A Deep Learning Regression Model for Photonic Crystal Fiber Sensor With XAI Feature Selection and Analysis

A Deep Learning Multi-output regression model is employed to correctly model the relationships between optical design parameters of an asymmetric Twin Elliptical Core Photonic Crystal Fiber (TEC-PCF) and its sensing performances. TEC-PCF acts as a biosensor to detect the blood glucose level taking hemoglobin components into account. Since asymmetric TEC-PCF uses a dual elliptical core, four super modes have to be evaluated to analyze the sensing performance in terms of effective index difference, transmission spectrum, coupling length, and sensor sensitivity. The dataset used in this work is of the optical design parameters of the sensor and Finite Element Method (FEM) results with effective indices of four super modes obtained from the COMSOL Multiphysics by varying hemoglobin concentration to 120 g/L, 140 g/L, and 160 g/L. Gretel.ai’s free open-source synthetic data library is used to augment the dataset to make the training more efficient. Explainable AI (XAI) feature analysis using Shapley Additive Explanations (SHAP) framework is used for two purposes: feature selection and to know the feature’s effect on prediction. The former led to the development of an optimal model with much fewer computational demands and the latter made the model interpretable. The proposed model can predict the accurate super modes when given input specifications with wavelength ranging from 1.27-
$1.5~\mu \text{m}$
and for various glucose concentrations under the influence of hemoglobin much faster compared to the other numerical simulations which are computationally expensive. Computation time taken by the proposed Artificial Neural Network (ANN) model, the proposed model with XAI and FEM is also being compared.