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Spectral and Angle-Resolved Magneto-Optical Characterization of Photonic Nanostructures

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Published on: November 21, 2019

Toward Machine-Learning-Accelerated Design of All-Dielectric Magnetophotonic Nanostructures.

William O F Carvalho¹, Marcio Tulio Aiex Taier Filho², Osvaldo N Oliveira¹

¹Sao Carlos Institute of Physics, University of Sao Paulo, CP 369, São Carlos, São Paulo 13560-970, Brazil.

ACS Applied Materials & Interfaces

|July 30, 2024

Summary

Machine learning accelerates the design of all-dielectric magnetophotonic nanostructures. This approach rapidly predicts the transverse magneto-optical Kerr effect (TMOKE), enabling sensitive detection for nanophotonic devices.

Keywords:

TMOKE sensing all-dielectric machine-learning-accelerated design magnetophotonic neural networks polynomial regression

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Area of Science:

Nanophotonics
Magnetophotonics
Machine Learning

Background:

All-dielectric magnetophotonic nanostructures offer high resolution and sensitivity for integrated devices.
Current design methods rely on computationally intensive simulations and trial-and-error.

Purpose of the Study:

To develop a machine learning approach for accelerating the design of magnetophotonic nanostructures.
To enable rapid prediction of optical properties for device optimization.

Main Methods:

A dataset of 12,170 nanostructure samples with geometric parameters and incidence wavelength was used.
Neural network and polynomial regression algorithms were trained to predict the transverse magneto-optical Kerr effect (TMOKE).

Main Results:

Machine learning models predicted TMOKE amplitude with mean square error below 4.2% in 10^-3 s.
The approach identified nanostructures suitable for ultralow analyte concentration sensing.
A nanophotonic grating demonstrated a figure of merit of 672 RIU^-1 for sensing applications.

Conclusions:

Machine learning significantly accelerates the design cycle for magnetophotonic nanostructures.
The developed models provide a practical tool for assessing experimental feasibility and optimizing device performance.