Performance optimization of Bloch surface wave based devices using a XGBoost machine learning model

Bloch surface wave (BSW) based photonic devices have attracted significant interest for bio-sensing, spectroscopy, and light manipulation applications due to their ability to confine light at dielectric interfaces. However, optimizing the geometry of BSW structures to achieve desired optical properties can be computationally expensive using conventional simulation methods for multi-parameter design spaces. In this work, we develop machine learning models based on the gradient boosting algorithm XGBoost to predict key optical characteristics of BSW devices and expedite the design process. Finite element method simulations are used to generate a dataset relating BSW structures’ excitation angle, sensitivity, and spectral response to their geometric parameters, including thickness, porosity, and surrounding refractive index. This dataset trains and validates different XGBoost regression models for photonic structure optimization. Our results demonstrate that a model utilizing deep decision trees achieves the highest predictive accuracy, with a mean absolute error of 0.09° in estimating the excitation angle for new structures. We apply this optimized model to uncover the thickness-porosity combinations, enabling a maximum sensitivity of 171-degree/RIU. This machine learning approach provides a powerful tool for the inverse design and performance enhancement of BSW photonic structures beyond the capabilities of conventional simulation-based optimization.