Abstract
Artificial intelligence techniques such as deep learning and machine learning are nowadays implemented in inspection systems in a growing number of industries. These models have reached human-level performance in defect detection and classification tasks when enough data is available. However, most models use supervised learning approaches and, therefore, must have prior knowledge of the number of defect classes that may occur along the production line. This is a major problem in dynamic industries, such as the semiconductor manufacturing industry, where continuous changes in equipment and environment lead to the emergence of new classes of defects. Hence, it is necessary to detect new defect classes and classify them as “unknown” in order to study them meticulously and ensure a good quality of the manufactured semiconductor wafer. This paper presents a novel approach that fuses the ResNet50 convolutional neural network with a Gaussian mixture model for the detection of 100% of the images from the unknown defect class.
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Abd Al Rahman, M., Mousavi, A.: A review and analysis of automatic optical inspection and quality monitoring methods in electronics industry. IEEE Access 8, 183192–183271 (2020)
Datta, S.: A review on convolutional neural networks. In: Bera, R., Pradhan, P.C., Liu, C.-M., Dhar, S., Sur, S.N. (eds.) ICCDN 2019. LNEE, vol. 662, pp. 445–452. Springer, Singapore (2020). https://doi.org/10.1007/978-981-15-4932-8_50
Geng, C., Huang, S.J., Chen, S.: Recent advances in open set recognition: a survey. IEEE Trans. Pattern Anal. Mach. Intell. 43(10), 3614–3631 (2020)
Gómez-Sirvent, J.L., López de la Rosa, F., Sánchez-Reolid, R., Fernández-Caballero, A., Morales, R.: Optimal feature selection for defect classification in semiconductor wafers. IEEE Trans. Semiconduct. Manuf. (2022). https://doi.org/10.1109/TSM.2022.3146849
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. arXiv preprint arXiv:1512.03385 (2015)
Hwang, J., Kim, H.: Variational deep clustering of wafer map patterns. IEEE Trans. Semicond. Manuf. 33(3), 466–475 (2020)
Lin, J., Ma, L., Yao, Y.: A spectrum-domain instance segmentation model for casting defects. Integrat. Comput. Aided Eng. 29, 63–82 (2022)
Modarres, M.H., Aversa, R., Cozzini, S., Ciancio, R., Leto, A., Brandino, G.P.: Neural network for nanoscience scanning electron microscope image recognition. Sci. Rep. 7(1), 1–12 (2017)
Nakamae, K.: Electron microscopy in semiconductor inspection. Measurem. Sci. Technol. 32(5), 052003 (2021)
Pedregosa, F., et al.: Scikit-learn: machine learning in python. J. Mach. Learn. Res. 12, 2825–2830 (2011)
Rasmussen, C.: The infinite gaussian mixture model. Adv. Neural Inf. Process. Syst. 12 (1999)
Reynolds, D.A.: Gaussian mixture models. Encyclop. Biomet. 741, 659–663 (2009)
López de la Rosa, F., Sánchez-Reolid, R., Gómez-Sirvent, J.L., Morales, R., Fernández-Caballero, A.: A review on machine and deep learning for semiconductor defect classification in scanning electron microscope images. Appl. Sci. 11(20), 9508 (2021)
Smith, B.: Six-sigma design (quality control). IEEE Spectrum 30(9), 43–47 (1993)
Su, C.T., Yang, T., Ke, C.M.: A neural-network approach for semiconductor wafer post-sawing inspection. IEEE Trans. Semiconduct. Manuf. 15(2), 260–266 (2002)
Wang, J., Jiang, J.: Unsupervised deep clustering via adaptive GMM modeling and optimization. Neurocomputing 433, 199–211 (2021)
Wang, M.J., Huang, C.L.: Evaluating the eye fatigue problem in wafer inspection. IEEE Trans. Semiconduct. Manuf. 17(3), 444–447 (2004)
Wang, P., et al.: The study of defects auto-classification system in semiconductor manufacturing. In: 2020 China Semiconductor Technology International Conference (CSTIC), pp. 1–3. IEEE (2020)
Yu, H., Yang, L.T., Zhang, Q., Armstrong, D., Deen, M.J.: Convolutional neural networks for medical image analysis: state-of-the-art, comparisons, improvement and perspectives. Neurocomputing 444, 92–110 (2021)
Yu, J.: Fault detection using principal components-based gaussian mixture model for semiconductor manufacturing processes. IEEE Trans. Semiconduct. Manuf. 24(3), 432–444 (2011)
Yu, J.: Semiconductor manufacturing process monitoring using gaussian mixture model and bayesian method with local and nonlocal information. IEEE Trans. Semiconduct. Manuf. 25(3), 480–493 (2012)
Yuan-Fu, Y., Min, S.: Double feature extraction method for wafer map classification based on convolution neural network. In: 2020 31st Annual SEMI Advanced Semiconductor Manufacturing Conference (ASMC), pp. 1–6. IEEE (2020)
Acknowledgments
This work was partially supported by iRel40, a European co-funded innovation project that has been granted by the ECSEL Joint Undertaking (JU) (grant number 876659). The funding of the project comes from the Horizon 2020 research programme and participating countries. National funding is provided by Germany, including the Free States of Saxony and Thuringia, Austria, Belgium, Finland, France, Italy, the Netherlands, Slovakia, Spain, Sweden, and Turkey. The publication is part of grant PCI2020-112001, funded by MCIN/AEI/10.13039/501100011033 and by “NextGeneration EU”/PRTR.
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López de la Rosa, F., Gómez-Sirvent, J.L., Kofler, C., Morales, R., Fernández-Caballero, A. (2022). Detection of Unknown Defects in Semiconductor Materials from a Hybrid Deep and Machine Learning Approach. In: Ferrández Vicente, J.M., Álvarez-Sánchez, J.R., de la Paz López, F., Adeli, H. (eds) Bio-inspired Systems and Applications: from Robotics to Ambient Intelligence. IWINAC 2022. Lecture Notes in Computer Science, vol 13259. Springer, Cham. https://doi.org/10.1007/978-3-031-06527-9_35
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DOI: https://doi.org/10.1007/978-3-031-06527-9_35
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