polarization-angle detection with metasurfaces
Bo Yang, Dina Ma, Wenwei Liu, Duk‐Yong Choi, Zhancheng Li, Hua Cheng, Jian-Guo Tian, Shuqi Chen
Abstract
Polarization plays a key role in both optics and photonics. Generally, the polarization states of light are measured with birefringent or dichroic optical elements paired with a power meter. Here we propose a direct polarization detection method based on colorimetric asymmetrical all-dielectric metasurfaces to obtain the polarization angles of the incident light. The independently tunable periods and diameters along the <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mi>x</mml:mi> </mml:math> and <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mi>y</mml:mi> </mml:math> axes enables double-layer nanopillars to realize high-performance dual-color palettes with arbitrary combinations under orthogonal polarization states. The polarization detection network based on residual networks is used to deeply learn the regulations between color palette variations and incident polarization angles, which can accurately recognize extremely slight polarization variations in about 1 s with an accuracy of 81.4% within 0.7° error and 99.5% within 1.4° error. Our strategy significantly improves the compactness of polarization detection, and it can be readily expanded to polarization distribution measurement and colorimetric polarization imaging on an intelligent platform.