Improved wafer map defect pattern classification using automatic data augmentation based lightweight encoder network in contrastive learning
Author:
Funder
Priority Academic Program Development of Jiangsu Higher Education Institutions
Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s10845-024-02444-w.pdf
Reference45 articles.
1. Abd Al Rahman, M., Danishvar, S., & Mousavi, A. (2021). An improved capsule network (wafercaps) for wafer bin map classification based on dcgan data upsampling. IEEE Transactions on Semiconductor Manufacturing, 35(1), 50–59. https://doi.org/10.1109/TSM.2021.3134625
2. Azizi, S., Mustafa, B., Ryan, F., Beaver, Z., Freyberg, J., Deaton, J., Loh, A., Karthikesalingam, A., Kornblith, S., & Chen, T. (2021). Big self-supervised models advance medical image classification (pp. 3478–3488). https://doi.org/10.1109/ICCV48922.2021.00346
3. Chen, S., Zhang, Y., Hou, X., Shang, Y., & Yang, P. (2022). Wafer map failure pattern recognition based on deep convolutional neural network. Expert Systems with Applications, 209, 118254. https://doi.org/10.1016/J.ESWA.2022.118254
4. Chen, T., Kornblith, S., Norouzi, M., & Hinton, G. (2020). A simple framework for contrastive learning of visual representations (pp. 1597–1607). https://doi.org/10.48550/arXiv.2002.05709
5. Chen, X., Chen, J., Han, X., Zhao, C., Zhang, D., Zhu, K., & Su, Y. (2020). A light-weighted cnn model for wafer structural defect detection. IEEE Access, 8, 24006–24018. https://doi.org/10.1109/ACCESS.2020.2970461
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