Generalized Lorenz-Mie theory : application to scattering and resonances of photonic complexes

Les structures photoniques complexes permettent de façonner la propagation lumineuse à l’échelle de la longueur d’onde au moyen de processus de diffusion et d’interférence. Cette fonctionnalité à l’échelle nanoscopique ouvre la voie à de multiples applications, allant des communications optiques aux biosenseurs. Cette thèse porte principalement sur la modélisation numérique de structures photoniques complexes constituées d’arrangements bidimensionnels de cylindres diélectriques. Deux applications sont privilégiées, soit la conception de dispositifs basés sur des cristaux photoniques pour la manipulation de faisceaux, de même que la réalisation de sources lasers compactes basées sur des molécules photoniques. Ces structures optiques peuvent être analysées au moyen de la théorie de Lorenz-Mie généralisée, une méthode numérique permettant d’exploiter la symétrie cylindrique des diffuseurs sous-jacents. Cette dissertation débute par une description de la théorie de Lorenz-Mie généralisée, obtenue des équations de Maxwell de l’électromagnétisme. D’autres outils théoriques utiles sont également présentés, soit une nouvelle formulation des équations de Maxwell-Bloch pour la modélisation de milieux actifs appelée SALT (steady state ab initio laser theory). Une description sommaire des algorithmes d’optimisation dits métaheuristiques conclut le matériel introductif de la thèse. Nous présentons ensuite la conception et l’optimisation de dispositifs intégrés permettant la génération de faisceaux d’amplitude, de phase et de degré de polarisation contrôlés. Le problème d’optimisation combinatoire associé est solutionné numériquement au moyen de deux métaheuristiques, l’algorithme génétique et la recherche tabou. Une étude théorique des propriétés de micro-lasers basés sur des molécules photoniques – constituées d’un arrangement simple de cylindres actifs – est finalement présentée. En combinant la théorie de Lorenz-Mie et SALT, nous démontrons que les propriétés physiques de ces lasers, plus spécifiquement leur seuil, leur spectre et leur profil d’émission, peuvent être affectés de façon nontriviale par les paramètres du milieu actif sous-jacent. Cette conclusion est hors d’atteinte de l’approche établie qui consiste à calculer les étatsméta-stables de l’équation de Helmholtz et leur facteur de qualité. Une perspective sur la modélisation de milieux photoniques désordonnés conclut cette dissertation.Complex photonic media mold the flow of light at the wavelength scale using multiple scattering and interference effects. This functionality at the nano-scale level paves the way for various applications, ranging from optical communications to biosensing. This thesis is mainly concerned with the numerical modeling of photonic complexes based on twodimensional arrays of cylindrical scatterers. Two applications are considered, namely the use of photonic-crystal-like devices for the design of integrated beam shaping elements, as well as active photonic molecules for the realization of compact laser sources. These photonic structures can be readily analyzed using the 2D Generalized Lorenz-Mie theory (2D-GLMT), a numerical scheme which exploits the symmetry of the underlying cylindrical structures. We begin this thesis by presenting the electromagnetic theory behind 2D-GLMT.Other useful frameworks are also presented, including a recently formulated stationary version of theMaxwell-Bloch equations called steady-state ab initio laser theory (SALT).Metaheuristics, optimization algorithms based on empirical rules for exploring large solution spaces, are also discussed. After laying down the theoretical content, we proceed to the design and optimization of beam shaping devices based on engineered photonic-crystal-like structures. The combinatorial optimization problem associated to beam shaping is tackled using the genetic algorithm (GA) as well as tabu search (TS). Our results show the possibility to design integrated beam shapers tailored for the control of the amplitude, phase and polarization profile of the output beam. A theoretical and numerical study of the lasing characteristics of photonic molecules – composed of a few coupled optically active cylinders – is also presented. Using a combination of 2D-GLMT and SALT, it is shown that the physical properties of photonic molecule lasers, specifically their threshold, spectrum and emission profile, can be significantly affected by the underlying gain medium parameters. These findings are out of reach of the established approach of computing the meta-stable states of the Helmholtz equation and their quality factor. This dissertation is concluded with a research outlook concerning themodeling of disordered photonicmedia

Using metaheuristics to improve the placement of multi-controllers in software-defined networking enabled clouds

