Machine Learning for Semiconductor Manufacturing Optimization

The semiconductor manufacturing industry is at the core of technological innovation, yet it faces persistent challenges related to process complexity, defect control, and yield variability. This study explores the integration of machine learning (ML) techniques to optimize semiconductor fabrication processes, focusing on yield prediction, defect detection, and real time process optimization. Using a multi-phase methodology, the research incorporates supervised learning models (e.g., linear regression, random forests, neural networks), unsupervised techniques (e.g., K means, DBSCAN), and reinforcement learning algorithms (e.g., Q learning, Deep Q Networks). The findings reveal that neural networks achieve superior performance in yield prediction with an R² of 0.97, while DBSCAN outperforms traditional clustering methods in defect detection, achieving a cluster purity of 90%. Deep Q Networks further demonstrate the potential for dynamic process optimization by reaching a reward score of 0.92. Despite the promise shown, challenges such as data quality, computational requirements, and real time deployment persist. This research provides valuable insights into the application of machine learning for enhancing efficiency, accuracy, and sustainability in semiconductor manufacturing.