Date of Graduation


Document Type


Degree Name

Bachelor of Science in Computer Science


Computer Science and Computer Engineering


Luu, Khoa

Committee Member/Reader

Gauch, John

Committee Member/Second Reader

Churchill, Hugh


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

Available for download on Saturday, April 27, 2024