Machine learning assisted strip waveguide Bragg gratings design for refractive index-based biosensing applications

Abstract

Waveguide Bragg grating (WBG) biosensors have attracted significant interest because of their high sensitivity, immunity to electromagnetic signals, and capability for lab-on-chip applications. Designing a WBG biosensor requires solving Maxwell’s equations for the optical waveguide geometries numerically. This process is resource-intensive and hinders quick optimization of the waveguides for sensing. In this work, we explore the design of a silicon strip WBG structure for biosensing using machine learning. We develop an artificial neural network (ANN) classification model for mode classification and a regression model for predicting the effective refractive index (neff) for a given strip waveguide. We utilize results from finite element method and Transfer Matrix method for strip WBG structures to train a multi-output ANN regression model for quickly predicting sensing parameters. The mode classification model provides an accuracy of over 99%, and the regression model predicts the neff with a mean absolute error (MAE) of 0.5%. The multi-output regression model predicts the sensitivity with 0.5% MAE and quality factor and maximum reflectivity with less than 3% MAE. ANN model training requires a few minutes of computational time and reduces the dependency on computationally expensive resources for optimization of the sensor design, thereby fast-tracking the biosensor design cycle.