•Rising Complexity in Semiconductor Manufacturing: As chip designs become smaller, more powerful, and more complex, traditional manufacturing processes face limitations. Science-driven AI, informed by physics, chemistry, and material science, offers targeted solutions to these challenges by enabling more accurate modeling, prediction, and optimization.

•Industry Demand for AI Integration: The semiconductor industry is increasingly adopting AI to stay competitive. However, there is a need for a structured platform to discuss best practices, share case studies, and address barriers to adoption.

•Limitations of General AI Models: General-purpose AI models often lack the depth of understanding required for specialized applications like semiconductor manufacturing. When applied to complex problems with limited data, these models can produce results that violate physical laws or fail to account for critical scientific constraints. On the other hand, science-based AI mitigates these risks by embedding physical laws, chemical properties, and domain-specific knowledge into AI algorithms, ensuring accuracy and reliability.

•Accelerating Innovation: The convergence of AI and semiconductor manufacturing has the potential to revolutionize the industry, from advanced chip design to smart factories. Hosting this workshop can catalyze innovation by connecting stakeholders and fostering new ideas.

•Educational Impact: This workshop will provide valuable learning opportunities for participants, helping them stay at the forefront of technological advancements and preparing them for future challenges.