Abstract: Herein we demonstrate that meticulous and in-depth analysis of the reaction mechanisms of nanoparticle formation is rewarded by full control of size, shape and crystal structure of superparamagnetic iron oxide nanocrystals during synthesis. Starting from two iron sources – iron(II)- and iron(III) carbonate -a strict separation of oleate formation from the generation of reactive pyrolysis products and concomitant nucleation of iron oxide nanoparticles was achieved. This protocol enabled us to analyze each step of nanoparticle formation independently in depth. Progress of the entire reaction was monitored via matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF MS) and gas chromatography (GC) gaining insight into the formation of various iron oleate species prior to nucleation. Interestingly, due to the intrinsic strongly reductive pyrolysis conditions of the oleate intermediates and redox process in early stages of the synthesis, pristine iron oxide nuclei were composed exclusively from wustite, irrespective of the oxidation state of the iron source. Controlling the reaction conditions provided a very broad range of size- and shape defined monodisperse iron oxide nanoparticles. Curiously, after nucleation star shaped nanocrystals were obtained, which underwent metamorphism towards cubic shaped particles. EELS tomography revealed ex post oxidation of the primary wustite nanocrystal providing a full 3D image of Fe2+ and Fe3+ distribution within. Overall, we developed a highly flexible synthesis, yielding multigram amounts of well-defined iron oxide nanocrystals of different sizes and morphologies.

Abstract: Non-charge-based logic devices are promising candidates for future logic circuits. Interest in studying and developing these devices has grown dramatically in the past decade as they possess key advantages over conventional CMOS technology. Due to their novel designs, a large number of micromagnetic simulations are required to fully characterize the behavior of these devices. The number and complexity of these simulations place large computational requirements on device development. We use state-of-the-art machine learning techniques to expedite identification of their behavior. Several intelligent sampling strategies are combined with machine learning multi-class classification models. These techniques are applied to a recently developed exchange-driven magnetic logic scheme that utilizes direct exchange coupling as the main driver.

Abstract: Infrared artwork inspection is typically performed through active thermography and infrared reflectography (IRR) with different setups and cameras. While IRR is an established technique in the museum field, exploiting mainly the IR-A (0.7 – 1.4 mu m) band to probe for hidden layers and modifications within the paint stratigraphy system, active thermography operating in the IR-C range (3 -5 mu m) is less frequently employed with the aim to visualize structural defects and features deeper inside the build-up. In this work, we investigate the usability of an IR-B+C system to identify overpainted works of art below a relatively thick absorbing layer of lead white paint.

Abstract: We discuss the selection and assessment of alternative metals by a combination of ab initio computation of electronic properties, experimental resistivity assessments, and calibrated line resistance models. Pt-group metals as well as Nb are identified as the most promising elements, with Ru showing the best combination of material properties and process maturity. An experimental assessment of the resistivity of Ru, Ir, and Co lines down to similar to 30 nm(2) is then used to devise compact models for line and via resistance that can be compared to Cu predictions. The main advantage of alternative metals originates from the possibility for barrierless metallization.

Abstract: For the first time, we demonstrate, using an atomistic description of a 30nm diameter spin-transfer-torque magnetic random access memories (STT-MRAM), that the difference in mechanical properties of its sub-nanometer layers induces a high compressive strain in the magnetic tunnel junction (MTJ) and leads to a detrimental magnetostrictive effect. Our model explains the issues met in engineering the electrical and magnetic performances in scaled STT-MRAM devices. The resulting high compressive strain built in the stack, particularly in the MgO tunnel barrier (t-MgO), and its associated non-uniform atomic displacements, impacts on the quality of the MTJ interface and leads to strain relieve mechanisms such as surface roughness and adhesion issues. We illustrate that the strain gradient induced by the different materials and their thicknesses in the stacks has a negative impact on the tunnel magneto-resistance (TMR), on the magnetic nucleation process and on the STT-MRAM performance.

Abstract: Superconductivity is one of the most important discoveries of the last century. With many applications in physics, engineering, and technology, superconductors are crucial to our way of living. Several material and engineering issues however prevent their widespread usage in everyday life. Comprehensive studies are being directed at these materials and their properties to come up with new technologies that will address these challenges and enhance their superconductive capabilities. In this context, numerical modeling plays an important role in the search of new solutions to existing material and engineering issues. The time-dependent Ginzburg-Landau (TDGL) theory is a powerful predictive tool for modeling the macroscopic behavior of superconductors. However most of the numerical algorithms developed so far are incapable of describing many basic properties of real superconducting devices, and are too slow on current hardware for large-scale numerical simulations necessary for their accurate description. Therefore, the purpose of this thesis is to develop high-performing numerical solutions that can correctly describe material features to be used as modeling tools of laboratory experiments. Some important innovations introduced in this work include the numerical modeling of nonrectangular geometrical shapes with complex electrical and insulating components, the inclusion of dynamic heating of the material, and the description of different types of material inhomogeneities. These encompass the principal features necessary for a complete description of the superconductive physics in real material samples. In this thesis a numerical solution is developed for modeling superconducting thin films and used to study the superconductive properties of three experimental configurations: the dynamics of vortex matter in a Corbino disk, the motion of ultrafast vortices in an hourglass-shaped microbridge, and the photon detection process in a meander-patterned nanowire. Moreover, a numerical solution is developed for modeling three-dimensional superconductors which are studied here for the first time in the type-I superconducting regime. These numerical algorithms are optimized to exploit the computational horsepower of graphics processing units (GPUs) and multicore central-processing unit (CPU) clusters such that they can achieve high-performance and be used to model large-scale problems previously impossible on conventional machines. Several computational tools are also designed to assist with the modeling of superconducting devices. These include a numerical library of the TDGL equations, a novel mechanism for the generation of complex geometries, a closed-form solver to conduct numerical simulations, and a graphics user interface (GUI) to visualize the dynamic behavior of superconductors. The contributions in this thesis ultimately push the boundaries on what is possible in state-of-the-art numerical modeling of superconductivity.

Abstract: The scalability potential of low resistivity ternary metallic alloys (MAX) as an interconnect medium has been benchmarked against copper through first-principle simulations. We report that some carbon and nitrogen MAX phases have the potential to display a reduced sensitivity of their intrinsic resistivity to scaling, while showing improved electromigration properties.

Abstract: Due to its strong bonds graphene can stretch up to 25% of its original size without breaking. Furthermore, mechanical deformations lead to the generation of pseudo-magnetic fields (PMF) that can exceed 300 T. The generated PMF has opposite direction for electrons originating from different valleys. We show that valley-polarized currents can be generated by local straining of multi-terminal graphene devices. The pseudo-magnetic field created by a Gaussian-like deformation allows electrons from only one valley to transmit and a current of electrons from a single valley is generated at the opposite side of the locally strained region. Furthermore, applying a pressure difference between the two sides of a graphene membrane causes it to bend/bulge resulting in a resistance change. We find that the resistance changes linearly with pressure for bubbles of small radius while the response becomes non-linear for bubbles that stretch almost to the edges of the sample. This is explained as due to the strong interference of propagating electronic modes inside the bubble. Our calculations show that high gauge factors can be obtained in this way which makes graphene a good candidate for pressure sensing.