239 research outputs found

    Free-space holographic optical interconnects in dichromated gelatin

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    Silicon photonic MEMS switches based on split waveguide crossings

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    The continuous push for high-performance photonic switches is one of the most crucial premises for the sustainable scaling of programmable and reconfigurable photonic circuits for a wide spectrum of applications. Large-scale photonic switches constructed with a large number of 2×\times2 elementary switches impose stringent requirements on the elementary switches. In contrast to conventional elementary switches based on mode interference or mode coupling, here we propose and realize a brand-new silicon MEMS 2×\times2 elementary switch based on a split waveguide crossing (SWX) consisting of two halves. With this structure, the propagation direction of the incident light can be manipulated to implement the OFF and ON states by splitting or combining the two halves of the SWX, respectively. More specifically, we introduce refractive-index engineering by incorporating subwavelength-tooth (SWT) structures on both reflecting facets to further reduce the excess loss in the ON state. Such a unique switching mechanism features a compact footprint on a standard SOI wafer and enables excellent photonic performance with low excess loss of 0.1-0.52/0.1-0.47dB and low crosstalk of <\lt-37/-22.5dB over an ultrawide bandwidth of 1400-1700nm for the OFF/ON states in simulation, while in experiment, excess loss of 0.15-0.52/0.42-0.66dB and crosstalk of <\lt-45.5/-25dB over the bandwidth of 1525-1605 nm for the OFF/ON states have been measured.Furthermore, excellent MEMS characteristics such as near-zero steady-state power consumption, low switching energy of sub-pJ, switching speed of {\mu}s-scale, durability beyond 10^9 switching cycles, and overall device robustness have been achieved. Finally, a 16×\times16 switch using Benes topology has also been fabricated and characterized as a proof of concept, further validating the suitability of the SWX switches for large-scale integration

    Conference on Binary Optics: An Opportunity for Technical Exchange

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    The papers herein were presented at the Conference on Binary Optics held in Huntsville, AL, February 23-25, 1993. The papers were presented according to subject as follows: modeling and design, fabrication, and applications. Invited papers and tutorial viewgraphs presented on these subjects are included

    High capacity photonic integrated switching circuits

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    As the demand for high-capacity data transfer keeps increasing in high performance computing and in a broader range of system area networking environments; reconfiguring the strained networks at ever faster speeds with larger volumes of traffic has become a huge challenge. Formidable bottlenecks appear at the physical layer of these switched interconnects due to its energy consumption and footprint. The energy consumption of the highly sophisticated but increasingly unwieldy electronic switching systems is growing rapidly with line rate, and their designs are already being constrained by heat and power management issues. The routing of multi-Terabit/second data using optical techniques has been targeted by leading international industrial and academic research labs. So far the work has relied largely on discrete components which are bulky and incurconsiderable networking complexity. The integration of the most promising architectures is required in a way which fully leverages the advantages of photonic technologies. Photonic integration technologies offer the promise of low power consumption and reduced footprint. In particular, photonic integrated semiconductor optical amplifier (SOA) gate-based circuits have received much attention as a potential solution. SOA gates exhibit multi-terahertz bandwidths and can be switched from a high-gain state to a high-loss state within a nanosecond using low-voltage electronics. In addition, in contrast to the electronic switching systems, their energy consumption does not rise with line rate. This dissertation will discuss, through the use of different kind of materials and integration technologies, that photonic integrated SOA-based optoelectronic switches can be scalable in either connectivity or data capacity and are poised to become a key technology for very high-speed applications. In Chapter 2, the optical switching background with the drawbacks of optical switches using electronic cores is discussed. The current optical technologies for switching are reviewed with special attention given to the SOA-based switches. Chapter 3 discusses the first demonstrations using quantum dot (QD) material to develop scalable and compact switching matrices operating in the 1.55”m telecommunication window. In Chapter 4, the capacity limitations of scalable quantum well (QW) SOA-based multistage switches is assessed through experimental studies for the first time. In Chapter 5 theoretical analysis on the dependence of data integrity as ultrahigh line-rate and number of monolithically integrated SOA-stages increases is discussed. Chapter 6 presents some designs for the next generation of large scale photonic integrated interconnects. A 16x16 switch architecture is described from its blocking properties to the new miniaturized elements proposed. Finally, Chapter 7 presents several recommendations for future work, along with some concluding remark

