Wafer map pattern classification using Manual Feature Extraction

Wafer map defect pattern classification using Manual Feature Extraction

Methodology

Manual Feature Extraction (MFE)

• Input: handcrafted features of wafer map
  • 59-dim
• Output: predicted score
• Model: FNN (2-layer MLP)

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 3.8
• Pytorch 1.9.1
• Pandas 1.3.2
• Scikit-learn 1.0.2
• OpenCV-python 4.5.3
• Scikit-image 0.18.3

References

• WM-811K(LSWMD). National Taiwan University Department of Computer Science Multimedia Information Retrieval LAB http://mirlab.org/dataSet/public/
• 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.