Latest Advancements in Micro Nano Molding Technologies – Process Developments and Optimization, Materials, Applications, Key Enabling Technologies

Micro- and nano-molding technologies are continuously being developed due to enduring trends like increasing miniaturization and higher functional integration of products, devices, and systems. Furthermore, with the introduction of higher performance polymers, feedstocks, and composites, new opportunities in terms of material properties can be exploited, and, consequently, more micro-products and micro/nano-structured surfaces are currently being designed and manufactured.Innovations in micro- and nano-molding techniques are seen in the different processes employed in production (injection molding, micro injection molding, etc.); on the use of new and functional materials; for an ever-increasing number of applications (health-care devices, micro-implants, mobility, and communications products, optical elements, micro-electromechanical systems, sensors, etc.); in several key enabling technologies that support the successful realization of micro and nano molding processes (micro- and nano-tooling technologies, process monitoring techniques, micro- and nanometrology methods for quality control, simulation, etc.) and their integration into new manufacturing process chains.This Special Issue reprint showcases research papers and review articles that focus on the latest developments in micro-manufacturing and key enabling technologies for the production of both micro-products and micro-structured surfaces

Uma abordagem baseada em redes neurais artificiais para computação de propriedades ópticas de cristais fotônicos

Orientadores: Hugo Enrique Hernández-Figueroa, Gilliard Nardel Malheiros SilveiraTese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia Elétrica e de ComputaçãoResumo: Esta tese aborda o emprego de processos baseados em redes neurais artificiais para computação de relações de dispersão e banda fotônica proibida de cristais fotônicos. A proposta objetiva prover um modelo de computação alternativo capaz de calcular rapidamente estas propriedades ópticas em relação às simulações eletromagnéticas convencionais. O modelo é baseado nas redes neurais artificiais Perceptron de Múltiplas Camadas e Máquinas de Aprendizado Extremo, que são projetadas para processarem dados geométricos e de materiais de cristais fotônicos e assim predizerem estas propriedades ópticas. Uma arquitetura simples de rede neural é proposta para permitir processos rápidos de treinamento. O modelo é testado em uma variedade de cristais fotônicos bi- and tri-dimensionais com arranjos, geometrias, e materiais diferentes, e sua capacidade de predição e desempenho de computação são avaliados em relação a um simulador eletromagnético bem estabelecido na comunidade de fotônicaAbstract: This thesis addresses the employment of Artificial Neural Network-based processes for computing dispersion relations and photonic bandgaps of photonic crystals. The proposal aims to provide an alternative computing model able to fastly calculate these optical properties regarding conventional electromagnetic simulations. The model is based on Multilayer Perceptron and Extreme Learning Machine Artificial Neural Networks, which are designed to process the geometric and material data of photonic crystals in order to predict such optical properties. A simple neural-network architecture is proposed for allowing fast training processes. The model is tested on a variety of bi- and tri-dimensional photonic crystals with different lattices, geometries, and materials, and its predicting capability and computing performance are evaluated in regard to a well-established electromagnetic simulator in photonic communityDoutoradoTelecomunicações e TelemáticaDoutor em Engenharia ElétricaCAPE

Next Generation Silicon Photonic Transceiver: From Device Innovation to System Analysis

Silicon photonics is recognized as a disruptive technology that has the potential to reshape many application areas, for example, data center communication, telecommunications, high-performance computing, and sensing. The key capability that silicon photonics offers is to leverage CMOS-style design, fabrication, and test infrastructure to build compact, energy-efficient, and high-performance integrated photonic systems-on- chip at low cost. As the need to squeeze more data into a given bandwidth and a given footprint increases, silicon photonics becomes more and more promising. This work develops and demonstrates novel devices, methodologies, and architectures to resolve the challenges facing the next-generation silicon photonic transceivers. The first part of this thesis focuses on the topology optimization of passive silicon photonic devices. Specifically, a novel device optimization methodology - particle swarm optimization in conjunction with 3D finite-difference time-domain (FDTD), has been proposed and proven to be an effective way to design a wide range of passive silicon photonic devices. We demonstrate a polarization rotator and a 90◦ optical hybrid for polarization-diversity and phase-diversity communications - two important schemes to increase the communication capacity by increasing the spectral efficiency. The second part of this thesis focuses on the design and characterization of the next- generation silicon photonic transceivers. We demonstrate a polarization-insensitive WDM receiver with an aggregate data rate of 160 Gb/s. This receiver adopts a novel architecture which effectively reduces the polarization-dependent loss. In addition, we demonstrate a III-V/silicon hybrid external cavity laser with a tuning range larger than 60 nm in the C-band on a silicon-on-insulator platform. A III-V semiconductor gain chip is hybridized into the silicon chip by edge-coupling to the silicon chip. The demonstrated packaging method requires only passive alignment and is thus suitable for high-volume production. We also demonstrate all silicon-photonics-based transmission of 34 Gbaud (272 Gb/s) dual-polarization 16-QAM using our integrated laser and silicon photonic coherent transceiver. The results show no additional penalty compared to commercially available narrow linewidth tunable lasers. The last part of this thesis focuses on the chip-scale optical interconnect and presents two different types of reconfigurable memory interconnects for multi-core many-memory computing systems. These reconfigurable interconnects can effectively alleviate the memory access issues, such as non-uniform memory access, and Network-on-Chip (NoC) hot-spots that plague the many-memory computing systems by dynamically directing the available memory bandwidth to the required memory interface