Machine learning assisted inverse design of microresonators

Pal A, Ghosh A, Zhang S, Bi T, Del'haye P (2023)

Publication Type: Journal article

Publication year: 2023

Journal

Optics Express Optical Society of America

Book Volume: 31

Pages Range: 8020-8028

Journal Issue: 5

DOI: 10.1364/OE.479899

Abstract

The high demand for fabricating microresonators with desired optical properties has led to various techniques to optimize geometries, mode structures, nonlinearities, and dispersion. Depending on applications, the dispersion in such resonators counters their optical nonlinearities and influences the intracavity optical dynamics. In this paper, we demonstrate the use of a machine learning (ML) algorithm as a tool to determine the geometry of microresonators from their dispersion profiles. The training dataset with ∼460 samples is generated by finite element simulations and the model is experimentally verified using integrated silicon nitride microresonators. Two ML algorithms are compared along with suitable hyperparameter tuning, out of which Random Forest yields the best results. The average error on the simulated data is well below 15%.

Involved external institutions

Max-Planck-Institut für die Physik des Lichts (MPL) / Max Planck Institute for the Science of Light DE Germany (DE)

How to cite

APA:

Pal, A., Ghosh, A., Zhang, S., Bi, T., & Del'haye, P. (2023). Machine learning assisted inverse design of microresonators. Optics Express, 31(5), 8020-8028. https://dx.doi.org/10.1364/OE.479899

MLA:

Pal, Arghadeep, et al. "Machine learning assisted inverse design of microresonators." Optics Express 31.5 (2023): 8020-8028.

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