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The document presents a research paper on an optimal predictive maintenance technique for semiconductor manufacturing using machine learning (ML) methods. It highlights the importance of improving manufacturing efficiency and reducing equipment downtime, with Random Forest Classifier achieving the highest accuracy of over 93.62% in predicting wafer failures. The methodology includes data preprocessing, feature selection, and model evaluation using various ML algorithms to enhance productivity while minimizi

Author

kalyan010482

Language

EN