Classification of defects in semiconductor wafer using artificial intelligence
Machine learning, a subset of artificial intelligence is an emerging technology that enabled the classification of objects without the need of being explicitly programmed. Due to the popularity of artificial intelligence, many frameworks were invented. ANN, CNN, Faster RCNN will be explained t...
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| Other Authors: | |
| Format: | Final Year Project (FYP) |
| Language: | English |
| Published: |
2019
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| Online Access: | http://hdl.handle.net/10356/78800 |
| _version_ | 1811689750540582912 |
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| author | Lit, Yek Kit |
| author2 | Anand Krishna Asundi |
| author_facet | Anand Krishna Asundi Lit, Yek Kit |
| author_sort | Lit, Yek Kit |
| collection | NTU |
| description | Machine learning, a subset of artificial intelligence is an emerging technology that enabled
the classification of objects without the need of being explicitly programmed. Due to the
popularity of artificial intelligence, many frameworks were invented. ANN, CNN, Faster RCNN
will be explained to understand the fundamentals of machine learning. However, the
focus of this project is on the framework Mask-RCNN, developed by Facebook, it uses
region-based convolutional neural network that simultaneously perform object detection and
instance segmentation.
This project comprises of two important part. The first step is to obtain the datasets in the
form of images in large numbers of a 1000. The images are annotated by drawing polygons
on the region of interest and a json file is obtained. The Mask R-CNN is downloaded on the
computer and a virtual environment is created, dependencies are installed for training to take
place. The second part includes running the training to obtain the h5 files. Detection is run to
determine the success of the training. The whole process will be repeated if the detection is
unable to produce the results needed. |
| first_indexed | 2024-10-01T05:53:04Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/78800 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T05:53:04Z |
| publishDate | 2019 |
| record_format | dspace |
| spelling | ntu-10356/788002023-03-04T19:05:05Z Classification of defects in semiconductor wafer using artificial intelligence Lit, Yek Kit Anand Krishna Asundi School of Mechanical and Aerospace Engineering Engineering::Mechanical engineering Machine learning, a subset of artificial intelligence is an emerging technology that enabled the classification of objects without the need of being explicitly programmed. Due to the popularity of artificial intelligence, many frameworks were invented. ANN, CNN, Faster RCNN will be explained to understand the fundamentals of machine learning. However, the focus of this project is on the framework Mask-RCNN, developed by Facebook, it uses region-based convolutional neural network that simultaneously perform object detection and instance segmentation. This project comprises of two important part. The first step is to obtain the datasets in the form of images in large numbers of a 1000. The images are annotated by drawing polygons on the region of interest and a json file is obtained. The Mask R-CNN is downloaded on the computer and a virtual environment is created, dependencies are installed for training to take place. The second part includes running the training to obtain the h5 files. Detection is run to determine the success of the training. The whole process will be repeated if the detection is unable to produce the results needed. Bachelor of Engineering (Mechanical Engineering) 2019-06-28T04:25:52Z 2019-06-28T04:25:52Z 2019 Final Year Project (FYP) http://hdl.handle.net/10356/78800 en Nanyang Technological University 48 p. application/pdf |
| spellingShingle | Engineering::Mechanical engineering Lit, Yek Kit Classification of defects in semiconductor wafer using artificial intelligence |
| title | Classification of defects in semiconductor wafer using artificial intelligence |
| title_full | Classification of defects in semiconductor wafer using artificial intelligence |
| title_fullStr | Classification of defects in semiconductor wafer using artificial intelligence |
| title_full_unstemmed | Classification of defects in semiconductor wafer using artificial intelligence |
| title_short | Classification of defects in semiconductor wafer using artificial intelligence |
| title_sort | classification of defects in semiconductor wafer using artificial intelligence |
| topic | Engineering::Mechanical engineering |
| url | http://hdl.handle.net/10356/78800 |
| work_keys_str_mv | AT lityekkit classificationofdefectsinsemiconductorwaferusingartificialintelligence |