Machine learning-based evaluation of performance of silicon nitride waveguide fabrication: Gradient-boosted forests for predicting propagation and bend excess losses
Year: 2024
Type: article
Abstract: \nThe 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.\n
Authors: Jakob Wilhelm Hinum-Wagner, Samuel Marko Hoermann, Gandolf Feigl, Christoph Schmidt, Jochen Kraft, Alexander Bergmann
Institutions: AMS (Austria), Graz University of Technology
Language: English
Topic: Photonic and Optical Devices
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W6884563121
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