In this study, the effect of AlN buffer layer structure and morphology on the GaN films deposited on Si (111) substrate by reactive DC magnetron sputtering have been studied. For structural and morphological characterization X-ray diffraction (XRD) and Scanning electron microscope (SEM) were used. It is found that AlN films without Al pre-deposition on Si have a poor crystalline quality due to the formation of an amorphous layer at Si/AlN interface. Various initial Al sputtering durations on Si substrate result in changing the AlN buffer layer’s quality and morphology and choosing proper deposition duration can improve AlN buffer layer and the subsequent GaN layer quality. It is found that more than 15 s Al pre deposition can suppress the amorphous layer formation and enhance the film quality. The Rocking curve FWHM for both in-plane and out-of-plane orientation decreased from 1.86 and 1.56   to 0.4   and 0.3  , respectively, by 4 minutes Al pre-deposition on Si, however, overlong Al deposition results in poor surface morphology. It is found that 15s Al deposition is sufficient to enhance the quality as well as keeping the surface relatively smooth. In addition, AlN films deposited at 1000   showed the best film quality. GaN films directly grown on Si showed a poor crystalline quality and surface morphology, improvement of quality and morphology was observed for GaN samples deposited on AlN buffer layer. Also, melt-back etching observed on the surface of GaN grown on Si without AlN buffer layer. It is found that duration of Al deposition has a significant effect on GaN surface morphology, Al layer deposition more than 15 s result in a rough surface for GaN films. With choosing the optimum duration of Al pre-deposition and proper growth temperature a good quality GaN with a smooth surface morphology can be produced.

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Several sputtering depositions were done by direct current (DC) magnetron sputtering epitaxy (MSE) techniquefor the goal of improving the growth rate and crystalline quality of GaN thin film on Al2O3 substrate. Thegrowth rate was higher when substrate-to-target distance D = 7 cm compared with D = 9.3 cm with eitherfloating or positive bias on the substrate side. The crystalline quality was improved by raising up the growthtemperature from 700◦C to 900◦C, but the quality was declined from 900◦C to 1000◦C due to strong desorption.Gas composition in the metal mode gives better quality due to its sufficient Ga condition with less N2. Positivesubstrate bias boosted the plasma potential and therefore created higher actual sputtering power comparedwith the condition at floating substrate potential. In general, applying a higher power can elevate the growthrate and film quality. However, there has not been an evident difference of both growth rate and film qualitywhen the actual sputtering power is close for floating substrate potential and positive substrate bias.

