Machine Learning Assisted Inverse Design of Microresonators
This provides a tool for researchers and engineers in photonics to optimize microresonator fabrication, though it is incremental as it applies existing ML methods to a known design challenge.
The paper tackles the problem of designing microresonators with specific optical properties by using machine learning to predict geometries from dispersion profiles, achieving an average error below 15% on simulated data.
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 (RF) yields the best results. The average error on the simulated data is well below 15%.