    Reconstruction from Spatio-Spectrally Coded Multispectral Light Fields

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    In dieser Arbeit werden spektral codierte multispektrale Lichtfelder, wie sie von einer Lichtfeldkamera mit einem spektral codierten Mikrolinsenarray aufgenommen werden, untersucht. FĂŒr die Rekonstruktion der codierten Lichtfelder werden zwei Methoden entwickelt und im Detail ausgewertet. ZunĂ€chst wird eine vollstĂ€ndige Rekonstruktion des spektralen Lichtfelds entwickelt, die auf den Prinzipien des Compressed Sensing basiert. Um die spektralen Lichtfelder spĂ€rlich darzustellen, werden 5D-DCT-Basen sowie ein Ansatz zum Lernen eines Dictionary untersucht. Der konventionelle vektorisierte Dictionary-Lernansatz wird auf eine tensorielle Notation verallgemeinert, um das Lichtfeld-Dictionary tensoriell zu faktorisieren. Aufgrund der reduzierten Anzahl von zu lernenden Parametern ermöglicht dieser Ansatz grĂ¶ĂŸere effektive AtomgrĂ¶ĂŸen. Zweitens wird eine auf Deep Learning basierende Rekonstruktion der spektralen Zentralansicht und der zugehörigen DisparitĂ€tskarte aus dem codierten Lichtfeld entwickelt. Dabei wird die gewĂŒnschte Information direkt aus den codierten Messungen geschĂ€tzt. Es werden verschiedene Strategien des entsprechenden Multi-Task-Trainings verglichen. Um die QualitĂ€t der Rekonstruktion weiter zu verbessern, wird eine neuartige Methode zur Einbeziehung von Hilfslossfunktionen auf der Grundlage ihrer jeweiligen normalisierten GradientenĂ€hnlichkeit entwickelt und gezeigt, dass sie bisherige adaptive Methoden ĂŒbertrifft. Um die verschiedenen RekonstruktionsansĂ€tze zu trainieren und zu bewerten, werden zwei DatensĂ€tze erstellt. ZunĂ€chst wird ein großer synthetischer spektraler Lichtfelddatensatz mit verfĂŒgbarer DisparitĂ€t Ground Truth unter Verwendung eines Raytracers erstellt. Dieser Datensatz, der etwa 100k spektrale Lichtfelder mit dazugehöriger DisparitĂ€t enthĂ€lt, wird in einen Trainings-, Validierungs- und Testdatensatz aufgeteilt. Um die QualitĂ€t weiter zu bewerten, werden sieben handgefertigte Szenen, so genannte Datensatz-Challenges, erstellt. Schließlich wird ein realer spektraler Lichtfelddatensatz mit einer speziell angefertigten spektralen Lichtfeldreferenzkamera aufgenommen. Die radiometrische und geometrische Kalibrierung der Kamera wird im Detail besprochen. Anhand der neuen DatensĂ€tze werden die vorgeschlagenen RekonstruktionsansĂ€tze im Detail bewertet. Es werden verschiedene Codierungsmasken untersucht -- zufĂ€llige, regulĂ€re, sowie Ende-zu-Ende optimierte Codierungsmasken, die mit einer neuartigen differenzierbaren fraktalen Generierung erzeugt werden. DarĂŒber hinaus werden weitere Untersuchungen durchgefĂŒhrt, zum Beispiel bezĂŒglich der AbhĂ€ngigkeit von Rauschen, der Winkelauflösung oder Tiefe. Insgesamt sind die Ergebnisse ĂŒberzeugend und zeigen eine hohe RekonstruktionsqualitĂ€t. Die Deep-Learning-basierte Rekonstruktion, insbesondere wenn sie mit adaptiven Multitasking- und Hilfslossstrategien trainiert wird, ĂŒbertrifft die Compressed-Sensing-basierte Rekonstruktion mit anschließender DisparitĂ€tsschĂ€tzung nach dem Stand der Technik
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