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

Authors

  • Clayton Fowler 1 Department of Electrical and Computer Engineering University of Massachusetts Lowell, 1 University Ave., Lowell, MA 01854, USA
  • Sensong An Department of Electrical and Computer Engineering University of Massachusetts Lowell, 1 University Ave., Lowell, MA 01854, USA
  • Bowen Zheng Department of Electrical and Computer Engineering University of Massachusetts Lowell, 1 University Ave., Lowell, MA 01854, USA
  • Hong Tang Department of Electrical and Computer Engineering University of Massachusetts Lowell, 1 University Ave., Lowell, MA 01854, USA
  • Hang Li Department of Electrical and Computer Engineering University of Massachusetts Lowell, 1 University Ave., Lowell, MA 01854, USA
  • Wei Guo Department of Physics and Applied Physics, University of Massachusetts Lowell, 1 University Ave., Lowell, MA 01854, USA
  • Hualiang Zhang Department of Electrical and Computer Engineering University of Massachusetts Lowell, 1 University Ave., Lowell, MA 01854, USA

Keywords:

Hyperspectral imaging, metal-insulatormetal, metasurface, narrowband filter, recurrent neural network

Abstract

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.

References

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Published

2020-11-07

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Articles