| dc.description.abstract | Analysis of wafer maps is required for yield enhancement in semiconductor production since
they contain important information obtained during manufacture about process, design, and test
problem. Wafer maps provide data on multiple fault patterns on the wafer surface, and
automated classification of these defects is crucial for discovering their causes. Moreover, the
wafer maps data is collected in real-world semiconductor manufacturing, the majority class is
a non-defect pattern with approximately 85% of total labeled data. As a result, the primary goal
of this research is to determine the optimal classification model to maximize the classification
result by applying the ensemble Convolutional neural network model through the WM-811K
dataset. Also, the data augmentation techniques such as geometric transformation, and
conditional generative adversarial networks (cGAN) were implemented to improve the
performance of proposal model. | en_US |
