Computer Vision-Assisted Conductive Atomic Force Microscopy for 2D Materials

The thesis, "Computer Vision-Assisted Conductive Atomic Force Microscopy for 2D Materials" examines the integration of computer vision with Conductive Atomic Force Microscopy (C-AFM) to analyze 2D materials, particularly Transition Metal Dichalcogenides (TMDs), due to their significant importance in technology. The first part provides a comprehensive overview of 2D materials, discussing their properties, and applications in electronics, energy storage, biomedical fields, and photonics. An in-depth section is about the synthesis techniques such as mechanical exfoliation and chemical vapor deposition (CVD), which are crucial for producing 2D materials. The second part focuses on the principles, methodologies, and applications of Conductive Atomic Force Microscopy (C-AFM) in the study of 2D materials. It begins with an overview of C-AFM, explaining its fundamental principles and how it is used to measure electrical properties at the nanoscale. Further, it discusses various applications of C-AFM, highlighting recent technological advancements that have enhanced its capabilities. The chapter also outlines the challenges and limitations associated with C-AFM, providing a balanced view of its potential and constraints in the study of 2D materials. The third chapter explores the impact of computer vision on semiconductor metrology and its integration with C-AFM. It begins by emphasizing the importance of managing surface quality in 2D materials and the role of computational analysis in enhancing the properties of these materials. The chapter describes how computer vision techniques are used to analyze the coverage of samples, including a detailed explanation of the process for determining the noise floor in C-AFM images. It also covers the analysis of conduction levels, describing how computer vision can reveal details of current distribution in TMD samples and detect discrete current levels. The chapter further delves into the identification of point and extended defects in 2D materials using computer vision. The thesis integrates advanced computer vision techniques with C-AFM to improve the analysis and understanding of 2D materials, with the aim to make contributions to the fields of nanotechnology and material science.