Non-interleaved chiral metasurfaces and neural networks enhance the spatial resolution of polarimetry

Abstract Non-interleaved chiral metasurfaces for high-spatial-resolution polarimetry are proposed and demonstrated. Furthermore, a convolutional neural network is incorporated to analyze interferometric images with the polarization state of light, and it results in accurate Stokes parameters.