Abstract
With the development of the semiconductor industry, the demand for wafer production has gradually increased. Wafer manufacturing is a very complicated process, and any abnormal fluctuations in each process in this process may cause surface defects on the wafer. Accurate and rapid identification of wafer defect patterns can promptly reflect abnormal problems in the production process. Aiming at such tasks, this paper proposes a pattern recognition method for wafer surface defects based on unsupervised learning. The method is divided into two parts: unsupervised pre-training and classification fine-tuning. In the unsupervised pre-training stage, in order to improve the model’s surface defect feature extraction capability, we propose a new wafer-oriented unsupervised sampling method (WaUSM). Our wafer surface defect pattern recognition method uses an unsupervised pre-training model as the initialization to set up a classification model downstream, and fine-tune the classification model through a small number of label images with wafer defect patterns, so that the classification model can more accurately identify various types of defects pattern. The method we proposed was successfully verified by wafer defect pattern recognition on the public dataset WM-811K, and the experimental results fully proved the effectiveness and industrial applicability of the method we proposed.
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References
Yu, J., Lu, X.: Wafer map defect detection and recognition using joint local and nonlocal linear discriminant analysis. IEEE Trans. Semicond. Manuf. 29(1), 33–43 (2015)
Friedman, D.J., Hansen, M.H., Nair, V.N., James, D.A.: Model-free estimation of defect clustering in integrated circuit fabrication. IEEE Trans. Semicond. Manuf. 10(3), 344–359 (1997)
Zhao, W., Wang, W.: SeizureNet: a model for robust detection of epileptic seizures based on convolutional neural network. Cogn. Comput. Syst. 2(3), 119–124 (2020)
Zhou, J., Jia, X., Shen, L., Wen, Z., Ming, Z.: Improved softmax loss for deep learning-based face and expression recognition. Cogn. Comput. Syst. 1(4), 97–102 (2019)
Liu, X., Yin, J.: Stacked residual blocks based encoder-decoder framework for human motion prediction. Cogn. Comput. Syst. 2(4), 242–246 (2020)
Hu, D., Luo, Z., Zhao, L.: Gender identification based on human brain structural MRI with a multi-layer 3D convolution extreme learning machine. Cogn. Comput. Syst. 1(4), 91–96 (2019)
Gidaris, S., Singh, P., Komodakis, N.: Unsupervised representation learning by predicting image rotations. arXiv preprint arXiv:1803.07728 (2018)
Wang, F., Liu, H., Guo, D., Sun, F.: Unsupervised representation learning by invariancepropagation. arXiv preprint arXiv:2010.11694 (2020)
Misra, I., Zitnick, C.L., Hebert, M.: Shuffle and learn: unsupervised learning using temporal order verification. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9905, pp. 527–544. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46448-0_32
Doersch, C., Gupta, A., Efros, A.A.: Unsupervised visual representation learning by context prediction. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1422–1430 (2015)
Zhang, R., Isola, P., Efros, A.A.: Split-brain autoencoders: unsupervised learning by cross-channel prediction. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1058–1067 (2017)
Li, D., Hung, W.-C., Huang, J.-B., Wang, S., Ahuja, N., Yang, M.-H.: Unsupervised visual representation learning by graph-based consistent constraints. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9908, pp. 678–694. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46493-0_41
Wang, F., Liu, H.: Understanding the behaviour of contrastive loss. arXiv preprint arXiv:2012.09740 (2020)
Li, X., Liu, H., Zhou, J., Sun, F.: Learning cross-modal visual-tactile representation using ensembled generative adversarial networks. Cogn. Comput. Syst. 1(2), 40–44 (2019)
Noroozi, M., Favaro, P.: Unsupervised learning of visual representations by solving jigsaw puzzles. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9910, pp. 69–84. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46466-4_5
Zhang, R., Isola, P., Efros, A.A.: Colorful image colorization. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9907, pp. 649–666. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46487-9_40
Pathak, D., Krahenbuhl, P., Donahue, J., Darrell, T., Efros, A.A.: Context encoders: feature learning by inpainting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2536–2544 (2016)
Chang, C.Y., Li, C.H., Chang, Y.C., Jeng, M.: Wafer defect inspection by neural analysis of region features. J. Intell. Manuf. 22(6), 953–964 (2011)
Chen, F.L., Liu, S.F.: A neural-network approach to recognize defect spatial pattern in semiconductor fabrication. IEEE Trans. Semicond. Manuf. 13(3), 366–373 (2000)
Chen, J., Hsu, C.J., Chen, C.C.: A self-growing hidden Markov tree for wafer map inspection. J. Process Control 19(2), 261–271 (2009)
Cunningham, S.P., Mackinnon, S.: Statistical methods for visual defect metrology. IEEE Trans. Semicond. Manuf. 11(1), 48–53 (1998)
Kim, J., Lee, Y., Kim, H.: Detection and clustering of mixed-type defect patterns in wafer bin maps. Iise Trans. 50(2), 99–111 (2018)
Wang, C.H.: Recognition of semiconductor defect patterns using spatial filtering and spectral clustering. Expert Syst. Appl. 34(3), 1914–1923 (2008)
Wang, C.H.: Separation of composite defect patterns on wafer bin map using support vector clustering. Expert Syst. Appl. 36(2), 2554–2561 (2009)
Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised feature learning via non-parametric instance discrimination. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3733–3742 (2018)
Hinton, G.E., Srivastava, N., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.R.: Improving neural networks by preventing co-adaptation of feature detectors. arXiv preprint arXiv:1207.0580 (2012)
Wu, M.J., Jang, J.S.R., Chen, J.L.: Wafer map failure pattern recognition and similarity ranking for large-scale data sets. IEEE Trans. Semicond. Manuf. 28(1), 1–12 (2014)
Chien, J.C., Wu, M.T., Lee, J.D.: Inspection and classification of semiconductor wafer surface defects using CNN deep learning networks. Appl. Sci. 10(15), 5340 (2020)
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Geng, S., Liu, H., Wang, F., Zhao, S., Liu, H. (2022). Unsupervised Learning for Wafer Surface Defect Pattern Recognition. In: Deng, Z. (eds) Proceedings of 2021 Chinese Intelligent Automation Conference. Lecture Notes in Electrical Engineering, vol 801. Springer, Singapore. https://doi.org/10.1007/978-981-16-6372-7_32
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DOI: https://doi.org/10.1007/978-981-16-6372-7_32
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