Silicon-on-Insulator Optical Buffer Based on Magneto-Optical 1 × 3 Micro-Rings Array Coupled Sagnac Ring

Wang, Huiying; Wang, Zhi; Fu, Ziling; Jiang, Rui; Liu, Lanlan; Wang, Jian; Wu, Chung‐Chih

doi:10.3390/photonics9060366

Cited by 1 publication

(2 citation statements)

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“…In recent years, the optical response of magnetic materials has been tuned by exploiting magneto-optical (MO) effects, 1 which has led to breakthroughs in (bio)sensing, 1 routers/switches, 2 modulators, 3,4 circulators and isolators, 5−7 and buffering. 8 Because MO effects are weak in the nanoscale, enhancement has been sought through plasmonic resonance incorporating metallic building components. MO activity is increased in these magnetoplasmonic (MO metal) nanostructures that have enhanced, localized plasmonic fields (at the metal surface) distributed inside an adjacent MO layer.…”

Section: ■ Introductionmentioning

confidence: 99%

“…Developing a new host of active optical devices depends upon the ability to design nanostructures whose optical properties can be modulated dynamically. In recent years, the optical response of magnetic materials has been tuned by exploiting magneto-optical (MO) effects, which has led to breakthroughs in (bio)sensing, routers/switches, modulators, , circulators and isolators, − and buffering . Because MO effects are weak in the nanoscale, enhancement has been sought through plasmonic resonance incorporating metallic building components.…”

Section: Introductionmentioning

confidence: 99%

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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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