Artificial Intelligence Assisted Optimization and Prediction of Absorption of Metasurfaces for Hot-Electron Generation

Raktim Sarma; Raktim Sarma; Michael Goldflam; Emily Donahue; Abigail Pribisova; Sylvain Gennaro; Sylvain Gennaro; Jeremy Wright; Igal Brener; Igal Brener; Jayson Briscoe

doi:10.1364/CLEO_AT.2021.JTu3A.106

Conference on Lasers and Electro-Optics
OSA Technical Digest (Optica Publishing Group, 2021),
paper JTu3A.106
•https://doi.org/10.1364/CLEO_AT.2021.JTu3A.106

Artificial Intelligence Assisted Optimization and Prediction of Absorption of Metasurfaces for Hot-Electron Generation

Raktim Sarma, Michael Goldflam, Emily Donahue, Abigail Pribisova, Sylvain Gennaro, Jeremy Wright, Igal Brener, and Jayson Briscoe

CLEO: Applications and Technology 2021

San Jose, California United States
9–14 May 2021
ISBN: 978-1-943580-91-0

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Joint Poster Session I (JTu3A)

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R. Sarma, M. Goldflam, E. Donahue, A. Pribisova, S. Gennaro, J. Wright, I. Brener, and J. Briscoe, "Artificial Intelligence Assisted Optimization and Prediction of Absorption of Metasurfaces for Hot-Electron Generation," in Conference on Lasers and Electro-Optics, J. Kang, S. Tomasulo, I. Ilev, D. Müller, N. Litchinitser, S. Polyakov, V. Podolskiy, J. Nunn, C. Dorrer, T. Fortier, Q. Gan, and C. Saraceno, eds., OSA Technical Digest (Optica Publishing Group, 2021), paper JTu3A.106.

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Abstract

We use artificial intelligence techniques such as the genetic algorithm and convolutional neural networks for optimization and prediction of absorption spectra of plasmonic metasurfaces for enhancing hot-electron generation. The predictions of our algorithms agree well to experimental results.

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