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

Published in: Light, science & applications (2024)

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.

Keyphrases

convolutional neural network
neural network
deep learning
capillary electrophoresis
single molecule
ionic liquid
high resolution
fluorescent probe
machine learning
mass spectrometry