Open AccessArtificial neural network discovery of a switchable metasurface reflector
Author(s) -
Jonathan Thompson,
Joshua A. Burrow,
Piyush Shah,
Jonathan E. Slagle,
Eric Harper,
Andre Van Rynbach,
Imad Agha,
Matthew S. Mills
Publication year - 2020
Publication title -
optics express
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.394
H-Index - 271
ISSN - 1094-4087
DOI - 10.1364/oe.400360
Subject(s) - artificial neural network , robustness (evolution) , computer science , optics , refractive index , reflection (computer programming) , ternary operation , transmission (telecommunications) , materials science , artificial intelligence , physics , telecommunications , biochemistry , chemistry , gene , programming language
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 Ge 2 Sb 2 Te 5 (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.