Keywords

Materials Science, Scanning Tunneling Microscopy, Machine Learning, Transition Metal Dichalcogenides, Microscopy, Deep Learning

Abstract

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.

Completion Date

2024

Semester

Spring

Committee Chair

Ishigami, Masahiro

Degree

Doctor of Philosophy (Ph.D.)

College

College of Sciences

Department

Physics

Format

application/pdf

Identifier

DP0028376

URL

https://purls.library.ucf.edu/go/DP0028376

Language

English

Rights

In copyright

Release Date

May 2024

Length of Campus-only Access

None

Access Status

Doctoral Dissertation (Open Access)

Campus Location

Orlando (Main) Campus

Accessibility Status

Meets minimum standards for ETDs/HUTs

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