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

Air-holes induced multimodal fiber design to increase the effective index difference between higher order guided modes

A novel technique is proposed to increase the effective index difference (△neff) between higher order modes of a multimode step-index fiber. Multimode fibers provide a higher effective area and their higher order modes are also resistant to area reduction due to bending. However, the larger effective area comes with an increased number of modes which are more prone to mode coupling and mode mixing. The modal stability is directly related to the effective index difference between the mode of propagation and its neighboring modes. We have shown here that the modal stability between LP06 mode and its neighboring antisymmetric LP15 and LP16 modes can be increased more than 54% by the introduction of air-holes array along the circumference of the fiber. We have also shown variation in the effective index difference with possible fabrication tolerances that may occur in air-holes size and change in their locations. Furthermore, the technique presented here can also be applied to increase the stability of other higher modes of a multimode fiber

Low Temperature Graphene Growth and Its Applications in Electronic and Optical Devices

Graphene, a two dimensional allotrope of carbon in a honeycomb lattice, has gathered wide attention due to its excellent electrical, thermal, optical and mechanical properties. It has extremely high electron/hole mobility, very high thermal conductivity and fascinating optical properties, and combined with its mechanical strength and elasticity, graphene is believed to find commercial applications in existing as well as novel technologies. One of the biggest reasons behind the rapid development in graphene research during the last decade is the fact that laboratory procedures to obtain high quality graphene are rather cheap and simple. However, any new material market is essentially driven by the progress in its large scale commercial production with minimal costs, with properties that are suited for different applications. And it is in this aspect that graphene is still required to make a huge progress before its commercial benefits can be derived. Laboratory graphene synthesis techniques such as mechanical exfoliation, liquid phase exfoliation and SiC graphene growth pose several challenges in terms of cost, reliability and scalability. To this end, Chemical Vapor Deposition (CVD) growth of graphene has emerged as a widely used synthesis method that overcomes these problems. Unfortunately, conventional thermal CVD requires a high temperature of growth and a catalytic metal substrate, making the undesirable step of graphene transfer a necessity. Besides requiring a catalyst, the high temperature of growth also limits the range of growth substrates. In this work, I have successfully demonstrated low temperature (~550 °C) growth of graphene directly on dielectric materials using a Plasma-Enhanced CVD (PECVD) process. The PECVD technique described here solves the issues faced by conventional CVD methods and provides a direct route for graphene synthesis on arbitrary materials at relatively low temperatures. Detailed growth studies, as described here, illustrate the difference between the PECVD and the CVD growth mechanisms. This work also provides the first experimental comparison of graphene growth rates on different substrates using PECVD. In the second part of my thesis, I have discussed some of the potential applications of PECVD graphene, including graphene as a diffusion barrier, ultra-dark graphene metamaterials, graphene-protected metal plasmonics and copper-graphene hybrids for RF transmission line applications. The experimental findings discussed here lay a solid platform for integration of graphene in damascene structures, low-loss plasmonic materials, flexible electronics and dark materials, among others

A comprehensive deep learning method for empirical spectral prediction and its quantitative validation of nano-structured dimers

Abstract Nanophotonics exploits the best of photonics and nanotechnology which has transformed optics in recent years by allowing subwavelength structures to enhance light-matter interactions. Despite these breakthroughs, design, fabrication, and characterization of such exotic devices have remained through iterative processes which are often computationally costly, memory-intensive, and time-consuming. In contrast, deep learning approaches have recently shown excellent performance as practical computational tools, providing an alternate avenue for speeding up such nanophotonics simulations. This study presents a DNN framework for transmission, reflection, and absorption spectra predictions by grasping the hidden correlation between the independent nanostructure properties and their corresponding optical responses. The proposed DNN framework is shown to require a sufficient amount of training data to achieve an accurate approximation of the optical performance derived from computational models. The fully trained framework can outperform a traditional EM solution using on the COMSOL Multiphysics approach in terms of computational cost by three orders of magnitude. Furthermore, employing deep learning methodologies, the proposed DNN framework makes an effort to optimise design elements that influence the geometrical dimensions of the nanostructure, offering insight into the universal transmission, reflection, and absorption spectra predictions at the nanoscale. This paradigm improves the viability of complicated nanostructure design and analysis, and it has a lot of potential applications involving exotic light-matter interactions between nanostructures and electromagnetic fields. In terms of computational times, the designed algorithm is more than 700 times faster as compared to conventional FEM method (when manual meshing is used). Hence, this approach paves the way for fast yet universal methods for the characterization and analysis of the optical response of nanophotonic systems

Artificial Neural Network Modelling for Optimizing the Optical Parameters of Plasmonic Paired Nanostructures

The Artificial Neural Network (ANN) has become an attractive approach in Machine Learning (ML) to analyze a complex data-driven problem. Due to its time efficient findings, it has became popular in many scientific fields such as physics, optics, and material science. This paper presents a new approach to design and optimize the electromagnetic plasmonic nanostructures using a computationally efficient method based on the ANN. In this work, the nanostructures have been simulated by using a Finite Element Method (FEM), then Artificial Intelligence (AI) is used for making predictions of associated sensitivity (S), Full Width Half Maximum (FWHM), Figure of Merit (FOM), and Plasmonic Wavelength (PW) for different paired nanostructures. At first, the computational model is developed by using a Finite Element Method (FEM) to prepare the dataset. The input parameters were considered as the Major axis, a, the Minor axis, b, and the separation gap, g, which have been used to calculate the corresponding sensitivity (nm/RIU), FWHM (nm), FOM, and plasmonic wavelength (nm) to prepare the dataset. Secondly, the neural network has been designed where the number of hidden layers and neurons were optimized as part of a comprehensive analysis to improve the efficiency of ML model. After successfully optimizing the neural network, this model is used to make predictions for specific inputs and its corresponding outputs. This article also compares the error between the predicted and simulated results. This approach outperforms the direct numerical simulation methods for predicting output for various input device parameters