Inverse Design of Microresonators Using Machine Learning

The rapidly increasing number of ways to fabricate integrated microresonators have led to a demand for optimization of such structures based on the desired optical properties. Tweaking the geometry of photonic structures can alter their dispersion profiles, which can counter optical nonlinearities a...

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Main Authors: Pal, Arghadeep, Ghosh, Alekhya, Zhang, Shuangyou, Bi, Toby, Del'Haye, Pascal
Format: Conference Proceeding
Language:English
Subjects:
Dispersion
Europe
Geometry
Integrated optics
Machine learning
Microcavities
Optical device fabrication
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Ghosh, Alekhya
Zhang, Shuangyou
Bi, Toby
Del'Haye, Pascal
description The rapidly increasing number of ways to fabricate integrated microresonators have led to a demand for optimization of such structures based on the desired optical properties. Tweaking the geometry of photonic structures can alter their dispersion profiles, which can counter optical nonlinearities and influence the intracavity optical dynamics. The parameter D_{\text{int}} describes the integrated dispersion which can be written as,
doi_str_mv 10.1109/CLEO/Europe-EQEC57999.2023.10232825
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source IEEE Xplore All Conference Series
subjects Dispersion
Europe
Geometry
Integrated optics
Machine learning
Microcavities
Optical device fabrication
title Inverse Design of Microresonators Using Machine Learning
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