Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Articles Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Classification of Silicon (Si) Wafer Material Defects in Semiconductor Choosers using a Deep Learning ShuffleNet-v2-CNN Model Description An account of the resource 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. Creator An entity primarily responsible for making the resource Doss R.; Ramakrishnan J.; Kavitha S.; Ramkumar S.; Charlyn Pushpa Latha G.; Ramaswamy K. Source A related resource from which the described resource is derived Advances in Materials Science and Engineering, Vol-2022 Publisher An entity responsible for making the resource available Hindawi Limited Date A point or period of time associated with an event in the lifecycle of the resource 2022-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1155/2022/1829792" target="_blank" rel="noreferrer noopener">https://doi.org/10.1155/2022/1829792</a> <br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85138893732&amp;doi=10.1155%2F2022%2F1829792&amp;partnerID=40&amp;md5=d5be0ce8b0a688b35607681f82f759e4" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85138893732&amp;doi=10.1155%2f2022%2f1829792&amp;partnerID=40&amp;md5=d5be0ce8b0a688b35607681f82f759e4</a> Rights Information about rights held in and over the resource All Open Access; Gold Open Access Relation A related resource ISSN: 16878434 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Article Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant 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