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

Vishwaraj, NP and Nataraj, CT and Jagannath, RPK and Talabattula, S and Prashanth, GR (2024) Machine learning assisted strip waveguide Bragg gratings design for refractive index-based biosensing applications. In: Optik, 300 .

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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. © 2024 Elsevier GmbH

Item Type:	Journal Article
Publication:	Optik
Publisher:	Elsevier GmbH
Additional Information:	The copyright for this article belongs to Elsevier GmbH.
Keywords:	Biosensors; Machine learning; Neural networks; Refractive index; Regression analysis, Bragg grating sensors; Label-free biosensing; Machine-learning; Mean absolute error; Mode classification; Regression; Regression modelling; Strip waveguides; Transfer-matrix method; Waveguide Bragg grating, Transfer matrix method
Department/Centre:	Division of Electrical Sciences > Electrical Communication Engineering Division of Electrical Sciences > Electrical Communication Engineering > Electrical Communication Engineering - Technical Reports
Date Deposited:	04 Mar 2024 05:57
Last Modified:	04 Mar 2024 05:57
URI:	https://eprints.iisc.ac.in/id/eprint/84133

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