DMkelllog/WMPC_Stacking_TF2

Wafer map pattern classification using stacking ensemble with TF2 Keras

Wafer map pattern classification using Stacking ensemble

Wafer map defect pattern classification using Stacking ensemble

Proposed by H.Kang and S.Kang

• Hyungu Kang, Seokho Kang* (2021), "A stacking ensemble classifier with handcrafted and convolutional features for wafer map pattern classification", Computers in Industry 129: 103450 (https://www.sciencedirect.com/science/article/pii/S0166361521000579?via%3Dihub)

Methodology

Stacking Ensemble

• Input: probability outputs from MFE and CNN
• Output: predicted class
• Model: Stacking ensemble (FNN as the meta-classifer)

Data

• WM811K

  • 811457 wafer maps collected from 46393 lots in real-world fabrication

  • 172950 wafers were labeled by domain experts.

  • 9 defect classes (Center, Donut, Edge-ring, Edge-local, Local, Random, Near-full, Scratch, None)

  • provided by MIR Lab (http://mirlab.org/dataset/public/)

  • .pkl file downloaded from Kaggle dataset (https://www.kaggle.com/qingyi/wm811k-wafer-map)

  • directory: /data/LSWMD.pkl

Dependencies

• Python
• Pandas
• Tensorflow 2.1
• Scikit-learn
• Scikit-image

References

• WM-811K(LSWMD). National Taiwan University Department of Computer Science Multimedia Information Retrieval LAB http://mirlab.org/dataSet/public/
• Kang, H., & Kang, S. (2021). A stacking ensemble classifier with handcrafted and convolutional features for wafer map pattern classification. Computers in Industry, 129, 103450.
• 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.
• Shim, J., Kang, S., & Cho, S. (2020). Active learning of convolutional neural network for cost-effective wafer map pattern classification. IEEE Transactions on Semiconductor Manufacturing, 33(2), 258-266.
• Kang, S. (2020). Rotation-Invariant Wafer Map Pattern Classification With Convolutional Neural Networks. IEEE Access, 8, 170650-170658.
• 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.
• Fan, M., Wang, Q., & van der Waal, B. (2016, October). Wafer defect patterns recognition based on OPTICS and multi-label classification. In 2016 IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC) (pp. 912-915). IEEE.
• Saqlain, M., Jargalsaikhan, B., & Lee, J. Y. (2019). A voting ensemble classifier for wafer map defect patterns identification in semiconductor manufacturing. IEEE Transactions on Semiconductor Manufacturing, 32(2), 171-182.