Data-driven programming of feedforward photonic integrated circuits

Programming photonic integrated hardware often reveals itself as a challenging task because of the presence of non-idealities in the photonic chip. These include fabrication imperfections and parasitic effects such as thermal crosstalk, which cause unwanted coupling between control signals. Traditional control methods based on idealized models often fail to account for these phenomena, leading to significant discrepancies between the desired and actual circuit behavior. In this work, we propose a data-driven approach for controlling meshes of thermally tunable Mach–Zehnder interferometers (MZIs), which exploits a machine learning (ML) model trained to compensate for these non-idealities by pre-adjusting the electrical power given to integrated phase shifters. The proposed ML system is assessed using synthetic datasets and experimentally validated on a 3 × 3 triangular MZI mesh. Results demonstrate that the data-driven controller significantly improves programming accuracy, offering a robust solution for accurate programming of photonic integrated circuits.