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The scanning tunneling microscope (STM) is one of the most advanced surface science tools capable of atomic resolution imaging and atomic manipulation. Unfortunately, STM has many time-consuming bottlenecks, like probe conditioning, tip instability, and noise artificing, which causes the technique to have low experimental throughput. This dissertation describes my efforts to address these challenges through automation and machine learning. It consists of two main sections each describing four projects for a total of eight studies. The first section details two studies on nanoscale sample fabrication and two studies on STM tip preparation. The first two studies describe the fabrication of graphene-based Josephson Junction devices and the factorial optimization of patterned carbon nanotube forest synthesis. The second two studies focus on the factorial optimization of electrochemical STM tip etching and automated STM tip functionalization via in-situ silicon nanocolumn growth. The second section details four studies on the use of neural networks for STM image and spectroscopy analysis. The third two studies are on the effectiveness of convolutional neural networks for identifying images of conditioned STM tips on the Au(111) surface and on the detection and metrology of atomic scale defects in single crystal tungsten diselenide, a transition metal dichalcogenide. The fourth two studies are on the use of variational autoencoders to autonomously classify scanning tunneling spectra of various materials, molecules, and surface structures and to identify bismuth and nickel atoms from cross sectional STM images of doped gallium arsenide.

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The electronic and geometrical structure of single difluoro-bora-1,3,5,7-tetraphenyl-aza-dipyrromethene (aza-BODIPY) molecules adsorbed on the Au(111) surface is investigated by low temperature scanning tunneling microscopy and spectroscopy in conjunction with ab initio density functional theory simulations of the density of states and of the interaction with the substrate. Our DFT calculations indicate that the aza-BODIPY molecule forms a chemical bond with the Au(111) substrate, with distortion of the molecular geometry and significant charge transfer between the molecule and the substrate. Nevertheless, most likely due to the low corrugation of the Au(111) surface, diffusion of the molecule is observed for applied bias in excess of 1 V. / Dieser Beitrag ist mit Zustimmung des Rechteinhabers aufgrund einer (DFG-geförderten) Allianz- bzw. Nationallizenz frei zugänglich.

A coupling-limited approach for the Ullmann reaction-like on-surface synthesis of a two-dimensional covalent organic network starting from a halogenated metallo-porphyrin is demonstrated. Copper-octabromo-tetraphenylporphyrin molecules can diffuse and self-assemble when adsorbed on the inert Au(111) surface. Splitting-off of bromine atoms bonded at the macrocyclic core of the porphyrin starts at room temperature after the deposition and is monitored by X-ray photoelectron spectroscopy for different annealing steps. Direct coupling between the reactive carbon sites of the molecules is, however, hindered by the molecular shape. This leads initially to an ordered non-covalently interconnected supramolecular structure. Further heating to 300 °C and an additional hydrogen dissociation step is required to link the molecular macrocycles via a phenyl group and form large ordered polymeric networks. This approach leads to a close-packed covalently bonded network of overall good quality. The structures are characterized using scanning tunneling microscopy. Different kinds of lattice defects and, furthermore, the impact of polymerization on the HOMO–LUMO gap are discussed. Density functional theory calculations corroborate the interpretations and give further insight into the adsorption of the debrominated molecule on the surface and the geometry and coupling reaction of the polymeric structure. / Dieser Beitrag ist aufgrund einer (DFG-geförderten) Allianz- bzw. Nationallizenz frei zugänglich.