Defect characterization of materials' surface using Python

Abstract

Nowadays, technology flows at an extremely rapid rate in this technologically advancing world. As millions of products and devices these days hugely rely on IC chips, the demand for IC chips has immensely increased. As a result, the global chip shortage became a very transparent issue, especially when Covid-19 started. Thus, the semiconductor engineering field plays a crucial role in this modern world. Surface defect analysis is vital to the semiconductor industry, R&D fields, and material science studies. As more and more new technologies are invented, there is a growing need for methods and software that can analyze, characterize, and visualize different types of materials’ surface defects quickly and accurately. In this final year project (FYP), the software that can rapidly detect the surface defects and count the number of defects on the AFM images will be implemented using a computer vision library in Python. In addition, the program will automatically calculate defect density based on the defect count obtained and then generate the defect density value of the AFM image