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

ABSTRACT: 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 machine-learning 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 a

SUBMITTER: Carvalho WOF 

PROVIDER: S-EPMC11331439 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature