Classification of Silicon (Si) Wafer Material Defects in Semiconductor Choosers using a Deep Learning ShuffleNet-v2-CNN Model
- Title
- Classification of Silicon (Si) Wafer Material Defects in Semiconductor Choosers using a Deep Learning ShuffleNet-v2-CNN Model
- Creator
- Doss R.; Ramakrishnan J.; Kavitha S.; Ramkumar S.; Charlyn Pushpa Latha G.; Ramaswamy K.
- 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. 2022 Rajesh Doss et al.
- Source
- Advances in Materials Science and Engineering, Vol-2022
- Date
- 2022-01-01
- Publisher
- Hindawi Limited
- Coverage
- Doss R., Department of Computer Science, National Defence Academy, Khadakwasla, Maharashtra, Pune, India; Ramakrishnan J., Department of Information Technology and Security, College of Computer Science and Information Technology, Jazan University, Jizan, Saudi Arabia; Kavitha S., Department of Mechanical Engineering, Kalasalingam Academy of Research and Education (Deemed to Be University), Tamil Nadu, Srivilliputhur, India; Ramkumar S., Department of Business Analytics, School of Business and Management, Christ (Deemed to Be University), Karnataka, Bengaluru, India; Charlyn Pushpa Latha G., Department of Information Technology, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences (Deemed to Be University), Tamilnadu, Chennai, India; Ramaswamy K., Electrical and Computer Engineering, Dambi Dollo University, Dambi Dollo, Ethiopia
- Rights
- All Open Access; Gold Open Access
- Relation
- ISSN: 16878434
- Format
- Online
- Language
- English
- Type
- Article
Collection
Citation
Doss R.; Ramakrishnan J.; Kavitha S.; Ramkumar S.; Charlyn Pushpa Latha G.; Ramaswamy K., “Classification of Silicon (Si) Wafer Material Defects in Semiconductor Choosers using a Deep Learning ShuffleNet-v2-CNN Model,” CHRIST (Deemed To Be University) Institutional Repository, accessed February 24, 2025, https://archives.christuniversity.in/items/show/15314.