Wafer defect pattern classification robust for rotation

Yuki Okazaki; Hiroki Takahashi

doi:10.1117/12.2591005

13 March 2021 Wafer defect pattern classification robust for rotation

Yuki Okazaki, Hiroki Takahashi

Proceedings Volume 11766, International Workshop on Advanced Imaging Technology (IWAIT) 2021; 1176617 (2021) https://doi.org/10.1117/12.2591005
Event: International Workshop on Advanced Imaging Technology 2021 (IWAIT 2021), 2021, Online Only

Abstract

Classification of defect patterns that occur on semiconductor wafers is very important in the manufacturing process. Although CNN (Convolutional Neural Networks) is used in much of the research, these are not robust for rotation. We, therefore, propose a CNN model that is robust to rotation by performing data augmentation with rotation. The proposed method achieves higher classification accuracy than the conventional study of CNN using data augmentation.

Citation Download Citation

Yuki Okazaki and Hiroki Takahashi "Wafer defect pattern classification robust for rotation", Proc. SPIE 11766, International Workshop on Advanced Imaging Technology (IWAIT) 2021, 1176617 (13 March 2021); https://doi.org/10.1117/12.2591005

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