08 Fakultät Mathematik und Physik

Permanent URI for this collectionhttps://elib.uni-stuttgart.de/handle/11682/9

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Start date: 2020Subject: search.filters.subject.670

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    Mass-producible micro-optical elements by injection compression molding and focused ion beam structured titanium molding tools
    (2020) Ristok, Simon; Roeder, Marcel; Thiele, Simon; Hentschel, Mario; Guenther, Thomas; Zimmermann, André; Herkommer, Alois; Giessen, Harald
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    Holistic process models : a Bayesian predictive ensemble method for single and coupled unit operation models
    (2022) Montano Herrera, Liliana; Eilert, Tobias; Ho, I-Ting; Matysik, Milena; Laussegger, Michael; Guderlei, Ralph; Schrantz, Bernhard; Jung, Alexander; Bluhmki, Erich; Smiatek, Jens
    The coupling of individual models in terms of end-to-end calculations for unit operations in manufacturing processes is a challenging task. We present a probability distribution-based approach for the combined outcomes of parametric and non-parametric models. With this so-called Bayesian predictive ensemble, the statistical moments such as mean value and standard deviation can be accurately computed without any further approximation. It is shown that the ensemble of different model predictions leads to an uninformed prior distribution, which can be transformed into a predictive posterior distribution using Bayesian inference and numerical Markov Chain Monte Carlo calculations. We demonstrate the advantages of our method using several numerical examples. Our approach is not restricted to certain unit operations, and can also be used for the more robust interpretation and assessment of model predictions in general.
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    Nanoscale mapping of magnetic auto-oscillations with a single spin sensor
    (2025) Hache, Toni; Anshu, Anshu; Shalomayeva, Tetyana; Richter, Gunther; Stöhr, Rainer; Kern, Klaus; Wrachtrup, Jörg; Singha, Aparajita
    Spin Hall nano-oscillators convert DC to magnetic auto-oscillations in the microwave regime. Current research on these devices is dedicated to creating next-generation energy-efficient hardware for communication technologies. Despite intensive research on magnetic auto-oscillations within the past decade, the nanoscale mapping of those dynamics remained a challenge. We image the distribution of free-running magnetic auto-oscillations by driving the electron spin resonance transition of a single spin quantum sensor, enabling fast acquisition (100 ms/pixel). With quantitative magnetometry, we experimentally demonstrate for the first time that the auto-oscillation spots are localized at magnetic field minima acting as local potential wells for confining spin-waves. By comparing the magnitudes of the magnetic stray field at these spots, we decipher the different frequencies of the auto-oscillation modes. The insights gained regarding the interaction between auto-oscillation modes and spin-wave potential wells enable advanced engineering of real devices.