Wafer defect identification with optimal hyper-parameter tuning of support vector machine using the deep feature of ResNet 101
Author:
Publisher
Springer Science and Business Media LLC
Subject
Strategy and Management,Safety, Risk, Reliability and Quality
Link
https://link.springer.com/content/pdf/10.1007/s13198-023-02220-8.pdf
Reference18 articles.
1. Baly R, Hajj H (2022) Wafer classification using support vector machines. IEEE Trans Semicond Manuf 25:373–383
2. Chien JC, Wu MT, Lee JD (2020) Inspection and classification of semiconductor wafer surface defects using CNN deep learning networks. Appl Sci 10:5340
3. Devika B, George N (2019) Convolutional neural network for semiconductor wafer defect detection. In: 10th international conference on computing, communication and networking technologies, p 1–6
4. Ganum A, Iskandar DNF, Chin LP, Fauzi AH (2022) Detection of monocrystalline silicon wafer defects using deep transfer learning. J Telecommun Inf Technol. https://doi.org/10.26636/jtit.2022.156321
5. Ghosal P, Nandanwar L, Kanchan S, Bhadra A, Chakraborty J, Nandi D (2019) Brain tumor classification using ResNet-101 based squeeze and excitation deep neural network. In: Second international conference on advanced computational and communication paradigms, p 1–6
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