08 Fakultät Mathematik und Physik
Permanent URI for this collectionhttps://elib.uni-stuttgart.de/handle/11682/9
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Item Open Access Exploring the growth of refractory metal and sapphire films by thermal laser epitaxy(2024) Majer, Lena N.; Mannhart, Jochen (Prof. Dr.)Item Open Access Self-organized structures and excitations in dipolar quantum fluids(2024) Hertkorn, Jens; Pfau, Tilman (Prof. Dr.)Quantum many-body phenomena at a macroscopic scale, such as superfluidity and superconductivity, are rooted in the interplay between microscopic particles, governed by the laws of quantum mechanics. Exploring how this interplay leads to quantum behavior at a large scale allows us to gain a deeper understanding of nature and to discover new quantum phases. An elusive quantum phase in which the frictionless flow of superfluids and the crystal structure of solids coexists - the supersolid - was recently realized with quantum droplets in dipolar Bose-Einstein condensates. In this thesis we investigate self-organized structures, their formation mechanism, and excitations in dipolar quantum fluids created from such Bose-Einstein condensates. We show that the supersolid formation mechanism is driven by density fluctuations due to low-energy roton excitations, leading to a crystal structure of quantum droplets that are immersed in a superfluid background. These roton excitations split into a Goldstone mode and a Higgs amplitude mode, associated to the broken translational symmetry in the supersolid. We investigate the symmetry breaking of dipolar quantum fluids in a range of confinement geometries and establish a comprehensive description of elementary excitations across the superfluid to supersolid droplet phase transition. The droplets are stabilized by an interplay between interactions and the presence of quantum fluctuations. We show how this interplay can be used to find regimes where droplets are immersed in a high superfluid background, allowing for frictionless flow throughout the crystal. Moreover we show that towards higher densities beyond the quantum droplet phase, this interplay leads to several new self-organized structures in the phase diagram of dipolar quantum fluids. We theoretically predict new supersolid honeycomb, amorphous labyrinth, and other phases in oblate dipolar quantum fluids. Finally, we present a new experimental setup for the exploration of self-organized phases in dipolar quantum fluids and which also lays the foundation for the implementation of a quantum gas microscope. The results of this thesis present a complete framework for understanding and creating exotic phases in dipolar quantum fluids. The versatile structure formation, governed by a competition of controllable interactions and the presence of quantum fluctuations, positions dipolar quantum fluids as a model system for exploring self-organized equilibrium in weakly-interacting quantum many-body systems.Item Open Access Nanoscale magnetic resonance spectroscopy with nitrogen-vacancy centers in diamond(2021) Paone, Domenico; Wrachtrup, Jörg (Prof. Dr.)Stickstoff-Fehlstellen (NV-Zentren) in Diamant bilden interessante Quantensysteme, welche für Quanten-Sensing Protokolle genutzt werden können. In der vorliegenden Arbeit, werden NV-Zentren genutzt, um einzelne Molekülsysteme auszulesen und supraleitende Proben lokal zu charakterisieren. Zusätzlich werden Methoden entwickelt, um die Spineigenschaften der NV-Zentren zu optimieren, welche dann Einfluss auf das Sensorikverhalten des Systems haben.Item Open Access Long wave approximation over and beyond the natural time scale(2024) Hofbauer, Sarah; Schneider, Guido (Prof. Dr.)Item Open Access Polarized neutron reflectometry study of complex magnetism and hydrogen incorporation in thin-film structures(2022) Guasco, Laura; Keimer, Bernhard (Prof. Dr.)In this thesis, we present the study of the structural and magnetic properties of simple metals and complex oxide thin films by means of polarized neutron reflectometry. The nuclear and electronic properties of thin films were modified via two routes, namely via hydrogen incorporation, in the case of niobium systems and complex oxide layers, and via depth modulated hole doping, in the case of manganite heterostructures.Item Open Access Thermodynamic uncertainty relation for stochastic field theories : general formulation and application to the Kardar-Parisi-Zhang equation(2022) Niggemann, Oliver; Seifert, Udo (Prof. Dr.)Item Open Access Real-space spectroscopy of interacting quasiparticles in exotic semimetals(2022) He, Qingyu; Takagi, Hidenori (Prof. Dr.)Item Open Access High