Abstract
The final goal of this study is an integrated approach of walk-through from the classification of defect products to the feature estimation for preventing the cause of the defects in real-time or proactively. Particularly, this paper will introduce the multimodal data analytics in production process integrations to analyze the classification of defect patterns of final product quality and the defect cause impact in integrated manner. There are three steps to summarize the possibilities and limitations of the multimodal data analyses. The first step is the accuracy improvement of the classification of the multi-class defect patterns of images at lower cost by using additional feature/data substituting human-being domain knowledge. That is also to prepare more accurate and effective classifications of superposition pattern of small samples data. The second step is adaptive selections and estimations of important features including interactions and variety within a practical computational time and accuracy. The third step is an integrated approach of walk-through from the classification to the feature estimation for proactively preventing the causes of the defect. As the first report, this paper introduces the first and the third issues through a case study. Particularly, this paper presents the semiconductor defect patterns case.
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Acknowledgements
This research has been partly executed in response to support to KIOXIA Corporation. We all thank for the support. We also appreciate the constructive comments of all reviewers.
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Takada, D., Itou, H., Ohta, R., Maeda, T., Watanabe, K., Arima, S. (2023). Image-Multimodal Data Analysis for Defect Classification: Case Study of Semiconductor Defect Patterns. In: Czarnowski, I., Howlett, R., Jain, L.C. (eds) Intelligent Decision Technologies. KESIDT 2023. Smart Innovation, Systems and Technologies, vol 352. Springer, Singapore. https://doi.org/10.1007/978-981-99-2969-6_5
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