Physics-informed machine learning for programmable photonic circuits

Integrated photonic circuits offer a promising platform to implement matrix-vector multiplication in optical feedforward neural networks. The most common implementations rely on thermal phase shifters, which are inevitably susceptible to effects such as thermal and electrical crosstalk. Although deterministic, crosstalk-induced distortions have been challenging to accurately incorporate into physics-based analytical models. Additionally, analog hardware platforms suffer from fabrication deviations, that can have a significant impact on the computing performance, thus limiting scalability in implemented matrix size. In contrast, data-driven modeling techniques have shown to be promising approaches to modeling such circuits, yet they rely on black-box physics-agnostic modeling, require massive and unscalable amounts of training data, and cannot guarantee physically plausible results. Going beyond the data-driven black-box modeling techniques, but still taking advantage of the information captured by the data, we investigate the advantages of using physics-informed machine learning for photonic meshes. We analyze the ability of this approach to provide more accurate, less data-hungry, and physically plausible models for programmable photonic meshes. Moreover, we explore the potential to extract the knowledge from the trained model.