Machine Learning-Driven Compensation for Non-Ideal Channels in AWG-Based FBG Interrogator
This work addresses performance limitations in compact FBG interrogators for sensing applications, offering an incremental improvement in calibration accuracy and scalability.
The study tackled the problem of non-ideal spectral responses in AWG-based FBG interrogators by comparing analytical and machine learning calibration methods, with the ML approach achieving a root mean square error of 3.17 pm compared to 7.11 pm for the analytical method and maintaining sub-5 pm accuracy over an extended wavelength range.
We present an experimental study of a fiber Bragg grating (FBG) interrogator based on a silicon oxynitride (SiON) photonic integrated arrayed waveguide grating (AWG). While AWG-based interrogators are compact and scalable, their practical performance is limited by non-ideal spectral responses. To address this, two calibration strategies within a 2.4 nm spectral region were compared: (1) a segmented analytical model based on a sigmoid fitting function, and (2) a machine learning (ML)-based regression model. The analytical method achieves a root mean square error (RMSE) of 7.11 pm within the calibrated range, while the ML approach based on exponential regression achieves 3.17 pm. Moreover, the ML model demonstrates generalization across an extended 2.9 nm wavelength span, maintaining sub-5 pm accuracy without re-fitting. Residual and error distribution analyses further illustrate the trade-offs between the two approaches. ML-based calibration provides a robust, data-driven alternative to analytical methods, delivering enhanced accuracy for non-ideal channel responses, reduced manual calibration effort, and improved scalability across diverse FBG sensor configurations.