Metric learning: a new approach for defining similarity measures for nanophotonics

Mohammadreza Zandehshahvar; Yashar Kiarashi; Muliang Zhu; Daqian Bao; Mohammad Hadigheh Javani; Reza Pourabolghasem; Ali Adibi

doi:10.1117/12.2661572

17 March 2023 Metric learning: a new approach for defining similarity measures for nanophotonics

Mohammadreza Zandehshahvar, Yashar Kiarashi, Muliang Zhu, Daqian Bao, Mohammad Hadigheh Javani, Reza Pourabolghasem, Ali Adibi

Author Affiliations +

Proceedings Volume PC12431, Photonic and Phononic Properties of Engineered Nanostructures XIII; PC124310P (2023) https://doi.org/10.1117/12.2661572
Event: SPIE OPTO, 2023, San Francisco, California, United States

Abstract

In this work, we present a new approach based on metric learning for defining new similarity measures that are well-matched for design tasks in nanophotonics. Majority of the existing approaches use mean squared error (MSE) or mean absolute error (MAE) as the similarity measure to compare the desired and optimal spectra while it is clear that point-wise distance cannot capture the important features of the responses. Here, our goal is to use deep metric learning to provide a systematic approach for defining new metrics in nanophotonics.

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Mohammadreza Zandehshahvar, Yashar Kiarashi, Muliang Zhu, Daqian Bao, Mohammad Hadigheh Javani, Reza Pourabolghasem, and Ali Adibi "Metric learning: a new approach for defining similarity measures for nanophotonics", Proc. SPIE PC12431, Photonic and Phononic Properties of Engineered Nanostructures XIII, PC124310P (17 March 2023); https://doi.org/10.1117/12.2661572

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