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            Chapter Topological Characterization and Advanced Noise-Filtering Techniques for Phase Unwrapping of Interferometric Data Stacks

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            Author(s)
            Imperatore, Pasquale
            Pepe, Antonio
            Language
            English
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            Abstract
            Semiconductor optical amplifiers (SOAs) have been extensively used in a wealth of telecom and datacom applications as a powerful building block that features large optical gain, all-optical gating function, fast response, and ease of integration with other functional semiconductor devices. As fabrication technologies are steadily maturing toward enhanced yield, SOAs are foreseen to play a pivotal role in complex photonics integrated circuits (PICs) of the near future. From a design standpoint, accurate numerical modeling of SOA devices is required toward optimizing PICs response from a system perspective, while enhanced circuit complexity calls for efficient solvers. In this book chapter, we present established experimentally validated SOA numerical modeling techniques and a gain parameterization procedure applicable to a wide range of SOA devices. Moreover, we describe multigrid concepts and implicit schemes that have been only recently presented to SOA modeling, enabling adaptive time stepping at the SOA output, with dense sampling at transient phenomena during the gain recovery and scarce sampling during the steady-state response. Overall, a holistic simulation methodology approach along with recent research trends are described, aiming to form the basis of further developments in SOA modeling.
            URI
            https://doab-dev.siscern.org/handle/20.500.12854/163209
            Keywords
            Semiconductor optical amplifier, numerical modeling, transfer matrix method, multigrid techniques
            DOI
            10.5772/61847
            Publisher
            InTechOpen
            Publication date and place
            2016
            Classification
            Applied optics
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              This project received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 871069.

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