Defect characterization of materials' surface using Python

Abstract

Gallium nitride semiconductor (GaN) has become popular over the past few years. GaN based devices can be found in a charger, electric vehicles, and the latest 5G network. GaN layer could be grown on a different substrate such as Silicon (Si) and Silicon Carbide (SiC). However, growing GaN on different substrates will cause dislocation due to lattice mismatch. As the demand for fast charging chargers surges and the rapid growth of the electric vehicle industry and the 5G network, there has been an increase in pressure and demand in the semiconductor industry to create a better performance chip. Thus, resulting in the heavy investment in the Research and Development department to develop a better process. In this final year project (FYP), we will create a Python program to help detect the dislocations on the GaN layer and calculate its defect density. The GaN layer is characterized using atomic force microscopy (AFM) and the AFM images are analyzed in this study. Besides detecting and calculating the defect density, we will also do some image processing using the Python program.