Highly Precise and Broadband Full-Stokes Polarimeter Based on a Deep Learning Algorithm

Recent developments in nanophotonics enable the practical realization of ultracompact full-Stokes polarimeters and imaging devices based on the well-defined polarization-sensitive structures of subwavelength size. However, the resonant feature of building blocks and the limited ability of the current processing technology greatly limit their detection accuracy and working waveband. Herein, we experimentally demonstrate a highly precise and broadband full-Stokes polarimeter based on the high-performance dielectric chiral shells (DCSs) and the deep learning (DL) algorithm. The large-area dielectric chiral metasurfaces (DCMs) fabricated by depositing Si on the self-assembled microsphere monolayer own a large transmittance, strong chirality, and anisotropy in the visible waveband, which enable the effective perception of the polarization state of light. The Stokes parameters are successfully detected by measuring several transmittances from DCSs with different rotation angles, i.e., I, and then analyzing with the pre-established mapping relationship between the Stokes parameters S and the recorded transmittance I based on the DL algorithm, i.e., S = f(I). In contrast to the traditional polarimeters, the detection accuracy based on the DL approach seems insensitive to the fabrication errors. A mean square error (MSE) of smaller than 0.5% at 760.06 nm was recorded, and an averaged MSE of less than 4% for Stokes parameters over a broad waveband from 400 to 840 nm was obtained, although the optical anisotropy and chirality of DCMs own a strong dispersion feature. The small MSE in a broad waveband, together with the easily accessing DCMs, make our polarimeter as the most potential candidate for future applications, such as the color polarization imaging devices and the ultraviolent polarimeter.