Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/157834
Title: Defect characterization of materials' surface using Python
Authors: Khine Mya Phyu Tun
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2022
Publisher: Nanyang Technological University
Source: Khine Mya Phyu Tun (2022). Defect characterization of materials' surface using Python. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/157834
Project: P2032-202
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
URI: https://hdl.handle.net/10356/157834
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:EEE Student Reports (FYP/IA/PA/PI)

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