Machine learning-based evaluation of performance of silicon nitride waveguide fabrication: Gradient-boosted forests for predicting propagation and bend excess losses - Hinum-Wagner Jakob Wilhelm; Hoermann Samuel Marko; Feigl Gandolf; Schmidt Christoph; Kraft Jochen; Bergmann Alexander (EPJ Web of Conferences)
The propagation and bend excess loss characteristics of silicon nitride strip waveguides at an 850 nm wavelength were explored in this study. The aim was to optimize fabrication processes using machine learning, particularly gradient-boosted forests, to achieve low-loss photonic integrated circuits (PICs) and accurately predict the losses. The impact of waveguide geometry and layer properties on loss was examined using a full factorial design of experiment. These machine learning models’ predictive accuracy and ability to capture complex relationships between fabrication parameters and different loss mechanisms were assessed. Key parameters and interactions were identified, improving PIC efficiency for photonic sensing applications.