Artificial Neural Network Discovery of a Switchable Metasurface Reflector

Authors

J. R. Thompson, Air Force Research Laboratory
J. A. Burrow, Azimuth Corporation
P. J. Shah, Air Force Research Laboratory
Jonathan E. Slagle, Air Force Research Laboratory
E. S. Harper, Air Force Research Laboratory
A. Van Rynbach, Air Force Research Laboratory
I. Agha, Azimuth Corporation
M. S. Mills, Air Force Research Laboratory

Document Type

Article

Publication Date

8-5-2020

Department

Department of Engineering Physics

School or Division

Graduate School of Engineering and Management

Digital Object Identifier

10.1364/OE.400360

Source Publication

Optics Express (e-ISSN 1094-4087)

Abstract

Optical materials engineered to dynamically and selectively manipulate electromagnetic waves are essential to the future of modern optical systems. In this paper, we simulate various metasurface configurations consisting of periodic 1D bars or 2D pillars made of the ternary phase change material Ge2Sb2Te5 (GST). Dynamic switching behavior in reflectance is exploited due to a drastic refractive index change between the crystalline and amorphous states of GST. Selectivity in the reflection and transmission spectra is manipulated by tailoring the geometrical parameters of the metasurface. Due to the immense number of possible metasurface configurations, we train deep neural networks capable of exploring all possible designs within the working parameter space. The data requirements, predictive accuracy, and robustness of these neural networks are benchmarked against a ground truth by varying quality and quantity of training data. After ensuring trustworthy neural network advisory, we identify and validate optimal GST metasurface configurations best suited as dynamic switchable mirrors depending on selected light and manufacturing constraints.

Comments

© 2020 Optical Society of America under the terms of the OSA Open Access Publishing Agreement, and shared on AFIT Scholar in accordance with OSA's open access policies. Users may use, reuse, and build upon the article, or use the article for text or data mining, so long as such uses are for non-commercial purposes and appropriate attribution is maintained. All other rights are reserved.

Sourced from the version of record as cited below and linked in the DOI.

Author Jonathan E. Slagle was in a PhD program at AFIT at the time of publication. (PhD March 2021, AFIT-ENP-DS-21-M-327)

Recommended Citation

J. R. Thompson, J. A. Burrow, P. J. Shah, J. Slagle, E. S. Harper, A. Van Rynbach, I. Agha, and M. S. Mills, "Artificial neural network discovery of a switchable metasurface reflector," Opt. Express 28, 24629-24656 (2020)