The traditional numerical analysis in waveguide design can be time-consuming and inefficient. This is even more prominent in the THz region and with complex shapes and materials. As an alternative to overcome these drawbacks, we propose a machine learning (ML) approach to design porous-core photonic crystal fibers (PCFs) for the THz band. This method is based on an artificial neural network (ANN) model trained to predict key parameters such as the effective refractive index, effective area, dispersion, and loss values with accuracy and speed. In that sense, the network was trained to perform multiple-output regression of the above parameters. The training data for this model comes from numerical calculations that use the finite element method (FEM) to simulate and evaluate analytical expressions. Our results demonstrate the ML model’s ability to capture the complex and nonlinear relationships between the input and output parameters and accurately predict the behavior of the THz PCF. Moreover, the proposed model has an inference time of ∼0.03584 s for a batch of 32 data sets, which substantially outperforms typical calculation times needed in FEM simulations for THz waveguide design. These results show that this approach is efficient and effective and has the potential to significantly accelerate the design process of PCFs for THz applications.
A novel compact and multiband polarization beam splitter based on a dual-core transversally chirped microstructured optical fiber is proposed and analyzed, using finite element method. The results show that the ≈2.9 mm-long polarization splitter can reach an extinction ratio lower than -20 dB in two bands at 1140 nm and 1556 nm. The bandwidths of both bands are 11.7 nm and 47.2 nm respectively. This work analyzes the operation of the device when it is subjected to curvature, finding that it is possible to tune the operating bands. Numerical calculation indicates that this novel structure may find application in telecommunications, because it is capable of working at different wavelength ranges.
We present a novel sensing architecture consisting of a two-core transversally chirped microstructured optical fiber (MOF) suitable for label-free detection of molecules. The air holes of rings surrounding one core of the structure are functionalized for antibody detection by immobilization of an antigen sensor layer onto the walls of the holes. The change of the layer thickness of biomolecules can then be detected as a change in the device transmittance. Numerical calculations indicate that this novel structure can achieve acceptable level of sensitivity whereas the biosensor is mm long.
In this work, a technique for measuring high frequency micro-vibrations by using non holographic fiber specklegram sensor is experimentally demonstrated. In our setup, a laser source emitting at 632nm is coupled to a structure of Singlemode-Multimode-Singlemode fibers which produce a filtering effect that is used as optical transducer. Mechanical perturbations of controlled amplitude and frequency are applied to the Multimode fiber, which is fastened at the ends to induce vibrations. Perturbations higher than 3 KHz and below one micron are perfectly recovered by the system, which additionally, exhibits a linear response. Due to the low cost and the simplicity of the technique, it becomes an interesting method for the implementation of fiber sensors in a wide range of engineering applications.
The telecommunications industry and sensors require fast methods for engineering fiber lasers. In this work, using lowoptical-
power flat-top pulse excitations, it has been possible to determine both the attenuation coefficients and the
intrinsic saturation powers of doped single-mode fibers at 980 and 1550 nm. Laser systems have been projected for
which the optimal fiber length and output power have been determined as a function of the input power. Ring and linear
laser cavities have been set up, and the characteristics of the output laser have been obtained and compared with the
theoretical predictions based on the measured parameters.
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