Elucidating the Physics of Nanophotonic Structures Through Explainable Machine Learning Algorithms

Christopher Yeung; Ju-Ming Tsai; Brian King; Yusaku Kawagoe; David Ho; Aaswath Raman

doi:10.1364/FIO.2020.FM2A.2

Frontiers in Optics / Laser Science
OSA Technical Digest (Optica Publishing Group, 2020),
paper FM2A.2
•https://doi.org/10.1364/FIO.2020.FM2A.2

Elucidating the Physics of Nanophotonic Structures Through Explainable Machine Learning Algorithms

Christopher Yeung, Ju-Ming Tsai, Brian King, Yusaku Kawagoe, David Ho, and Aaswath Raman

Frontiers in Optics 2020

Washington, DC United States
14–17 September 2020
ISBN: 978-1-943580-80-4

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Machine Learning and Optimization (FM2A)

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C. Yeung, J. Tsai, B. King, Y. Kawagoe, D. Ho, and A. Raman, "Elucidating the Physics of Nanophotonic Structures Through Explainable Machine Learning Algorithms," in Frontiers in Optics / Laser Science, B. Lee, C. Mazzali, K. Corwin, and R. Jason Jones, eds., OSA Technical Digest (Optica Publishing Group, 2020), paper FM2A.2.

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Abstract

Explainable machine learning algorithms were applied to convolutional neural networks to reveal deeper insights into the properties of metamaterials, demonstrating new avenues for physics discovery and device optimization in optics and photonics.

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