A Learning-Based Approach for Wafer Defect Detection in Production Quality Control
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Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-99-7862-5_27
Reference15 articles.
1. Adly, F., Yoo, P. D., Muhaidat, S., Al-Hammadi, Y., Lee, U., & Ismail, M. (2015). Randomized general regression network for identification of defect patterns in semiconductor wafer maps. IEEE Transactions on Semiconductor Manufacturing, 28(2), 145–152. https://doi.org/10.1109/TSM.2015.2405252
2. Alawieh, M. B., Boning, D., & Pan, D. Z. (2020). Wafer map defect patterns classification using deep selective learning. In Proceedings—Design Automation Conference, 2020-July. https://doi.org/10.1109/DAC18072.2020.9218580
3. Fan, M., Wang, Q., & Van Der Waal, B. (2017). Wafer defect patterns recognition based on OPTICS and multi-label classification. In Proceedings of 2016 IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC 2016). https://doi.org/10.1109/IMCEC.2016.7867343
4. Gupta, J. N. D., Ruiz, R., Fowler, J. W., & Mason, S. J. (2006). Operational planning and control of semiconductor wafer production. Production Planning and Control, 17(7). https://doi.org/10.1080/09537280600900733
5. Imoto, K., Nakai, T., Ike, T., Haruki, K., & Sato, Y. (2019). A CNN-Based transfer learning method for defect classification in semiconductor manufacturing. IEEE Transactions on Semiconductor Manufacturing, 32(4). https://doi.org/10.1109/TSM.2019.2941752
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