Defect characterization of materials' surface using Python
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...
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| Format: | Final Year Project (FYP) |
| Language: | English |
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Nanyang Technological University
2022
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| Online Access: | https://hdl.handle.net/10356/157834 |
| _version_ | 1826129874455101440 |
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| author | Khine Mya Phyu Tun |
| author2 | Radhakrishnan K |
| author_facet | Radhakrishnan K Khine Mya Phyu Tun |
| author_sort | Khine Mya Phyu Tun |
| collection | NTU |
| description | 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 |
| first_indexed | 2024-10-01T07:47:20Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/157834 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T07:47:20Z |
| publishDate | 2022 |
| publisher | Nanyang Technological University |
| record_format | dspace |
| spelling | ntu-10356/1578342023-07-07T19:04:15Z Defect characterization of materials' surface using Python Khine Mya Phyu Tun Radhakrishnan K School of Electrical and Electronic Engineering ERADHA@ntu.edu.sg Engineering::Electrical and electronic engineering 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 Bachelor of Engineering (Electrical and Electronic Engineering) 2022-05-24T04:12:13Z 2022-05-24T04:12:13Z 2022 Final Year Project (FYP) 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 https://hdl.handle.net/10356/157834 en P2032-202 application/pdf Nanyang Technological University |
| spellingShingle | Engineering::Electrical and electronic engineering Khine Mya Phyu Tun Defect characterization of materials' surface using Python |
| title | Defect characterization of materials' surface using Python |
| title_full | Defect characterization of materials' surface using Python |
| title_fullStr | Defect characterization of materials' surface using Python |
| title_full_unstemmed | Defect characterization of materials' surface using Python |
| title_short | Defect characterization of materials' surface using Python |
| title_sort | defect characterization of materials surface using python |
| topic | Engineering::Electrical and electronic engineering |
| url | https://hdl.handle.net/10356/157834 |
| work_keys_str_mv | AT khinemyaphyutun defectcharacterizationofmaterialssurfaceusingpython |