# Data Driven Programming of Photonic Integrated Circuits

## TL;DR

This paper presents a machine learning-based control method for photonic integrated circuits that compensates for fabrication imperfections and parasitic effects, significantly improving programming accuracy and robustness.

## Contribution

It introduces a data-driven ML approach to control photonic meshes, addressing non-idealities that traditional models fail to handle effectively.

## Key findings

- ML-based control improves programming accuracy
- Method validated on synthetic and experimental datasets
- Achieves robust compensation for non-idealities in photonic circuits

## Abstract

Programming photonic integrated hardware often reveals as a challenging task because of the presence of non-idealities in the photonic chip. These include fabrication imper- fections 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 phenomana, leading to significant discrepancies between the desired and actual circuit behaviour. In this work, we propose a data-driven approach for control- ling meshes of thermally tuneable 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 x 3 triangular MZI mesh. Results demonstrate that the data-driven controller significantly improves program- ming accuracy, offering a robust solution for accurate programming of photonic integrated circuits.