Boundary-Focused Semantic Segmentation for Limited Wafer Transmission Electron Microscope Images
Author:
Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-97-4677-4_1
Reference14 articles.
1. Shinde, P.P., Pai, P.P., Adiga, S.P.: Wafer defect localization and classification using deep learning techniques. IEEE Access 10, 39969–39974 (2022)
2. Kahng, H., Kim, S.B.: Self-supervised representation learning for wafer bin map defect pattern classification. IEEE Trans. Semicond. Manuf. 34(1), 74–86 (2020)
3. Baderot, J., et al.: Application of deep-learning based techniques for automatic metrology on scanning and transmission electron microscopy images. J. Vacuum Sci. Technol. B 40(5) (2022)
4. Marks, M.R., Hassan, Z., Cheong, K.Y.: Characterization methods for ultrathin wafer and die quality: a review. IEEE Trans. Components Packag. Manuf. Technol. 4(12), 2042–2057 (2014)
5. Saaim, K.M., Afridi, S.K., Nisar, M., Islam, S.: In search of best automated model: explaining nanoparticle TEM image segmentation. Ultramicroscopy 233, 113437 (2022)
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