Data Quality Control in Semiconductor Manufacturing through Automated ETL Processes and Class Imbalance Handling Techniques

In semiconductor manufacturing, ensuring data quality is crucial for maintaining high production efficiency and product consistency. However, missing values, noise, and class imbalance in sensor data complicate the quality control process. This paper proposes a comprehensive framework that automates data cleaning and quality control by integrating ETL processes, advanced interpolation techniques, and class imbalance handling methods. A feature selection mechanism based on a voting strategy is introduced to optimize model predictions. Our research on real semiconductor manufacturing data validates the accuracy of the proposed method in improving data quality, yield, and defect detection prediction accuracy. This contributes to advancing data quality control in semiconductor manufacturing and provides a practical approach for future research in industrial data management and predictive maintenance.