Silicon Wafer Fault Detection Using Machine Learning Techniques
Keywords:
Data preprocessing, Ensemble learning, Machine learning, Neural networks, Semiconductor manufacturingAbstract
In recent years, the growing demand for electronic devices has underscored the critical role of semiconductor manufacturing. This industry focuses on transforming semiconductor materials into integrated circuits, demanding precision and utilizes advanced technologies to etch intricate patterns onto silicon wafers. However, the acquisition of comprehensive datasets pertaining to wafer production encounters formidable challenges such as data imbalance and noise. This research explores the application of machine learning techniques and deep neural networks for the classification of defective wafers, assessing their impact on managing semiconductor manufacturing processes using the Semiconductor Manufacturing Process (SECOM) dataset. Subsequently, various methodologies, including sample-based, instance-based, ensemble learning, and Support Vector Machine approaches, were implemented and rigorously evaluated to provide a thorough comparison.
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