Machine Learning-Driven Compensation for Non-Ideal Channels in AWG-Based FBG Interrogator
Abstract
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.
Cite
@article{arxiv.2506.13575,
title = {Machine Learning-Driven Compensation for Non-Ideal Channels in AWG-Based FBG Interrogator},
author = {Ivan A. Kazakov and Iana V. Kulichenko and Egor E. Kovalev and Angelina A. Treskova and Daria D. Barma and Kirill M. Malakhov and Ivan V. Oseledets and Arkady V. Shipulin},
journal= {arXiv preprint arXiv:2506.13575},
year = {2025}
}
Comments
The manuscript has been accepted and is now available in early access in IEEE Sensors Letters. This revision includes the addition of a co-author, and updates the style of Figure 4 and the formatting of Table 1