Automated Crack Detection in 2D Hexagonal Boron Nitride Coatings Using Machine Learning

Author:

Rahman Md Hasan-Ur12ORCID,Shrestha Gurung Bichar Dip3ORCID,Jasthi Bharat K.24ORCID,Gnimpieba Etienne Z.3ORCID,Gadhamshetty Venkataramana12ORCID

Affiliation:

1. Department of Civil and Environmental Engineering, South Dakota School of Mines & Technology, Rapid City, SD 57701, USA

2. 2-Dimensional Materials for Biofilm Engineering Science and Technology (2D-BEST) Center, South Dakota School of Mines & Technology, Rapid City, SD 57701, USA

3. Department of Biomedical Engineering, University of South Dakota, Vermillion, SD 57069, USA

4. Department of Materials and Metallurgical Engineering, South Dakota School of Mines & Technology, Rapid City, SD 57701, USA

Abstract

Characterizing defects in 2D materials, such as cracks in chemical vapor deposited (CVD)-grown hexagonal boron nitride (hBN), is essential for evaluating material quality and reliability. Traditional characterization methods are often time-consuming and subjective and can be hindered by the limited optical contrast of hBN. To address this, we utilized a YOLOv8n deep learning model for automated crack detection in transferred CVD-grown hBN films, using MATLAB’s Image Labeler and Supervisely for meticulous annotation and training. The model demonstrates promising crack-detection capabilities, accurately identifying cracks of varying sizes and complexities, with loss curve analysis revealing progressive learning. However, a trade-off between precision and recall highlights the need for further refinement, particularly in distinguishing fine cracks from multilayer hBN regions. This study demonstrates the potential of ML-based approaches to streamline 2D material characterization and accelerate their integration into advanced devices.

Funder

National Science Foundation (NSF) RII FEC awards

NSF CBET award

National Institute of General Medical Sciences of the National Institutes of Health

Publisher

MDPI AG

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