SDN is a model that separates the control and the data levels in an arrangement to enhance capability to program and configure the network in a more agile and efficient manner. Multiple controller modules have been used in the SDN engineering to empower programmable and adaptable configurations such as improving scalability and reliability. The distance and time calculations and other performance measures have to be considered in solving the Multi-Controller Position Problem (MCPP). This paper investigates the use of metaheuristic algorithms to build an MCPP mathematical model. Both the symmetric Harmony Search (HS) modelling and the Particle Swarm Optimization (PSO) algorithm are considered in this respect. Thus, our hybrid approach is proposed and known as Harmony Search with Particle Swarm Optimization (HSPSO) is applied and we compared the extracted results with the state-of-the-art techniques in the previous literature. Besides the development of the mathematical model, a simulation study has been done considering the relevant parameters including the link distance description and the access time between the SDN entities. The console simulation uses NetBeans with CloudsimSDN procedure files in the SDN-based cloud environment

Third-order Optical Nonlinearities for Integrated Microwave Photonics Applications

The field of integrated photonics aims at compressing large and environmentally-sensitive optical systems to micron-sized circuits that can be mass-produced through existing semiconductor fabrication facilities. The integration of optical components on single chips is pivotal to the realization of miniature systems with high degree of complexity. Such novel photonic chips find abundant applications in optical communication, spectroscopy and signal processing. This work concentrates on harnessing nonlinear phenomena to this avail. The first part of this dissertation discusses, both from component and system level, the development of a frequency comb source with a semiconductor mode-locked laser at its heart. New nonlinear devices for supercontinuum and second-harmonic generations are developed and their performance is assessed inside the system. Theoretical analysis of a hybrid approach with synchronously-pumped Kerr cavity is also provided. The second part of the dissertation investigates stimulated Brillouin scattering (SBS) in integrated photonics. A fully-tensorial open-source numerical tool is developed to study SBS in optical waveguides composed of crystalline materials, particularly silicon. SBS is demonstrated in an all-silicon optical platform

Latest Advances in Nanoplasmonics and Use of New Tools for Plasmonic Characterization

Nanoplasmonics is an area that uses light to couple electrons in metals, and can break the diffraction limit for light confinement into subwavelength zones, allowing for strong field enhancements. In the last two decades, there has been a resurgence of this research topic and its applications. Thus, this Special Issue presents a collection of articles and reviews by international researchers and is devoted to the recent advances in and insights into this research topic, including plasmonic devices, plasmonic biosensing, plasmonic photocatalysis, plasmonic photovoltaics, surface-enhanced Raman scattering, and surface plasmon resonance spectroscopy

EM-driven miniaturization of high-frequency structures through constrained optimization

The trends afoot for miniaturization of high-frequency electronic devices require integration of active and passive high-frequency circuit elements within a single system. This high level of accomplishment not only calls for a cutting-edge integration technology but also necessitates accommodation of the corresponding circuit components within a restricted space in applications such as implantable devices, internet of things (IoT), or 5G communication systems. At the same time, size reduction does not remain the only demand. The performance requirements of the abovementioned systems form a conjugate demand to that of the size reduction, yet with a contrasting nature. A compromise can be achieved through constrained numerical optimization, in which two kinds of constrains may exist: equality and inequality ones. Still, the high cost of electromagnetic-based (EM-based) constraint evaluations remains an obstruction. This issue can be partly mitigated by implicit constraint handling using the penalty function approach. Nevertheless, securing its performance requires expensive guess-work-based identification of the optimum setup of the penalty coefficients. An additional challenge lies in allocating the design within or in the vicinity of a thin feasible region corresponding to equality constraints. Furthermore, multimodal nature of constrained miniaturization problems leads to initial design dependency of the optimization results. Regardless of the constraint type and the corresponding treatment techniques, the computational expenses of the optimization-based size reduction persist as a main challenge. This thesis attempts to address the abovementioned issues specifically pertaining to optimization-driven miniaturization of high frequency structures by developing relevant algorithms in a proper sequence. The first proposed approach with automated adjustment of the penalty functions is based on the concept of sufficient constraint violation improvement, thereby eliminating the costly initial trial-and-error stage for the identification of the optimum setup of the penalty factors. Another introduced approach, i.e., correction-based treatment of the equality constraints alleviates the difficulty of allocating the design within a thin feasible region where designs satisfying the equality constraints reside. The next developed technique allows for global size reduction of high-frequency components. This approach not only eliminates the aforementioned multimodality issues, but also accelerates the overall global optimization process by constructing a dimensionality-reduced surrogate model over a pre-identified feasible region as compared to the complete parameter search space. Further to the latter, an optimization framework employing multi-resolution EM-model management has been proposed to address the high cost issue. The said technique provides nearly 50 percent average acceleration of the optimization-based miniaturization process. The proposed technique pivots upon a newly-defined concept of model-fidelity control based on a combination of algorithmic metrics, namely convergence status and constraint violation level. Numerical validation of the abovementioned algorithms has also been provided using an extensive set of high-frequency benchmark structures. To the best of the author´s knowledge, the presented study is the first investigation of this kind in the literature and can be considered a contribution to the state of the art of automated high-frequency design and miniaturization

