In the AI-Driven Autonomous Factory of the Future, both AI techniques and Digital Twins play an important role for enhancing fab performance and maximizing fab out.
The presentation will elaborate

(1) How Digital Twins empowered by stochastic simulation can be used for dynamic capacity modelling of wafer fabs, and

(2) How AI techniques will empower the critical step from currently predictive to prescriptive analytics of fab operations by increasing the degree of automation when an AI optimisation agent has to navigate through the mind-blowing complexity of the search space associated with such fab operations.Such AI-driven, simulation-based optimisation enables essential capacity planning and WIP flow optimisation decisions such as
• Prediction of bottlenecks and prescription of actions for maximizing fab output,
• Dynamic capacity allocation based on real-time demand and constraints,
• Prioritization of operational opportunities to simplify or better manage queue time requirements, depending on impact on cycle time, WIP flow and potential excursions,

• Determination of the direct impact of OEE initiatives on overall fab output and prioritization of projects accordingly,

• Optimisation of advanced strategies for wafer start decisions and balancing output maximization with cycle time reduction through intelligent wafer release, taking into account predicted line constraints, capacity, and demand. How the foundation for this has been successfully laid will be showcased through the benefits that have been achieved by Micron through the deployment of the simulation-based D-SIMCON Digital Twin framework and a collaborative effort to further develop these use cases.