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Full-Control and Switching of Optical Fano Resonance by Continuum State Engineering

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Abstract
Fano resonance, known for its unique asymmetric line shape, has gained significant attention in photonics, particularly in sensing applications. However, it remains difficult to achieve controllable Fano parameters with a simple geometric structure. Here, a novel approach of using a thin-film optical Fano resonator with a porous layer to generate entire spectral shapes from quasi-Lorentzian to Lorentzian to Fano is proposed and experimentally demonstrated. The glancing angle deposition technique is utilized to create a polarization-dependent Fano resonator. By altering the linear polarization between s- and p-polarization, a switchable Fano device between quasi-Lorentz state and negative Fano state is demonstrated. This change in spectral shape is advantageous for detecting materials with a low-refractive index. A bio-particle sensing experiment is conducted that demonstrates an enhanced signal-to-noise ratio and prediction accuracy. Finally, the challenge of optimizing the film-based Fano resonator due to intricate interplay among numerous parameters, including layer thicknesses, porosity, and materials selection, is addressed. The inverse design tool is developed based on a multilayer perceptron model that allows fast computation for all ranges of Fano parameters. The method provides improved accuracy of the mean validation factor (MVF = 0.07, q-q') compared to the conventional exhaustive enumeration method (MVF = 0.37). An optical Fano resonator with a porous layer allows for full control of spectral shapes from quasi-Lorentzian to Lorentzian to Fano. The resonator exhibits switchable behavior between quasi-Lorentz and negative Fano states through polarization changes. Enhanced bio-particle sensing capabilities are demonstrated, and an inverse design tool based on a multilayer perceptron model is developed to optimize Fano parameters efficiently.image
Author(s)
Ko, Joo HwanPark, Jin-HwiYoo, Young JinChang, SehuiKang, JiwonWu, AiguoYang, FangKim, SejeongJeon, Hae-GonSong, Young Min
Issued Date
2023-09
Type
Article
DOI
10.1002/advs.202304310
URI
https://scholar.gist.ac.kr/handle/local/10019
Publisher
WILEY
Citation
ADVANCED SCIENCE, v.10, no.32
ISSN
2198-3844
Appears in Collections:
Department of AI Convergence > 1. Journal Articles
Department of Electrical Engineering and Computer Science > 1. Journal Articles
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