As semiconductor manufacturing evolves in complexity and scale, the importance of intelligent, data-driven facility management grows exponentially. At Intel, we are innovating the future of semiconductor manufacturing by integrating physics-based digital twins with advanced analytics, machine learning, and subject matter expertise to optimize critical facility systems. One of the key platforms enabling this transformation is Honeywell's Asset Performance Management (APM), which serves as the backbone for managing our digital twin infrastructure. Our digital twin framework is built on a foundation of high-fidelity, physics-based models representing critical infrastructure assets such as chilled water plants, oil-free air compressors, and exhaust systems. These models mirror the behaviour of physical systems in close-to-real-time and are calibrated using historical and live sensor data. Integrating these models with machine learning algorithms and codified fault rules allows for intelligent, automated insights grounded in theoretical and empirical knowledge. A cornerstone of our approach is the seamless fusion of domain expertise into the analytics layer. Facility subject matter experts (SMEs) have contributed their deep operational knowledge to developing fault detection logic and failure mode libraries encoded into our models and analytics pipelines. This ensures that the digital twin doesn’t just detect anomalies—it understands them in context, enabling actionable and accurate root cause analysis. We have successfully deployed this integrated digital twin system on critical assets across multiple Intel facilities. These implementations support automated anomaly detection, real-time root cause analysis, and predictive “what-if” simulations. The combination of physics-based models and machine learning enables early identification of system degradation, efficiency loss, or emerging operational risks—often before they manifest in production-impacting ways. One of the most transformative applications of our digital twin system is in the command-and-control layer, where it serves as an optimisation engine for facility operations. Our chilled water plant operations are automatically adjusted based on real-time digital twin outputs. This command-and-control capability continuously evaluates operating conditions and environmental variables to fine-tune equipment settings, reduce energy consumption, and maximise system performance without compromising reliability or availability. This initiative exemplifies how a hybrid intelligence approach, merging human expertise with AI and physics-based modelling, can unlock new levels of operational excellence in semiconductor manufacturing. By grounding our digital twin strategy in rigorous engineering principles while enhancing it with data-driven learning and SME insight, Intel is setting a new standard for proactive, scalable facility management. As we look to the future, we see these systems playing an even greater role in enabling autonomous operations, driving sustainability goals, and supporting rapid scaling of advanced manufacturing capacity.
