The Classification of Wafer Defects: A Support Vector Machine with Different DenseNet Transfer Learning Models Evaluation
Wafer defect detection is a non-trivial issue in the semiconductor industry. Conventional means of defect detection is often labor-intensive based that is prone to error owing to a myriad of issue. Hence, there is push toward automatic defect detection in the industry. This work shall investigate th...
| Main Authors: | , , , , , , , |
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| Format: | Conference or Workshop Item |
| Language: | English |
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Springer Nature
2023
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| Online Access: | http://umpir.ump.edu.my/id/eprint/37269/1/The%20Classification%20of%20Wafer%20Defects%20A%20Support%20Vector%20Machine%20with%20Different%20DenseNet%20Transfer%20Learning%20Models%20Evaluation%20%281%29.pdf |
| _version_ | 1825814898907545600 |
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| author | Ismail, Mohd Khairuddin Lim, Shi Xuen Mohd Azraai, Mohd Razman Jessnor Arif, Mat Jizat Yuen, Edmund Jiang, Haochuan Yap, Eng Hwa Anwar, P. P. Abdul Majeed |
| author_facet | Ismail, Mohd Khairuddin Lim, Shi Xuen Mohd Azraai, Mohd Razman Jessnor Arif, Mat Jizat Yuen, Edmund Jiang, Haochuan Yap, Eng Hwa Anwar, P. P. Abdul Majeed |
| author_sort | Ismail, Mohd Khairuddin |
| collection | UMP |
| description | Wafer defect detection is a non-trivial issue in the semiconductor industry. Conventional means of defect detection is often labor-intensive based that is prone to error owing to a myriad of issue. Hence, there is push toward automatic defect detection in the industry. This work shall investigate the efficacy of a transfer learning pipeline that consists of different pre-trained DenseNet convolutional neural network models in which its fully connected layer is swapped with different support vector machine (SVM) models in classifying the defect state of a wafer whether it passes or fail. The optimal hyperparameters are identified via the grid search technique. It was shown from the present investigation that the features extracted via the DenseNet121 transfer learning model with a linear-based SVM model with a C and gamma parameter of 0.01, respectively, could yield a validation and test classification accuracy of 93% and 86%, respectively on a stratified 60:20:20 data split ratio. The result from the present study demonstrates that the proposed pipeline is able to classify the defect level of the wafer well. |
| first_indexed | 2024-03-06T13:05:24Z |
| format | Conference or Workshop Item |
| id | UMPir37269 |
| institution | Universiti Malaysia Pahang |
| language | English |
| last_indexed | 2024-03-06T13:05:24Z |
| publishDate | 2023 |
| publisher | Springer Nature |
| record_format | dspace |
| spelling | UMPir372692023-03-13T03:44:52Z http://umpir.ump.edu.my/id/eprint/37269/ The Classification of Wafer Defects: A Support Vector Machine with Different DenseNet Transfer Learning Models Evaluation Ismail, Mohd Khairuddin Lim, Shi Xuen Mohd Azraai, Mohd Razman Jessnor Arif, Mat Jizat Yuen, Edmund Jiang, Haochuan Yap, Eng Hwa Anwar, P. P. Abdul Majeed TJ Mechanical engineering and machinery TS Manufactures Wafer defect detection is a non-trivial issue in the semiconductor industry. Conventional means of defect detection is often labor-intensive based that is prone to error owing to a myriad of issue. Hence, there is push toward automatic defect detection in the industry. This work shall investigate the efficacy of a transfer learning pipeline that consists of different pre-trained DenseNet convolutional neural network models in which its fully connected layer is swapped with different support vector machine (SVM) models in classifying the defect state of a wafer whether it passes or fail. The optimal hyperparameters are identified via the grid search technique. It was shown from the present investigation that the features extracted via the DenseNet121 transfer learning model with a linear-based SVM model with a C and gamma parameter of 0.01, respectively, could yield a validation and test classification accuracy of 93% and 86%, respectively on a stratified 60:20:20 data split ratio. The result from the present study demonstrates that the proposed pipeline is able to classify the defect level of the wafer well. Springer Nature 2023 Conference or Workshop Item PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/37269/1/The%20Classification%20of%20Wafer%20Defects%20A%20Support%20Vector%20Machine%20with%20Different%20DenseNet%20Transfer%20Learning%20Models%20Evaluation%20%281%29.pdf Ismail, Mohd Khairuddin and Lim, Shi Xuen and Mohd Azraai, Mohd Razman and Jessnor Arif, Mat Jizat and Yuen, Edmund and Jiang, Haochuan and Yap, Eng Hwa and Anwar, P. P. Abdul Majeed (2023) The Classification of Wafer Defects: A Support Vector Machine with Different DenseNet Transfer Learning Models Evaluation. In: Robot Intelligence Technology and Applications 7: RiTA 2022 , 7-9 December 2022 , Daejeon, Korea. pp. 304-309., 642. ISBN 978-3-031-26889-2 (Published) https://doi.org/10.1007/978-3-031-26889-2_27 |
| spellingShingle | TJ Mechanical engineering and machinery TS Manufactures Ismail, Mohd Khairuddin Lim, Shi Xuen Mohd Azraai, Mohd Razman Jessnor Arif, Mat Jizat Yuen, Edmund Jiang, Haochuan Yap, Eng Hwa Anwar, P. P. Abdul Majeed The Classification of Wafer Defects: A Support Vector Machine with Different DenseNet Transfer Learning Models Evaluation |
| title | The Classification of Wafer Defects: A Support Vector Machine with Different DenseNet Transfer Learning Models Evaluation |
| title_full | The Classification of Wafer Defects: A Support Vector Machine with Different DenseNet Transfer Learning Models Evaluation |
| title_fullStr | The Classification of Wafer Defects: A Support Vector Machine with Different DenseNet Transfer Learning Models Evaluation |
| title_full_unstemmed | The Classification of Wafer Defects: A Support Vector Machine with Different DenseNet Transfer Learning Models Evaluation |
| title_short | The Classification of Wafer Defects: A Support Vector Machine with Different DenseNet Transfer Learning Models Evaluation |
| title_sort | classification of wafer defects a support vector machine with different densenet transfer learning models evaluation |
| topic | TJ Mechanical engineering and machinery TS Manufactures |
| url | http://umpir.ump.edu.my/id/eprint/37269/1/The%20Classification%20of%20Wafer%20Defects%20A%20Support%20Vector%20Machine%20with%20Different%20DenseNet%20Transfer%20Learning%20Models%20Evaluation%20%281%29.pdf |
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