Deep Neural Network Inverse-Design for Long Wave Infrared Hyperspectral Imaging

This paper presents a deep learning approach for the inverse-design of metal-insulator-metal metasurfaces for hyperspectral imaging applications. Deep neural networks are able to compensate for the complex interactions between electromagnetic waves and metastructures to efficiently produce design so...

Full description

Saved in:
Bibliographic Details
Published in:Applied Computational Electromagnetics Society journal 2020-11, Vol.35 (11), p.1336-1337
Main Authors: Fowler, Clayton, An, Sensong, Zheng, Bowen, Tang, Hong, Li, Hang, Guo, Wei, Zhang, Hualiang
Format: Article
Language:English
Subjects:
Artificial neural networks
Deep learning
Electromagnetic radiation
Electromagnetic spectra
Hyperspectral imaging
Infrared imaging
Insulators
Machine learning
Neural networks
Recurrent neural networks
Spectrum allocation
Spectrum analysis
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:This paper presents a deep learning approach for the inverse-design of metal-insulator-metal metasurfaces for hyperspectral imaging applications. Deep neural networks are able to compensate for the complex interactions between electromagnetic waves and metastructures to efficiently produce design solutions that would be difficult to obtain using other methods. Since electromagnetic spectra are sequential in nature, recurrent neural networks are especially suited for relating such spectra to structural parameters.
ISSN:1054-4887
1054-4887
1943-5711
DOI:10.47037/2020.ACES.J.351137