Distributed curvature sensing using long period fiber grating and machine learning numerical analysis.

In this numerical study, we propose a fiber distributed curvature sensor based on the analysis of the spectral transmission of a long period fiber grating (LPG) with a neural network. A simulation of the optical transmissions of a proposed 6-cm LPG structure for different curvature profiles is first performed using EigenMode Expansion and a coupled-mode theory algorithm. Both fiber curvature profiles and their corresponding optical transmission spectra are then injected into a four dense layer neural network which, after training, leads to a 0.40% relative median estimation error in the bending profiles. This paper demonstrates the efficiency of neural network-based optical sensors to analyze non-uniform perturbations, while also revealing long-period gratings to be promising candidates for such systems.

Scaling the performances of integrated long period grating refractometers.

In this Letter, a theoretical analysis and design methodology of integrated long period gratings (LPGs) for refractometric applications are proposed. A detailed parametric analysis is applied to a LPG model based on two strip waveguides to highlight the main design variables and their effect on the refractometric performances, with focus on the spectral sensitivity and signature response. To illustrate the proposed methodology, four variants of the same LPG design are simulated with eigenmode expansion, displaying a wide range of sensitivities up to 300,000 nm/RIU with figures of merit (FOMs) as high as 8000.