One technique that made a measurable difference in improving yield during semiconductor fabrication was implementing real-time defect pattern analysis using machine learning. Instead of relying solely on post-process inspection, we integrated an inline monitoring system that flagged potential defect clusters during the photolithography stage. By training the model on historical wafer maps and defect signatures, I could predict and correct issues—like misalignments or contamination—before they propagated downstream. This proactive adjustment reduced rework and scrap significantly. Within the first quarter of deployment, overall yield improved by about 7%, and process variability decreased across multiple production lines. The biggest benefit, however, was consistency—what used to require manual intervention became a data-driven feedback loop. That experience reinforced for me that yield improvement isn't just about better equipment; it's about smarter analytics. When process data becomes predictive rather than reactive, quality and efficiency naturally follow.