Investigation of defective hybrid cladding with silicon nanocrystal PCF for supercontinuum generation

Defective hybrid cladding through a silicon nanocrystal-core-structured photonic crystal fiber intended for high pump power supercontinuum proliferation is discussed in this paper. The cladding comprehends a hybrid approach of a hexagonal air hole in the outer section and a petal-structured air hole in the inner layer with a twisted pattern. Such a procedure with an air hole in the cladding section with a silicon nanocore displays high nonlinearity and negative dispersion at the communication window for varying pulse widths with 20 kW pump power. The impact of structural parameters of the proposed structure on the optical constraints is discussed, namely, dispersion, nonlinearity and group-velocity dispersion for wavelengths ranging from 0.45 µm to 1.85 µm. The proposed structure with optimized structural parameters provides high nonlinearity of about 6.38 × 106 W−1 km−1 with negative dispersion of −70.19 ps (nm km)−1 at 1550 nm.

PCF Design With Extremely High Nonlinearity And Extremely Negative Dispersion

In this manuscript we designed a circular photonic crystal fiber (PCF) having three rectangular holes filled with GaP in the core region, three air hole rings and one annular air ring in cladding region. We found highest negative dispersion for the 1.8µm pitch alongwith very low confinement loss at wavelength 1.55µm. This designed PCF offers high nonlinearity (39612 W-1km-1) and high negative dispersion (-6586 ps nm-1 km-1) alongwith zero confinement loss at 1.55µm wavelength. We also compared the proposed PCF with the previously published PCF structure and found that the nonlinearity and negative dispersion of the designed PCF are very high in comparison to circular air hole based PCF. Another performance parameters viz. birefringence, numerical aperture, effective area and effective material loss are also analyzed.

Predication of negative dispersion for photonic crystal fiber using extreme learning machine

The photonic crystal fiber (PCF) is a resourceful optical device that can be used in various applications. The dispersion is a major impediment for such optical waveguides. We propose a modified PCF that evaluates the negative dispersion coefficient−3126 ps/(nm–km) at 1.55 μm wavelength. The precise value calculation of the design parameters is helpful to improve the desired output. The machine learning approaches are now more in fashion to predicate such parameters. The dispersion parameters are obtained for three different PCF models as conventional PCF with fixed radius air holes and type 1 and type 2 models with dual radius air holes. Further, the negative dispersion of a type-I PCF is modeled using an extreme learning machine (ELM) as a regression task and its performance is tested against benchmark models such as support vector machine with linear and radial basis function kernel function, Gaussian process regression, and artificial neural network. Results indicate superior performance of ELM as a regressor both, in terms of prediction and computation time.