logo
ResearchBunny Logo
Neural network assisted high-spatial-resolution polarimetry with non-interleaved chiral metasurfaces

Engineering and Technology

Neural network assisted high-spatial-resolution polarimetry with non-interleaved chiral metasurfaces

C. Chen, X. Xiao, et al.

Explore a groundbreaking non-interleaved, interferometric method for polarization analysis leveraging a tri-channel chiral metasurface and a deep convolutional neural network. This innovative technique, conducted by authors from Nanjing University, significantly enhances the speed, robustness, and accuracy of polarimetry, even under challenging conditions.

00:00
00:00
Playback language: English
Abstract
This paper proposes a non-interleaved, interferometric method for polarization analysis using a tri-channel chiral metasurface and a deep convolutional neural network. The method enables fast, robust, and accurate polarimetry with high spatial resolution, overcoming limitations of traditional bulky systems. Experimental results demonstrate its capability to measure both uniform and non-uniform polarizations, and its application in distinguishing similar glasses.
Publisher
Light: Science & Applications
Published On
Jan 31, 2023
Authors
Chen Chen, Xingjian Xiao, Xin Ye, Jiacheng Sun, Jitao Ji, Rongtao Yu, Wange Song, Shining Zhu, Tao Li
Tags
polarization analysis
tri-channel chiral metasurface
deep convolutional neural network
polarimetry
experimental results
high spatial resolution
Listen, Learn & Level Up
Over 10,000 hours of research content in 25+ fields, available in 12+ languages.
No more digging through PDFs, just hit play and absorb the world's latest research in your language, on your time.
listen to research audio papers with researchbunny