Advances in machine learning and deep learning applications towards wafer map defect recognition and classification: a review
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DOI: 10.1007/s10845-022-01994-1
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References listed on IDEAS
- Cheng Hao Jin & Hyun-Jin Kim & Yongjun Piao & Meijing Li & Minghao Piao, 2020. "Wafer map defect pattern classification based on convolutional neural network features and error-correcting output codes," Journal of Intelligent Manufacturing, Springer, vol. 31(8), pages 1861-1875, December.
- Jinho Kim & Youngmin Lee & Heeyoung Kim, 2018. "Detection and clustering of mixed-type defect patterns in wafer bin maps," IISE Transactions, Taylor & Francis Journals, vol. 50(2), pages 99-111, February.
- Byunghoon Kim & Young-Seon Jeong & Seung Hoon Tong & Myong K. Jeong, 2020. "A generalised uncertain decision tree for defect classification of multiple wafer maps," International Journal of Production Research, Taylor & Francis Journals, vol. 58(9), pages 2805-2821, May.
- Hsu, Shao-Chung & Chien, Chen-Fu, 2007. "Hybrid data mining approach for pattern extraction from wafer bin map to improve yield in semiconductor manufacturing," International Journal of Production Economics, Elsevier, vol. 107(1), pages 88-103, May.
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Keywords
Wafer Map; Semiconductor manufacturing; Machine learning; Deep learning; Defect recognition; Defect classification;All these keywords.
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