Multiskalensimulation des Ladungstransports in Silizium-Nanodraht-Transistoren: Evaluation der Grenzen des Simulationsmodells: Ist die Bestimmung von physikalischen Parameten aus gemessenem Strom-Spannungs-Kennlinien möglich?

Durch Multiskalensimulationen wird der Ladungstransport in nanodrahtbasierten Schottky-Barrieren-Feldeffekt-Transistoren im Materialsystem Ni2Si/Si untersucht. Die Bedingungen an die Genauigkeit der verwendeten Eingangsparameter werden bestimmt und Vorhersagen über optimale Material- und Geräteparameter werden getroffen. Es wird die Frage beantwortet, ob die Bestimmung von physikalischen Parametern aus einzelnen gemessenen Strom-Spannungs-Kennlinie möglich ist. Der Feldeffekt wird durch Berechnungen auf Basis der Finiten-Elemente-Methode und die resultierenden Stromflüsse durch ein quantenmechanisches Transportmodell ermittelt. In der Untersuchung der geometrischen Eingangsparameter wird gezeigt, dass bis auf den Radius des Nanodrahtes die in einem Experiment zu erwartenden Messfehler keinen drastischen Einfluss auf die Strom-Spannungs-Kennlinie haben. Signifikant ist hingegen der Einfluss der Temperatur, der effektiven Ladungsträgermassen und der Höhe der Schottky-Barriere. Da diese drei Eingangsparameter des betrachteten Systems mit relativ großen Ungenauigkeiten behaftet sind, ist die Bestimmung von physikalischen Parametern aus einzelnen gemessenen Strom-Spannungs-Kennlinien auf die erhoffte Weise nicht möglich. Die Arbeit zeigt auch, dass bereits moderate Veränderungen der Arbeitstemperatur einen bedeutenden Einfluss auf die Strom-Spannungs-Kennlinie haben. Für die Konstruktion von Transistoren mit hoher Stromdichte kann anhand der ermittelten Daten die Verkleinerung der aktiven Region durch Oxidation vorgeschlagen werden.:Kurzfassung/Abstract I Verwendete Symbole IV Verwendete Parameter VI Verwendete Abkürzungen VII 1 Motivation 8 2 Grundlagen 9 2.1 Modellbildung und Simulation 9 2.2 Schottky-Diode 10 2.3 Feldeffekt-Transistor 12 2.4 Feldeffekt-Transistor auf der Basis von Silizium-Nanodrähten 13 3 Methoden 17 3.1 Simulationsmodell 17 3.2 Finite-Elemente-Methode 20 3.3 Landauer-Büttiker-Formalismus 21 3.4 Hamiltonoperator 22 3.5 Transmissionsfunktion 23 3.6 Büttiker Sonde 24 4 Ergebnisse und Diskussion 26 4.1 Implementierung des Simulationsprogrammes 26 4.2 Berechnung der Basis-Strom-Spannungs-Kennlinie 31 4.3 Wahl der Simulationsparameter 35 4.4 Abhängigkeit von geometrischen Parametern 41 4.5 Abhängigkeit von physikalischen Parametern 49 5 Zusammenfassung, Schlussfolgerungen und Ausblick 55 Abbildungsverzeichnis 59 Literatur 62Charge transport in nanowire-based Schottky-barrier field-effect transistors in the material system Ni2Si/Si is examined by multi-scale simulations. The requirements for the accuracy of the input parameters are determined and predictions about optimum material and device parameters are made. The question is answered, whether the determination of physical parameters from individual measured current-voltage curves is possible? The field effect is described by calculations based on the finite element method and the resulting currents are calculated with a quantum mechanical transport model. In the study of the geometric input parameters it is shown that experimental uncertainties do not drastically affect the current-voltage characteristic, except from the nanowire radius. However, significant is the influence of the temperature, the effective charge carrier mass and the height of the Schottky-barrier. Since these three input parameters are known only with low experimental accuracy for the considered system, the determination of physical parameters from individual measured current-voltage curves is not possible in the expected way. The results also show that moderate changes of the working temperature have a significant influence on the current-voltage characteristic. For the construction of transistors with high current density the reduction of the active region by oxidation is proposed.:Kurzfassung/Abstract I Verwendete Symbole IV Verwendete Parameter VI Verwendete Abkürzungen VII 1 Motivation 8 2 Grundlagen 9 2.1 Modellbildung und Simulation 9 2.2 Schottky-Diode 10 2.3 Feldeffekt-Transistor 12 2.4 Feldeffekt-Transistor auf der Basis von Silizium-Nanodrähten 13 3 Methoden 17 3.1 Simulationsmodell 17 3.2 Finite-Elemente-Methode 20 3.3 Landauer-Büttiker-Formalismus 21 3.4 Hamiltonoperator 22 3.5 Transmissionsfunktion 23 3.6 Büttiker Sonde 24 4 Ergebnisse und Diskussion 26 4.1 Implementierung des Simulationsprogrammes 26 4.2 Berechnung der Basis-Strom-Spannungs-Kennlinie 31 4.3 Wahl der Simulationsparameter 35 4.4 Abhängigkeit von geometrischen Parametern 41 4.5 Abhängigkeit von physikalischen Parametern 49 5 Zusammenfassung, Schlussfolgerungen und Ausblick 55 Abbildungsverzeichnis 59 Literatur 6

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Newton, in notes that he would rather not have seen published, described a process for solving simultaneous equations that later authors applied specifically to linear equations. This method that Euler did not recommend, that Legendre called "ordinary," and that Gauss called "common" - is now named after Gauss: "Gaussian" elimination. Gauss's name became associated with elimination through the adoption, by professional computers, of a specialized notation that Gauss devised for his own least squares calculations. The notation allowed elimination to be viewed as a sequence of arithmetic operations that were repeatedly optimized for hand computing and eventually were described by matrices.Comment: 56 pages, 21 figures, 1 tabl

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Engineering improved ethylene production: Leveraging systems Biology and adaptive laboratory evolution

Ethylene is a small hydrocarbon gas widely used in the chemical industry. Annual worldwide production currently exceeds 150 million tons, producing considerable amounts of CO2 contributing to climate change. The need for a sustainable alternative is therefore imperative. Ethylene is natively produced by several different microorganisms, including Pseudomonas syringae pv. phaseolicola via a process catalyzed by the ethylene forming enzyme (EFE), subsequent heterologous expression of EFE has led to ethylene production in non-native bacterial hosts including E. coli and cyanobacteria. However, solubility of EFE and substrate availability remain rate limiting steps in biological ethylene production. We employed a combination of genome scale metabolic modelling, continuous fermentation, and protein evolution to enable the accelerated development of a high efficiency ethylene producing E. coli strain, yielding a 49-fold increase in production, the most significant improvement reported to date. Furthermore, we have clearly demonstrated that this increased yield resulted from metabolic adaptations that were uniquely linked to the EFE enzyme (WT vs mutant). Our findings provide a novel solution to deregulate metabolic bottlenecks in key pathways, which can be readily applied to address other engineering challenges