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Elucidating the Behavior of Nanophotonic Structures through Explainable Machine Learning Algorithms
journal contribution
posted on 2020-07-31, 14:36 authored by Christopher Yeung, Ju-Ming Tsai, Brian King, Yusaku Kawagoe, David Ho, Mark W. Knight, Aaswath P. RamanA central
challenge in the development of nanophotonic structures
is identifying the optimal design for a target functionality, and
understanding the physical mechanisms that enable the optimized device’s
capabilities. Previously investigated design methods for nanophotonic
structures, including both conventional optimization approaches as
well as nascent machine learning (ML) strategies have made progress,
yet they remain “black boxes” that lack explanations
for their predictions. Here we demonstrate that convolutional neural
networks (CNN) trained to predict the electromagnetic response of
classes of metal-dielectric-metal metamaterials, including complex
freeform designs, can be explained to reveal deeper insights into
the underlying physics of nanophotonic structures. Using an explainable
AI (XAI) approach, we show that we can identify the importance of
specific spatial regions of a nanophotonic structure for the presence
or lack of an absorption peak. Our results highlight that ML strategies
can be used for physics discovery, as well as design optimization,
in optics and photonics.