Machine learning approach for computing optical properties of a photonic crystal fiber.

Photonic crystal fibers (PCFs) are the specialized optical waveguides that led to many interesting applications ranging from nonlinear optical signal processing to high-power fiber amplifiers. In this paper, machine learning techniques are used to compute various optical properties including effective index, effective mode area, dispersion and confinement loss for a solid-core PCF. These machine learning algorithms based on artificial neural networks are able to make accurate predictions of above-mentioned optical properties for usual parameter space of wavelength ranging from 0.5-1.8 µm, pitch from 0.8-2.0 µm, diameter by pitch from 0.6-0.9 and number of rings as 4 or 5 in a silica solid-core PCF. We demonstrate the use of simple and fast-training feed-forward artificial neural networks that predicts the output for unknown device parameters faster than conventional numerical simulation techniques. Computation runtimes required with neural networks (for training and testing) and Lumerical MODE solutions are also compared.

Elimination of spurious modes in full-vectorial finite element method based acoustic modal solution.

Finite element method is a powerful technique for solving a wide range of engineering problems. However, the existence of the spurious solutions in full-vectorial finite element method has been a major problem for both acoustic and optic modal analyses. For emerging photonic devices exploiting light-sound interactions in high index contrast waveguides, this problem is a major limitation. A penalty function is introduced to remove these unwanted spurious modes in acoustic waveguides, which also identifies the acoustic modes more easily. Numerically simulated results also show considerably improved vector mode profiles. The proposed penalty method has been applied for the characterization of low index contrast single mode fiber and also for high index contrast silicon nanowire to demonstrate its effectiveness.

Efficient strategy to increase higher order inter-modal stability of a step index multimode fiber.

We demonstrate a novel approach to enhance the mode stability through increased effective index difference (Δneff) between the higher-order modes (LP18, LP09 and LP19) of a multimode fiber. Fibers with large diameters have bigger effective mode areas (Aeff) and can be useful for high power lasers and amplifiers. However, a large mode area (LMA) results in an increased number of modes that can be more susceptible to mode coupling. The modal effective index difference (Δneff) strongly correlates with mode stability and this increases as the modal order (m) increases. We report here that the mode spacing between the higher order modes can be further enhanced by introducing doped concentric rings in the core. In our work, we have shown a more than 35% increase in the mode spacing between the higher order modes by optimizing the doping profile of a LMA fiber. The proposed design technique is also scalable and can be applied to improve the mode spacing between different higher order modes and their neighboring antisymmetric modes, as necessary.