Enhancement of Transverse Magneto-Optic Kerr effect in subwavelength all-dielectric guided mode gratings with a buffer layer

Li, Junsheng; Liu, Youwen; Li, Xiao

doi:10.1016/j.rinp.2024.107597

Cited by 2 publications

(1 citation statement)

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“…However, energy efficiency is limited by the intrinsic joule heating in these magnetoplasmonic systems. This has motivated the use of all-dielectric magnetophotonic nanostructures, − which feature low levels of losses as a result of the lack of joule heating. The main challenge in designing these all-dielectric nanophotonic platforms is to identify the optical resonances with high MO amplitudes.…”

Section: Introductionmentioning

confidence: 99%

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

Carvalho,

Aiex Taier Filho,

Oliveira

et al. 2024

ACS Appl. Mater. Interfaces

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All-dielectric magnetophotonic nanostructures are promising for integrated nanophotonic devices with high resolution and sensitivity, but their design requires computationally demanding electromagnetic simulations evaluated through trial and error. In this paper, we propose a machinelearning approach to accelerate the design of these nanostructures. Using a data set of 12 170 samples containing four geometric parameters of the nanostructure and the incidence wavelength, trained neural network and polynomial regression algorithms were capable of predicting the amplitude of the transverse magneto-optical Kerr effect (TMOKE) within a time frame of 10 −3 s and mean square error below 4.2%. With this approach, one can readily identify nanostructures suitable for sensing at ultralow analyte concentrations in aqueous solutions. As a proof of principle, we used the machine-learning models to determine the sensitivity (S = |Δθ res /Δn a |) of a nanophotonic grating, which is competitive with state-of-the-art systems and exhibits a figure of merit of 672 RIU −1 . Furthermore, researchers can use the predictions of TMOKE peaks generated by the algorithms to assess the suitability for experimental setups, adding a layer of utility to the machine-learning methodology.

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Section: Introductionmentioning

confidence: 99%