In the intricate operations of a semiconductor facility, the reliability of supporting infrastructure—vacuum systems, chemical distribution, HVAC, emissions controls—is as critical as the fabrication process itself. These subsystems form the invisible scaffolding of manufacturing excellence, and their failure can compromise not just throughput, but compliance, safety, and environmental performance. Yet, many facility teams still rely on preventive or time-based maintenance schedules that often miss early indicators of system degradation. This presentation introduces a modern, AI-enabled architecture for predictive maintenance that transforms facility operations from reactive to anticipatory through the orchestration of intelligent, context-aware agents.
At the heart of this transformation lies a model-based protocol that contextualizes raw signals from distributed assets. These real-time data streams are interpreted by autonomous agents that simulate expert reasoning to detect anomalies, assess probable failure modes, and trigger corrective workflows. Unlike traditional threshold-based alerts, this framework leverages both temporal patterns and contextual metadata to understand when, why, and how to respond. Importantly, all actions—such as generating a work order, initiating escalations, or notifying technicians—are traceable, policy-driven, and configurable by human operators, offering transparency and control at every step.
The use of structured model context enables seamless interoperability between systems of record, sensor networks, and decision agents. This allows for targeted responses that reflect the specific asset, its operational history, environmental conditions, and priority within the facility. When conditions of interest are met, actions are autonomously proposed or executed—ranging from digital task generation to direct communication with operations personnel—closing the loop between sensing, understanding, and acting.
We deployed this architecture across multiple facility systems in a high-volume semiconductor manufacturing environment. Through this deployment, we observed measurable gains in uptime, reduced maintenance-related disruptions, and more efficient resource planning. Maintenance events are now triggered based on actual asset health rather than rigid calendar schedules, extending equipment longevity and lowering total cost of ownership. The inclusion of human-in-the-loop (HITL) mechanisms ensures that all automated decisions remain auditable, explainable, and aligned with organizational and safety policies.
Beyond operational metrics, this approach fosters a new mindset—one in which maintenance becomes a dynamic, self-evolving system rather than a static checklist. AI agents refine their predictions continuously, incorporating historical performance, sensor drift, operational variability, and failure feedback. Over time, the system gains precision, adaptability, and trust. Furthermore, the modular nature of the architecture ensures that new facility subsystems, asset types, or workflows can be integrated with minimal friction, providing a clear path to long-term scalability and innovation.
This architecture redefines our relationship with maintenance—transforming it from a necessary overhead into a strategic enabler. Attendees will gain an inside view of how predictive AI, harmonized with contextual protocols and automated response loops, is elevating reliability engineering into an intelligent conversation—between machines, systems, and humans—that is reshaping the future of semiconductor facility management.
