This course introduces graduate students to the theory and practice of applying artificial intelligence and machine learning to semiconductor process technology, metrology, manufacturing, and digital twins. The semiconductor industry is undergoing a transformation driven by the increasing complexity of advanced process nodes and the explosion of data generated in modern fabrication facilities. AI/ML techniques and digital twin frameworks are now critical tools for process modeling, equipment monitoring, defect inspection, yield optimization, and accelerating technology development cycles.

Designed for students with a background in semiconductor devices and processes but no prior AI/ML experience, the course begins with a rigorous introduction to machine learning fundamentals using Python, then progressively applies these techniques to real-world semiconductor challenges. Topics include surrogate modeling for TCAD, physics-informed neural networks, virtual metrology, SEM defect classification, yield prediction, fault detection, digital twins for fab modules, and ML-assisted technology development.

The course emphasizes hands-on learning through four Python labs using open semiconductor datasets, two problem-set homeworks, and a substantial semester-long research project. Industry guest lectures provide direct exposure to how these methods are deployed in production fabs and R&D environments.