Analysis of Photonic Circuit Losses with Machine Learning Techniques

2025/06/22 by Utama, Adrian Nugraha, Goh, Simon Chun Kiat, Hongyu, Li +4 · 1 citation
#Applied Physics (physics.app-ph) #Data Analysis #FOS: Physical sciences #Optics (physics.optics) #Statistics and Probability (physics.data-an)

paper · doi:10.48550/arxiv.2506.17999

Abstract

Low-loss waveguides enable efficient light delivery in photonic circuits, which are essential for high-speed optical communications and scalable implementations of photonic quantum technologies. We study the effects of several fabrication and experimental parameters on the waveguide losses of a silicon nitride integrated photonics platform using various machine learning techniques. Compared to more complex machine learning algorithms, our results show that a simple linear regression model with variable selection offers a lower prediction error with high interpretability. The high interpretability, along with our domain knowledge, led to unique identification of fabrication process dependencies on the final outcome. With these insights, we show that by improving the process flow, it is possible to improve the loss by mitigating the cause in a real experiment.

Citations

• Cross-Validation: What Does It Estimate and How Well Does It Do It?
• Adam: A Method for Stochastic Optimization
• A tutorial on support vector regression

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