Jeonghoon Choi1, Dongjun Suh1,*, Marc-Oliver Otto2
CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 2945-2966, 2023, DOI:10.32604/cmc.2023.033417
- 31 October 2022
Abstract Recently, machine learning-based technologies have been developed to automate the classification of wafer map defect patterns during semiconductor manufacturing. The existing approaches used in the wafer map pattern classification include directly learning the image through a convolution neural network and applying the ensemble method after extracting image features. This study aims to classify wafer map defects more effectively and derive robust algorithms even for datasets with insufficient defect patterns. First, the number of defects during the actual process may be limited. Therefore, insufficient data are generated using convolutional auto-encoder (CAE), and the expanded data are More >
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