Abstract
In semiconductor manufacturing, detecting defect patterns is important because they are directly related to the root causes of failures in the wafer process. The rapid advancement of the integrated circuit technology has recently led to more frequent occurrences of mixed-type defect patterns, wherein two or more defect patterns simultaneously occur in a wafer bin map. The detection of these mixed patterns is more difficult than that of single patterns. To detect these mixed patterns, binary relevance approaches based on convolutional neural networks have been proposed. However, as the manufacturing process has been advanced and integrated, various failure types are newly detected, thus the number of single models can be continuously increased following the diversification of defect types. Therefore, we propose an effective framework for detecting mixed-type patterns in which a simple single model, called the single shot detector, is employed. By applying the proposed model to the WM-811K dataset, we show that our framework outperforms existing CNN-based methods and also provides defect location information.
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Data availability
WM-811K is openly available in a public repository that does not issue DOIs. The WM-811K dataset that supports the findings of this study is openly available at [MIR lab] at [http://mirlab.org/dataSet/public/].
References
Alawieh, M. B., Wang, F., & Li, X. (2016). Identifying systematic spatial failure patterns through wafer clustering. In 2016 IEEE international symposium on circuits and systems (ISCAS) (pp. 910–913). IEEE.
Berre, L., & Desroziers, G. (2010). Filtering of background error variances and correlations by local spatial averaging: A review. Monthly Weather Review, 138(10), 3693–3720.
Bulnes, F. G., Usamentiaga, R., Garcia, D. F., & Molleda, J. (2016). An efficient method for defect detection during the manufacturing of web materials. Journal of Intelligent Manufacturing, 27(2), 431–445.
Chen, H., Pang, Y., Hu, Q., & Liu, K. (2020). Solar cell surface defect inspection based on multispectral convolutional neural network. Journal of Intelligent Manufacturing, 31(2), 453–468.
Cheon, S., Lee, H., Kim, C. O., & Lee, S. H. (2019). Convolutional neural network for wafer surface defect classification and the detection of unknown defect class. IEEE Transactions on Semiconductor Manufacturing., 32, 163–170.
Chien, C. F., Hsu, S. C., & Chen, Y. J. (2013). A system for online detection and classification of wafer bin map defect patterns for manufacturing intelligence. International Journal of Production Research, 51(8), 2324–2338.
Clark, A. (2015). Pillow (PIL fork) documentation.
Girshick, R. (2015). Fast r-cnn. In Proceedings of the IEEE international conference on computer vision (pp. 1440–1448).
He, K., Gkioxari, G., Dollár, P., & Girshick, R. (2017). Mask r-cnn. In Proceedings of the IEEE international conference on computer vision (pp. 2961–2969).
He, K., Zhang, X., Ren, S., & Sun, J. (2015). Delving deep into rectifiers: Surpassing human-level performance on imagenet classification. In Proceedings of the IEEE international conference on computer vision (pp. 1026–1034).
Jeong, Y. S., Kim, S. J., & Jeong, M. K. (2008). Automatic identification of defect patterns in semiconductor wafer maps using spatial correlogram and dynamic time warping. IEEE Transactions on Semiconductor Manufacturing, 21(4), 625–637.
Jin, C. H., Na, H. J., Piao, M., Pok, G., & Ryu, K. H. (2019). A novel DBSCAN-based defect pattern detection and classification framework for wafer bin map. IEEE Transactions on Semiconductor Manufacturing, 32(3), 286–292.
Kim, J., Lee, Y., & Kim, H. (2018). Detection and clustering of mixed-type defect patterns in wafer bin maps. IISE Transactions, 50(2), 99–111.
Kim, T. S., & Sohn, S. Y. (2020). Multitask learning for health condition identification and remaining useful life prediction: deep convolutional neural network approach. Journal of Intelligent Manufacturing, 1–11.
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. In Advances in neural information processing systems (pp. 1097–1105).
Kulchandani, J. S., & Dangarwala, K. J. (2015). Moving object detection: Review of recent research trends. In 2015 International conference on pervasive computing (ICPC) (pp. 1–5). IEEE.
Kyeong, K., & Kim, H. (2018). Classification of mixed-type defect patterns in wafer bin maps using convolutional neural networks. IEEE Transactions on Semiconductor Manufacturing, 31(3), 395–402.
Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C. Y., & Berg, A. C. (2016). SSD: Single shot multibox detector. In European conference on computer vision proceedings (pp. 21–37). Cham: Springer.
Nakazawa, T., & Kulkarni, D. V. (2018). Wafer map defect pattern classification and image retrieval using convolutional neural network. IEEE Transactions on Semiconductor Manufacturing, 31(2), 309–314.
Park, C., & Sohn, S. Y. (2020). Early detection of valuable patents using a deep learning model: Case of semiconductor industry. Technological Forecasting and Social Change, 158, 120146.
Piao, M., Jin, C. H., Lee, J. Y., & Byun, J. Y. (2018). Decision tree ensemble-based wafer map failure pattern recognition based on radon transform-based features. IEEE Transactions on Semiconductor Manufacturing, 31(2), 250–257.
Redmon, J., Divvala, S., Girshick, R., & Farhadi, A. (2016). You only look once: Unified, real-time object detection. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 779–788).
Ren, S., He, K., Girshick, R., & Sun, J. (2015). Faster r-cnn: Towards real-time object detection with region proposal networks. In Advances in neural information processing systems (pp. 91–99).
Taam, W., & Hamada, M. (1993). Detecting spatial effects from factorial experiments: An application from integrated-circuit manufacturing. Technometrics, 35(2), 149–160.
Wang, C. H., Kuo, W., & Bensmail, H. (2006). Detection and classification of defect patterns on semiconductor wafers. IIE Transactions, 38(12), 1059–1068.
Weimer, D., Scholz-Reiter, B., & Shpitalni, M. (2016). Design of deep convolutional neural network architectures for automated feature extraction in industrial inspection. CIRP Annals, 65(1), 417–420.
Wu, M. J., Jang, J. S. R., & Chen, J. L. (2014). Wafer map failure pattern recognition and similarity ranking for large-scale data sets. IEEE Transactions on Semiconductor Manufacturing, 28(1), 1–12.
Xue-Wu, Z., Yan-Qiong, D., Yan-Yun, L., Ai-Ye, S., & Rui-Yu, L. (2011). A vision inspection system for the surface defects of strongly reflected metal based on multi-class SVM. Expert Systems with Applications, 38(5), 5930–5939.
Zhang, M. L., & Zhou, Z. H. (2013). A review on multi-label learning algorithms. IEEE Transactions on Knowledge and Data Engineering, 26(8), 1819–1837.
Zhao, Z. Q., Zheng, P., Xu, S. T., & Wu, X. (2019). Object detection with deep learning: A review. IEEE Transactions on Neural Networks and Learning Systems, 30(11), 3212–3232.
Acknowledgements
This work was supported by the National Research Foundation of Korea (NRF) Grant funded by the Korea government (MSIT) (2020R1A2C2005026).
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Kim, T.S., Lee, J.W., Lee, W.K. et al. Novel method for detection of mixed-type defect patterns in wafer maps based on a single shot detector algorithm. J Intell Manuf 33, 1715–1724 (2022). https://doi.org/10.1007/s10845-021-01755-6
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DOI: https://doi.org/10.1007/s10845-021-01755-6