quality graphene for magnetic sensing(2022) Herlinger, Patrick; Smet, Jurgen (Dr.)In this thesis, we investigated the reliable fabrication of high quality graphene and its use as Hall transducer material. Charged impurities and random strain fluctuations were identified as main culprits that deteriorate the electrical properties of graphene devices. It was shown that these extrinsic sources of disorder can be reduced through optimized device processing steps as well as the use of a proper substrate material for graphene such as hexagonal boron nitride (hBN). This insulating material is atomically flat and possesses a very low intrinsic density of charged impurities. By performing Raman spectroscopy and electrical transport measurements, both without and with applied magnetic field, on a large number of different types of graphene devices, it was demonstrated that the encapsulation of graphene between hexagonal boron nitride thin films is the best way to obtain high quality graphene devices. However, even for these hBN-encapsulated devices, we still observed a notable sample-to-sample variation of the electrical properties. Therefore, we developed a post-processing technique that allows us to improve the electrical properties of such devices both significantly and reliably. Since our technique is applied after device fabrication, we could also demonstrate its beneficial effect by comparing one and the same device before and after treatment. We then assessed the application of such high quality graphene as Hall transducer material. The dependencies on and between all relevant operating parameters were explored. This allowed us to develop a deep understanding and empirical model for graphene Hall elements, including the interplay between thermal and 1/f noise in these devices. All key performance indicators for Hall sensors were measured and their typical values reported. For comparable device dimensions, hBN-encapsulated graphene Hall elements were found to have the potential to become a strong competitor to existing materials that are used in today's commercial Hall sensors. Unfortunately, the large-scale fabrication of hBN thin films still remains an unresolved challenge for the industrialization of large area, high quality graphene Hall elements. Also, the Si CMOS integration demands further development. Even though the application of graphene in Hall devices is promising, as shown in this work, this use case alone does likely not justify the significant efforts and investments we expect to be necessary to industrialize the fabrication of high quality graphene devices. Instead, these efforts and costs must be shared by developing a common technology platform for 2D materials that can address several commercially attractive applications where graphene or another 2D material offers superior performance as well. We hope that the insights provided in this work can help to accelerate this process.Item Open Access Quantifying quantum heterodyne and non-linear spectroscopy for nanoscale magnetic resonance(2022) Meinel, Jonas; Wrachtrup, Jörg (Prof. Dr.)Item Open Access Learning with high-dimensional data(2022) Fischer, Simon; Steinwart, Ingo (Prof. Dr.)This thesis is divided into three parts. In the first part we introduce a framework that allows us to investigate learning scenarios with restricted access to the data. We use this framework to model high-dimensional learning scenarios as an infinite-dimensional one in which the learning algorithm has only access to some finite-dimensional projections of the data. Finally, we provide a prototypical example of such an infinite-dimensional classification problem in which histograms can achieve polynomial learning rates. In the second part we present some individual results that might by useful for the investigation of kernel-based learning methods using Gaussian kernels in high- or infinite-dimensional learning problems. To be more precise, we present log-covering number bounds for Gaussian reproducing kernel Hilbert spaces on general bounded subsets of the Euclidean space. Unlike previous results in this direction we focus on small explicit constants and their dependence on crucial parameters such as the kernel width as well as the size and dimension of the underlying space. Afterwards, we generalize these bounds to Gaussian kernels defined on special infinite-dimensional compact subsets of the sequence space ℓ_2. More precisely, the considered domains are given by the image of the unit ℓ_∞-ball under some diagonal operator. In the third part we contribute some new insights to the compactness properties of diagonal operators from ℓ_p to ℓ_q for p ≠ q.