Abstract
In a semiconductor industry, wafer defect detection has becoming ubiquitous. Various machine learning algorithms had been adopted to be the “brain” behind the machine for reliable, fast defect detection. Transfer Learning is one of the common methods. Various algorithms under Transfer Learning had been developed for different applications. In this paper, an evaluation for these transfer learning to be applied in wafer defect detection. The objective is to establish the best transfer learning algorithms with a known baseline parameter for Wafer Defect Detection. Five algorithms were evaluated namely VGG16, VGG19, InceptionV3, DeepLoc and Squeezenet. All the algorithms were pretrained from ImageNet data-base before training with the wafer defect images. Three defects categories and one non-defect were chosen for this evaluation. The key metrics for the evaluation are classification accuracy, classification precision and classification recall. 855 images were used to train and test the algorithms. Each image went through the embedding process by the evaluated algorithms. This enhanced image data numbers then went through Logistic Regression as a classifier. A 20-fold cross-validation was used to validate the score metrics. Almost all the algorithms score 85% and above in terms of accuracy, precision and recall
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References
Tuv E, Guven M, Ennis P, Lee DHL (2018) IT@Intel white paper: faster, more accurate defect classification using machine learning
Huang SH, Pan YC (2015) Automated visual inspection in the semiconductor industry: a survey. https://doi.org/10.1016/j.compind.2014.10.006
White KP, Kundu B, Mastrangelo CM (2008) Classification of defect clusters on semiconductor wafers via the hough transformation. IEEE Trans Semicond Manuf 21:272–277. https://doi.org/10.1109/TSM.2008.2000269
Lin HD, Chiu SW (2011) Flaw detection of domed surfaces in LED packages by machine vision system. Expert Syst Appl. https://doi.org/10.1016/j.eswa.2011.05.080
LeCun Y (1989) Others: generalization and network design strategies. In: Connectionism in perspective
Russakovsky O, Deng J, Su H, Krause J, Satheesh S, Ma S, Huang Z, Karpathy A, Khosla A, Bernstein M, Berg AC, Fei-Fei L (2015) ImageNet large scale visual recognition challenge. Int J Comput Vis. https://doi.org/10.1007/s11263-015-0816-y
Shen Z, Yu J (2019) Wafer map defect recognition based on deep transfer learning. In: IEEE international conference on industrial engineering and engineering management. https://doi.org/10.1109/IEEM44572.2019.8978568
Lee JH, Lee JH (2019) A reliable defect detection method for patterned wafer image using convolutional neural networks with the transfer learning. In: IOP conference series: materials science and engineering. https://doi.org/10.1088/1757-899X/647/1/012010
Hou D, Liu T, Pan YT, Hou J (2019) AI on edge device for laser chip defect detection. In: 2019 IEEE 9th annual computing and communication workshop and conference, CCWC 2019. https://doi.org/10.1109/CCWC.2019.8666503
Kang S (2018) On effectiveness of transfer learning approach for neural network-based virtual metrology modeling. IEEE Trans Semicond Manuf. https://doi.org/10.1109/TSM.2017.2787550
Simonyan K, Zisserman A (2015) Very deep convolutional networks for large-scale image recognition. In: 3rd International conference on learning representations, ICLR 2015—conference track proceedings
Szegedy C, Vanhoucke V, Ioffe S, Shlens J, Wojna Z (2016) Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE computer society conference on computer vision and pattern recognition. https://doi.org/10.1109/CVPR.2016.308
Almagro Armenteros JJ, Sønderby CK, Sønderby SK, Nielsen H, Winther O (2017) DeepLoc: prediction of protein subcellular localization using deep learning. Bioinformatics. https://doi.org/10.1093/bioinformatics/btx431
Iandola FN, Moskewicz MW, Ashraf K, Han S, Dally WJ, Keutzer K (2016) SqueezeNet. arXiv
Demšar J, Curk T, Erjavec A, Gorup Č, Hočevar T, Milutinovič M, Možina M, Polajnar M, Toplak M, Starič A, Štajdohar M, Umek L, Žagar L, Žbontar J, Žitnik M, Zupan B (2013) Orange: data mining toolbox in python. J Mach Learn Res 14:2349–2353
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The authors would like to thanks IdealVision Sdn Bhd for providing the image dataset to make this evaluation possible.
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Mat Jizat, J., Abdul Majeed, A.P.P., Ab. Nasir, A., Taha, Z., Yuen, E., Lim, S. (2022). Evaluation of the Transfer Learning Models in Wafer Defects Classification. In: Ab. Nasir, A.F., Ibrahim, A.N., Ishak, I., Mat Yahya, N., Zakaria, M.A., P. P. Abdul Majeed, A. (eds) Recent Trends in Mechatronics Towards Industry 4.0. Lecture Notes in Electrical Engineering, vol 730. Springer, Singapore. https://doi.org/10.1007/978-981-33-4597-3_78
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DOI: https://doi.org/10.1007/978-981-33-4597-3_78
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