DMkelllog/WMPC_MultiNN_TF2

Wafer map pattern classification using multi-input neural network with TF2 Keras

Wafer map pattern classification using Multi-input neural network

Wafer map defect pattern classification using multi-input neural netork

Methodology

Multi-input neural network

• Input: wafer map image, manually extracted features
• Output: predicted class
• Model: Multi-input neural network

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
• Scikit-learn
• Scikit-image

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.