Phase transitions are fundamental phenomena in (quantum) many-body systems. They are associated with changes in the macroscopic physical properties of the system in response to the alteration in the conditions controlled by one or more parameters, like temperature or coupling constants. Quantum phase transitions are particularly intriguing as they reveal new insights into the fundamental nature of matter and the laws of physics. The study of phase transitions in such systems is crucial in aiding our understanding of how materials behave in extreme conditions, which are difficult to replicate in laboratory, and also the behavior of exotic states of matter with unique and potentially useful properties like superconductors and superfluids. Moreover, this understanding has other practical applications and can lead to the development of new materials with specific properties or more efficient technologies, such as quantum computers. Hence, detecting the transition point from one phase of matter to another and constructing the corresponding phase diagram is of great importance for examining many-body systems and predicting their response to external perturbations. Traditionally, phase transitions have been identified either through analytical methods like mean field theory or numerical simulations. The pinpointing of the critical value normally involves the measure of specific quantities such as local observables, correlation functions, energy gaps, etc. reflecting the changes in the physics through the transition. However, the latter approach requires prior knowledge of the system to calculate the order parameter of the transition, which is uniquely associated to its universality class. Recently, another method has gained more and more attention in the physics community. By using raw and very general representative data of the system, one can resort to machine learning techniques to distinguish among patterns within the data belonging to different phases. The relevance of these techniques is rooted in the ability of a properly trained machine to efficiently process complex data for the sake of pursuing classification tasks, pattern recognition, generating brand new data and even developing decision processes. The aim of this thesis is to explore phase transitions from this new and promising data-centric perspective. On the one hand, our work is focused on the developement of new machine learning architectures using state-of-the-art and interpretable models. On the other hand, we are interested in the study of the various possible data which can be fed to the artificial intelligence model for the mapping of a quantum many-body system phase diagram. Our analysis is supported by numerical examples obtained via matrix-product-states (MPS) simulations for several one-dimensional zero-temperature systems on a lattice such as the XXZ model, the Extended Bose-Hubbard model (EBH) and the two-species Bose Hubbard model (BH2S). In Part I, we provide a general introduction to the background concepts for the understanding of the physics and the numerical methods used for the simulations and the analysis with deep learning. In Part II, we first present the models of the quantum many-body systems that we study. Then, we discuss the machine learning protocol to identify phase transitions, namely anomaly detection technique, that involves the training of a model on a dataset of normal behavior and use it to recognize deviations from this behavior on test data. The latter can be applied for our purpose by training in a known phase so that, at test-time, all the other phases of the system are marked as anomalies. Our method is based on Generative Adversarial Networks (GANs) and improves the networks adopted by the previous works in the literature for the anomaly detection scheme taking advantage of the adversarial training procedure. Specifically, we train the GAN on a dataset composed of bipartite entanglement spectra (ES) obtained from Tensor Network simulations for the three aforementioned quantum systems. We focus our study on the detection of the elusive Berezinskii-Kosterlitz-Thouless (BKT) transition that have been object of intense theoretical and experimental studies since its first prediction for the classical two-dimensional XY model. The absence of an explicit symmetry breaking and its gappless-to-gapped nature which characterize such a transition make the latter very subtle to be detected, hence providing a challenging testing ground for the machine-driven method. We train the GAN architecture on the ES data in the gapless side of BKT transition and we show that the GAN is able to automatically distinguish between data from the same phase and beyond the BKT. The protocol that we develop is not supposed to become a substitute to the traditional methods for the phase transitions detection but allows to obtain a qualitative map of a phase diagram with almost no prior knowledge about the nature and the arrangement of the phases -- in this sense we refer to it as agnostic -- in an automatic fashion. Furthermore, it is very general and it can be applied in principle to all kind of representative data of the system coming both from experiments and numerics, as long as they have different patterns (even hidden to the eye) in different phases. Since the kind of data is crucially linked with the success of the detection, together with the ES we investigate another candidate: the probability density function (PDF) of a globally U(1) conserved charge in an extensive sub-portion of the system. The full PDF is one of the possible reductions of the ES which is known to exhibit relations and degeneracies reflecting very peculiar aspects of the physics and the symmetries of the system. Its patterns are often used to tell different kinds of phases apart and embed information about non-local quantum correlations. However, the PDF is measurable, e.g. in quantum gas microscopes experiments, and it is quite general so that it can be considered not only in the cases of the study but also in other systems with different symmetries and dimensionalities. Both the ES and the PDF can be extracted from the simulation of the ground state by dividing the one-dimensional chain into two complementary subportions. For the EBH we calculate the PDF of the bosonic occupation number in a wide range of values of the couplings and we are able to reproduce the very rich phase diagram containing several phases (superfluid, Mott insulator, charge density wave, phase separation of supersolid and superfluid and the topological Haldane insulator) just with an educated gaussian fit of the PDF. Even without resorting to machine learning, this analysis is instrumental to show the importance of the experimentally accessible PDF for the task. Moreover, we highlight some of its properties according to the gapless and gapped nature of the ground state which require a further investigation and extension beyond zero-temperature regimes and one-dimensional systems. The last chapter of the results contains the description of another architecture, namely the Concrete Autoencoder (CAE) which can be used for detecting phase transitions with the anomaly detection scheme while being able to automatically learn what the most relevant components of the input data are. We show that the CAE can recognize the important eigenvalues out of the entire ES for the EBH model in order to characterize the gapless phase. Therefore the latter architecture can be used to provide not only a more compact version of the input data (dimensionality reduction) -- which can improve the training -- but also some meaningful insights in the spirit of machine learning interpretability. In conclusion, in this thesis we describe two advances in the solution to the problem of phase recognition in quantum many-body systems. On one side, we improve the literature standard anomaly detection protocol for an automatic and agnostic identification of the phases by employing the GAN network. Moreover, we implement and test an explainable model which can make the interpretation of the results easier. On the other side we put the focus on the PDF as a new candidate quantity for the scope of discerning phases of matter. We show that it contains a lot of information about the many-body state being very general and experimentally accessible.

