Classification of Silicon (Si) Wafer Material Defects in Semiconductor Choosers using a Deep Learning ShuffleNet-v2-CNN Model
The silicon wafer is one of the raw materials used to make semiconductor chipsets. Semiconductor failure or dysfunction could be the result of defects in the layers of this material. As a result, it is essential to work toward the development of a system that is both quick and precise in identifying...
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| Format: | Article |
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Wiley
2022-01-01
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| Series: | Advances in Materials Science and Engineering |
| Online Access: | http://dx.doi.org/10.1155/2022/1829792 |
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| author | Rajesh Doss Jayabrabu Ramakrishnan S. Kavitha S. Ramkumar G. Charlyn Pushpa Latha Kiran Ramaswamy |
| author_facet | Rajesh Doss Jayabrabu Ramakrishnan S. Kavitha S. Ramkumar G. Charlyn Pushpa Latha Kiran Ramaswamy |
| author_sort | Rajesh Doss |
| collection | DOAJ |
| description | The silicon wafer is one of the raw materials used to make semiconductor chipsets. Semiconductor failure or dysfunction could be the result of defects in the layers of this material. As a result, it is essential to work toward the development of a system that is both quick and precise in identifying and classifying wafer defects. Wafer map analysis is necessary for the quality control and analysis of the semiconductor manufacturing process. There are some failure patterns that can be displayed by wafer maps. These patterns can provide essential details that can assist engineers in determining the reason of wafer failures. In this research, a deep-learning-based silicon wafer defect identification and classification model is proposed. The main objective of this research is to identify and classify the silicon wafer defects using the wafer map images. This proposed model identifies and classifies the defects based on the wafer map images from the WM-811K dataset. The proposed model is composed of a pretrained deep transfer learning model called ShuffleNet-v2 with convolutional neural network (CNN) architecture. This ShuffleNet-v2-CNN performs the defects identification and classification process following the workflow of data preprocessing, data augmentation, feature extraction, and classification. For performance evaluation, the proposed ShuffleNet-v2-CNN is evaluated with performance metrics like accuracy, recall, precision, and f1-score. The proposed model has obtained an overall accuracy of 96.93%, 95.40% precision, 96.26% recall, and 95.75% F1-score in classifying the silicon wafer defects based on the wafer map images. |
| format | Article |
| id | doaj-art-304bd9e0a8fe40f99d35370c88411b89 |
| institution | Kabale University |
| issn | 1687-8442 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Materials Science and Engineering |
| spelling | doaj-art-304bd9e0a8fe40f99d35370c88411b892025-02-03T06:00:56ZengWileyAdvances in Materials Science and Engineering1687-84422022-01-01202210.1155/2022/1829792Classification of Silicon (Si) Wafer Material Defects in Semiconductor Choosers using a Deep Learning ShuffleNet-v2-CNN ModelRajesh Doss0Jayabrabu Ramakrishnan1S. Kavitha2S. Ramkumar3G. Charlyn Pushpa Latha4Kiran Ramaswamy5Department of Computer ScienceDepartment of Information Technology and SecurityDepartment of Mechanical EngineeringDepartment of Business AnalyticsDepartment of Information TechnologyElectrical and Computer EngineeringThe silicon wafer is one of the raw materials used to make semiconductor chipsets. Semiconductor failure or dysfunction could be the result of defects in the layers of this material. As a result, it is essential to work toward the development of a system that is both quick and precise in identifying and classifying wafer defects. Wafer map analysis is necessary for the quality control and analysis of the semiconductor manufacturing process. There are some failure patterns that can be displayed by wafer maps. These patterns can provide essential details that can assist engineers in determining the reason of wafer failures. In this research, a deep-learning-based silicon wafer defect identification and classification model is proposed. The main objective of this research is to identify and classify the silicon wafer defects using the wafer map images. This proposed model identifies and classifies the defects based on the wafer map images from the WM-811K dataset. The proposed model is composed of a pretrained deep transfer learning model called ShuffleNet-v2 with convolutional neural network (CNN) architecture. This ShuffleNet-v2-CNN performs the defects identification and classification process following the workflow of data preprocessing, data augmentation, feature extraction, and classification. For performance evaluation, the proposed ShuffleNet-v2-CNN is evaluated with performance metrics like accuracy, recall, precision, and f1-score. The proposed model has obtained an overall accuracy of 96.93%, 95.40% precision, 96.26% recall, and 95.75% F1-score in classifying the silicon wafer defects based on the wafer map images.http://dx.doi.org/10.1155/2022/1829792 |
| spellingShingle | Rajesh Doss Jayabrabu Ramakrishnan S. Kavitha S. Ramkumar G. Charlyn Pushpa Latha Kiran Ramaswamy Classification of Silicon (Si) Wafer Material Defects in Semiconductor Choosers using a Deep Learning ShuffleNet-v2-CNN Model Advances in Materials Science and Engineering |
| title | Classification of Silicon (Si) Wafer Material Defects in Semiconductor Choosers using a Deep Learning ShuffleNet-v2-CNN Model |
| title_full | Classification of Silicon (Si) Wafer Material Defects in Semiconductor Choosers using a Deep Learning ShuffleNet-v2-CNN Model |
| title_fullStr | Classification of Silicon (Si) Wafer Material Defects in Semiconductor Choosers using a Deep Learning ShuffleNet-v2-CNN Model |
| title_full_unstemmed | Classification of Silicon (Si) Wafer Material Defects in Semiconductor Choosers using a Deep Learning ShuffleNet-v2-CNN Model |
| title_short | Classification of Silicon (Si) Wafer Material Defects in Semiconductor Choosers using a Deep Learning ShuffleNet-v2-CNN Model |
| title_sort | classification of silicon si wafer material defects in semiconductor choosers using a deep learning shufflenet v2 cnn model |
| url | http://dx.doi.org/10.1155/2022/1829792 |
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