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Boosted Stacking Ensemble Machine Learning Method for Wafer Map Pattern Classification

Jeonghoon Choi1, Dongjun Suh1,*, Marc-Oliver Otto2

1 Department of Convergence and Fusion System Engineering, Kyungpook National University, Sangju, 37224, Korea
2 Department of Mathematics, Natural and Economic Sciences, Ulm University of Applied Sciences, Ulm, 89075, Germany

* Corresponding Author: Dongjun Suh. Email: email

Computers, Materials & Continua 2023, 74(2), 2945-2966. https://doi.org/10.32604/cmc.2023.033417

Abstract

Recently, machine learning-based technologies have been developed to automate the classification of wafer map defect patterns during semiconductor manufacturing. The existing approaches used in the wafer map pattern classification include directly learning the image through a convolution neural network and applying the ensemble method after extracting image features. This study aims to classify wafer map defects more effectively and derive robust algorithms even for datasets with insufficient defect patterns. First, the number of defects during the actual process may be limited. Therefore, insufficient data are generated using convolutional auto-encoder (CAE), and the expanded data are verified using the evaluation technique of structural similarity index measure (SSIM). After extracting handcrafted features, a boosted stacking ensemble model that integrates the four base-level classifiers with the extreme gradient boosting classifier as a meta-level classifier is designed and built for training the model based on the expanded data for final prediction. Since the proposed algorithm shows better performance than those of existing ensemble classifiers even for insufficient defect patterns, the results of this study will contribute to improving the product quality and yield of the actual semiconductor manufacturing process.

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Cite This Article

J. Choi, D. Suh and M. Otto, "Boosted stacking ensemble machine learning method for wafer map pattern classification," Computers, Materials & Continua, vol. 74, no.2, pp. 2945–2966, 2023.



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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