Random forest modelling as a tool for propagation and bend excess loss minimization on silicon nitride waveguide platforms

Jakob Wilhelm Hinum-Wagner; Samuel Marko Hoermann; Gandolf Feigl; Christoph Schmidt; Jochen Kraft; Alexander Bergmann

doi:10.1117/12.3022455

18 June 2024 Random forest modelling as a tool for propagation and bend excess loss minimization on silicon nitride waveguide platforms

Jakob Wilhelm Hinum-Wagner, Samuel Marko Hoermann, Gandolf Feigl, Christoph Schmidt, Jochen Kraft, Alexander Bergmann

Author Affiliations +

Proceedings Volume 13017, Machine Learning in Photonics; 1301718 (2024) https://doi.org/10.1117/12.3022455
Event: SPIE Photonics Europe, 2024, Strasbourg, France

Conference Poster

Abstract

This paper presents a detailed investigation into the propagation loss characteristics of silicon nitride strip waveguides at an 850 nm wavelength, utilizing a random forest model. The primary aim is to optimize low-loss conditions in photonic integrated circuits (PICs). To achieve this, a systematic 2x3 full factorial design of experiments is implemented, focusing on different layers within the PIC framework. The study revolves around a critical examination of how the waveguide width influences propagation loss. Leveraging the random forest model, known for its high precision in complex data analysis, we delve into the correlation between various design elements and their impact on loss. This methodology not only aids in pinpointing the pivotal factors affecting loss but also elucidates their interplay, particularly emphasizing the role of waveguide width. One of the key contributions of this research is the identification of optimal material configurations that significantly reduce loss. This is instrumental in enhancing the efficiency of PICs, a crucial aspect for their performance in applications such as optical communications and photonic computing. Our approach uniquely combines empirical data analysis with machine learning techniques, offering a novel perspective in photonic engineering research. The findings of this study not only shed light on the complex dynamics of waveguide design but also pave the way for the development of more efficient and effective photonic systems. This research stands to make a significant impact in the field, presenting a comprehensive methodology for designing low-loss silicon nitride strip waveguides, thereby contributing to the advancement of photonic technologies.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Jakob Wilhelm Hinum-Wagner, Samuel Marko Hoermann, Gandolf Feigl, Christoph Schmidt, Jochen Kraft, and Alexander Bergmann "Random forest modelling as a tool for propagation and bend excess loss minimization on silicon nitride waveguide platforms", Proc. SPIE 13017, Machine Learning in Photonics, 1301718 (18 June 2024); https://doi.org/10.1117/12.3022455

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