Real-time imaging of standing-wave patterns in microresonators.

Real-time characterization of microresonator dynamics is important for many applications. In particular, it is critical for near-field sensing and understanding light-matter interactions. Here, we report camera-facilitated imaging and analysis of standing wave patterns in optical ring resonators.

Machine learning assisted inverse design of microresonators.

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.