Semiconductor manufacturing process optimization (SMPO) is at a critical inflection point where AI/ML-driven digital twins can accelerate yield ramp, reduce costs, and enable faster time-to-market for new nodes. Yet deploying digital twin technologies in fabs presents unique challenges. Data access is a major bottleneck, with terabytes of tool, metrology, and test data from disparate systems ingested into warehouses, making retrieval of task-specific data challenging—especially for R&D teams lacking query expertise. Data acquisition for new nodes is also prohibitively expensive: each fab trial costs millions, takes months, and multi-physics simulations are computationally intensive, with limited physics-based models available for many fab processes. The complexity of the fab process also requires the development of customized solutions at scale. To address these challenges, we propose a comprehensive AI platform that empowers R&D and process engineers to build and deploy digital twins, enabling seamless data acquisition, faster insights, intelligent decisions, and scalable process optimization.
