Date of Graduation
Bachelor of Science in Computer Science
Computer Science and Computer Engineering
Committee Member/Second Reader
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.
2D materials, thickness estimation, flake hunting, flake detection
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
Available for download on Saturday, April 27, 2024