DMkelllog/wafermap_handcrafted_features

Wafer map defect classification using handcrafted features (pytorch)

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.