<p>Management of incoming solar radiation and use of the sky as an ultimate heat sink are technological imperatives as climate change shifts our reliance from fossil fuels to sustainable sources.  Selective solar absorbers are a possible route for solar harvesting as they collect the incoming radiation for process heat or space heating.  Here, improvement in the performance of selective solar absorbers via photon recycling is investigated using a stepped index rugate filter.  The final proposed filter when integrated with a high vacuum selective solar absorber could see an improvment in solar-thermal conversion efficiency from 13% to 30.6%. Then, a frequency selective optical filter is fabricated with uses including improvement of radiative coolers.  The measured optical characteristics are compared with simulation data and found to match well.</p> <p><br></p> <p>The shift to sustainable sources of electricity will require an expansion of the electrical grid.  The backbone of the grid for converting high voltage AC to DC, and vice versa, is power electronics.  The current state-of-the-art technology is GaN HEMTs, but GaN MISHEMTs are poised to replace them since MISHEMTs reduce the gate leakage current; a deficiency of the GaN HEMT architecture.  First, time dependent dielectric breakdown in GaN MISHEMTs is investigated using concurrent electrical and thermoreflectance methods.  A susceptibility in the MISHEMT architecture is found and possible solutions are proposed.  Then, liquid cooling of GaN HEMT PAs is explored by demonstrating integration of an X-band front end module, printed circuit board, and fluid manifold.  The integration shows great promise as two-phase cooling performance improved with increasing power dissipated, while single-phase cooling performance degraded.</p>

Zur Ermittlung der Zuverlässigkeit von leistungselektronischen Bauelementen sind eine Reihe von Testverfahren etabliert. In Lastwechseltests ist die Temperatur der dominierende Parameter für bekannte Lebensdauermodelle. Aufgrund des Aufbaus und der Eigenschaften von AlGaN/GaN-Bauelementen ist es notwendig, neue Methoden zur Temperaturbestimmung zu etablieren. Die Untersuchungen berücksichtigen dabei verschiedene Bauteilkonzepte. Dazu gehören High Electron Mobility Transistors (HEMT) mit Schottky/p-Gate, für die eine Verwendung des Gateleckstromes als temperatursensitiver elektrischer Parameter (TSEP) untersucht und zur Temperaturbestimmung empfohlen wird. Für Gate Injection Transistors (GIT) wird ein ähnlicher Ansatz verfolgt. Aufgrund der Gatestruktur dieser stromgesteuerten Bauelemente wird vorgeschlagen, den vorhandenen pn-Übergang am Gate des GIT HEMT unter Verwendung der Gate-Source-Spannung als TSEP zu nutzen. In beiden Fällen erreichen die temperatursensitiven Parameter eine Messauflösung, die mindestens der des pn-Übergangs bei Si-Bauelementen entspricht. Im Lastwechseltest bestimmt im Wesentlichen die verwendete Aufbau- und Verbindungstechnik außerhalb des diskreten Packages die mögliche Zyklenzahl. Werden SMD-Bauelemente auf PCB gelötet, dominiert die Lotverbindung zwischen Bauteil und PCB den Ausfall. Durch ein neues Aufbaukonzept mit in Module gesinterten AlGaN/GaN Packages sind Zyklenzahlen möglich, die bis Faktor 10 über dem Erwartungswert für vergleichbare Si-Bauelemente mit Standard AVT liegen.

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