A Machine Learning-Based Methodology for in-Process Fluid Characterization With Photonic Sensors

This paper proposes a novel methodology for run-time fluid characterization through the application of machine learning techniques. It aims to integrate sophisticated multi-dimensional photonic sensors inside the chemical processes, following the Industry 4.0 paradigm. Currently, this analysis is done offline in laboratory environments, which increases the decision-making times. As an alternative, the proposed method tunes the spectralbased machine learning solutions to the requirements of each case enabling the integration of compound detection systems at the computing edge. It includes a novel feature selection strategy that combines filters and wrappers, namely Wavelength-based Hybrid Feature Selection, to select the relevant information of the spectrum (i.e., the relevant wavelengths). This technique allows providing different trade-offs involving the spectrum dimensionality, complexity, and detection quality. In terms of execution time, the provided solutions outperform the state-of-the-art up to 61.78 times using less than 99% of the wavelengths while maintaining the same detection accuracy. Also, these solutions were tested in a real-world edge platform, decreasing up to 68.57 times the energy consumption for an ethanol detection use case

Design and Optimization of Optical Devices Using Artificial Intelligence Techniques

Over the last decade, there has been a growing interest in utilizing novel photonic and optical devices for a diverse range of applications. For the next generation of wireless communication networks, the development of new and optimal optical devices is inevitable. Existing optical network infrastructure cannot meet the stringent requirements of next-generation data networks (such as a 1000-fold increase in bandwidth demand, very low latency, better spectral and energy efficiency, etc.). In other words, the physical layer of the communication network must be revolutionized to provide the proper foundation for these emerging technologies. Optical networks are based on propagating light. Light propagation in realistic settings is usually a complicated phenomenon. When it comes to the context of optical devices and its propagation in the new devices, the complexity of the problem becomes much higher. In other words, the relations between the light propagation characteristics and the structural parameters of the new devices are mostly unknown. Therefore, the conventional method for designing such devices in the absence of a clear analytic description is usually based on a trial and error process. This method has many disadvantages, being time-consuming, inefficient, and the designed device is usually far from an optimized one. Also, the designing process needs intensive human involvement. Therefore, to fill this gap, we have utilized artificial intelligence (AI) techniques to design, analyze, and optimize several different optical devices. More specifically, we have proposed several optimization frameworks for designing orbital angular momentum (OAM) fibers, large mode area photonic crystal (PhC) fibers, waveguide-based LP01 to LP0m mode converter, PhC filters, PhC sensors, and PhC-enhanced light-emitting diodes (LEDs). In all of these devices, we are dealing with a complicated system in which the relationships between the structural parameters and the output performance merit factors are very complicated. Such problems have a long simulation runtime, so it is not viable to employ an exhaustive optimization algorithm, which evaluates all of the possible combinations of the parameters to find the optimal one. Therefore, we consider our problem as a black box and use the AI optimization algorithm to find the optimal solution. Eventually, the proposed optimization frameworks open up an effective way to design high-performance optical devices for a diverse range of applications and pave the way for the development of next-generation optical devices for next-generation optical networks

The 1st International Conference on Computational Engineering and Intelligent Systems

Computational engineering, artificial intelligence and smart systems constitute a hot multidisciplinary topic contrasting computer science, engineering and applied mathematics that created a variety of fascinating intelligent systems. Computational engineering encloses fundamental engineering and science blended with the advanced knowledge of mathematics, algorithms and computer languages. It is concerned with the modeling and simulation of complex systems and data processing methods. Computing and artificial intelligence lead to smart systems that are advanced machines designed to fulfill certain specifications. This proceedings book is a collection of papers presented at the first International Conference on Computational Engineering and Intelligent Systems (ICCEIS2021), held online in the period December 10-12, 2021. The collection offers a wide scope of engineering topics, including smart grids, intelligent control, artificial intelligence, optimization, microelectronics and telecommunication systems. The contributions included in this book are of high quality, present details concerning the topics in a succinct way, and can be used as excellent reference and support for readers regarding the field of computational engineering, artificial intelligence and smart system