Date of Graduation
5-2023
Document Type
Thesis
Degree Name
Bachelor of Science in Computer Science
Degree Level
Undergraduate
Department
Computer Science and Computer Engineering
Advisor/Mentor
Luu, Khoa
Committee Member/Reader
Gauch, John
Committee Member/Second Reader
Churchill, Hugh
Abstract
2D materials like hexagonal boron nitride, graphene, and tungsten diselenide are widely utilized for studying their unique mechanical and opto-electronic properties to exploit them to make transistors and fabricating a variety of other devices. All these applications require that the 2D materials used be of specific uniform thickness. Until very recently, this process has been largely manual and tedious. However, few applications exploit the characteristic color-to-thickness correspondence of these near-transparent materials. To continue this effort, in this work we create a large-scale dataset for three different materials (hBN, graphene, and WSe$_2$) to train and test an image segmentation model along with a linear regression based machine learning algorithm for flake detection and thickness estimation respectively.
Keywords
2D Materials; Thickness estimation; Flake hunting; Flake detection
Citation
Bisht, A. (2023). Characterization of 2D Quantum Materials using AI and Large-scale Quantum Data Collection. Computer Science and Computer Engineering Undergraduate Honors Theses Retrieved from https://scholarworks.uark.edu/csceuht/116
