Semiconductor manufacturing increasingly relies on AI to anticipate process deviations, optimize yield, and build trust. This course equips you with practical skills to design, evaluate, and communicate AI-driven early-warning systems that protect yield and sustain long-term accountability. Through fab scenarios, you work with SPC data, equipment health logs, and governance frameworks to make forward-looking, actionable decisions rather than reactive analyses.

By the end of this course, you will be able to deploy a random-forest model on historical SPC data to predict critical dimension (CD) excursions 12 hours ahead and document the model’s precision and recall. You will also correlate equipment health logs with wafer-level yield losses across three fabs to pinpoint the two most significant predictive sensors, and define a governance framework that formalises model retraining cadence, data-quality gates, and escalation paths for presentation at the monthly staff meeting. Experience in semiconductors, yield or process engineering, or manufacturing analytics, along with familiarity with SPC and fab data workflows, is required. Hands-on exercises, predictive modeling, sensor analytics, and governance simulations provide you with the skills to anticipate problems, interpret complex datasets responsibly, and implement AI as a trusted operational capability in production environments.