Transmission line method for the simulation of fiber Bragg gratings

Abstract

A new method for the analysis and design of fiber Bragg gratings (FBG) based on the theory of transmission lines has been developed and verified both theoretically and experimentally. The method is an extension of the Coupled Mode Theory and utilizes the equivalent transmission lines in order to simulate any type of grating, with an easy and direct implementation. The method provides the ability to analyze the optical devices without using full wave approaches, while also facilitating the incorporation of core materials with a complex or nonlinear refractive index, non-uniform distributions of the grating's refractive index, and tilted and phase shifted gratings. The approach also allows the design of the grating for a given reflection spectra. Numerical results of the method's application on a randomly varied inscription of the refractive index of a FBG have also been simulated and discussed. Using this method, the characteristics of an Erbium-Doped FBG have been simulated and the predictions verified experimentally.

Measurement and Discrimination of Asymmetric Non-uniform Strain Distribution Based on Spectrum Characterization of FBG Sensors

“…To prove the feasibility and measuring resolution of the FBG spectrum, the TMM and FEM were combined to reconstruct the spectrum response with specific nonuniform strain in glass-to-metal seals. This spectrum reconstruction method combined the advantages of both FEM and TMM, and it has been applied by many researchers to predict spectrum variations under large-gradient strain distribution (Kakei et al, 2018;Rajabzadeh et al, 2019;Fazzi et al, 2019;Stathopoulos et al, 2019). The strain of the fiber core transferred directly to the surface of the sealing glass to simplify the simulation (Wang et al, 2019) because the bare fiber was well-fused with molten glass during the manufacturing process and there was little difference between the mechanical properties of fiber and sealing glass (the main component for both was SiO 2 ).…”

Demonstration of a fast-training feed-forward machine learning algorithm for studying key optical properties of FBG and predicting precisely the output spectrum

“…These FBGs are characterized by speci c properties such as effective refractive index (n eff ), central Bragg wavelength (λ B ), re ectivity and bandwidth spectrum. Due to distinct geometrical designs and light propagation mechanisms, each type of FBGs requires dedicated numerical modelling for analyzing the structure [9][10][11].…”