{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,25]],"date-time":"2025-10-25T12:37:24Z","timestamp":1761395844126},"reference-count":0,"publisher":"IOS Press","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020,9,15]]},"abstract":"<jats:p>Silicon wafer defect data collected from fabrication facilities is intrinsically imbalanced because of the variable frequencies of defect types. Frequently occurring types will have more influence on the classification predictions if a model gets trained on such skewed data. A fair classifier for such imbalanced data requires a mechanism to deal with type imbalance in order to avoid biased results. This study has proposed a convolutional neural network for wafer map defect classification, employing oversampling as an imbalance addressing technique. To have an equal participation of all classes in the classifier\u2019s training, data augmentation has been employed, generating more samples in minor classes. The proposed deep learning method has been evaluated on a real wafer map defect dataset and its classification results on the test set returned a 97.91% accuracy. The results were compared with another deep learning based auto-encoder model demonstrating the proposed method, a potential approach for silicon wafer defect classification that needs to be investigated further for its robustness.<\/jats:p>","DOI":"10.3233\/faia200547","type":"book-chapter","created":{"date-parts":[[2020,9,16]],"date-time":"2020-09-16T22:58:36Z","timestamp":1600297116000},"source":"Crossref","is-referenced-by-count":1,"title":["Oversampling Based on Data Augmentation in Convolutional Neural Network for Silicon Wafer Defect Classification"],"prefix":"10.3233","author":[{"given":"Uzma","family":"Batool","sequence":"first","affiliation":[{"name":"Department of Computer Science, University of Wah, Wah Cantt, Pakistan"},{"name":"Centre for Artificial Intelligence and Robotics iKohza, Malaysia-Japan International Institute of Technology, Universiti Teknologi Malaysia, Kuala Lumpur, Malaysia"}]},{"given":"Mohd Ibrahim","family":"Shapiai","sequence":"additional","affiliation":[{"name":"Centre for Artificial Intelligence and Robotics iKohza, Malaysia-Japan International Institute of Technology, Universiti Teknologi Malaysia, Kuala Lumpur, Malaysia"}]},{"given":"Nordinah","family":"Ismail","sequence":"additional","affiliation":[{"name":"Embedded System iKohza, Malaysia-Japan International Institute of Technology, Universiti Teknologi Malaysia, Kuala Lumpur, Malaysia"}]},{"given":"Hilman","family":"Fauzi","sequence":"additional","affiliation":[{"name":"Faculty of Electrical Engineering, Telkom University, Bandung, Indonesia"}]},{"given":"Syahrizal","family":"Salleh","sequence":"additional","affiliation":[{"name":"Tec D (Malaysia) Sdn. Bhd."}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","Knowledge Innovation Through Intelligent Software Methodologies, Tools and Techniques"],"original-title":[],"link":[{"URL":"http:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA200547","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,9,17]],"date-time":"2020-09-17T13:13:21Z","timestamp":1600348401000},"score":1,"resource":{"primary":{"URL":"http:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA200547"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,9,15]]},"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia200547","relation":{},"ISSN":["0922-6389","1879-8314"],"issn-type":[{"value":"0922-6389","type":"print"},{"value":"1879-8314","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,9,15]]}}}