Deep Neural Network Inverse-Design for Long Wave Infrared Hyperspectral Imaging
关键词:
Hyperspectral imaging, metal-insulatormetal, metasurface, narrowband filter, recurrent neural network摘要
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.
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