Pal A, Ghosh A, Zhang S, Bi T, Del'haye P (2023)
Publication Type: Journal article
Publication year: 2023
Book Volume: 31
Pages Range: 8020-8028
Journal Issue: 5
DOI: 10.1364/OE.479899
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%.
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://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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