As semiconductor features have reached atomic scales and process windows tighten, achieving and sustaining 99% yield is no longer just a statistical goal, but a systemic mandate. Modern semiconductor fabrication facilities (fabs) are increasingly turning to digital twins for process control, but today's challenges go beyond detection and visualization. The industry now demands predictive intelligence capable of enabling real-time optimization, root-cause traceability, and self-adaptive control, especially within the sub-fab, where unseen systems critically influence wafer quality.
This talk explores how PhysicsX is advancing the use of physics-based AI and high-fidelity digital twins to transform fabrication plant infrastructure into a dynamic source of predictive insight. From vacuum pumps to chillers to exhaust systems, we model complex multi-physics behavior, including flow, heat, vibration, and pressure dynamics, across entire process chains, delivering forward-looking performance insights at unprecedented speed and scale.
By embedding these real-time physics AI-powered digital twins into the operations of fabs, we enable proactive adjustments to process parameters before yield-impacting variation occurs. This includes predictive maintenance of critical equipment, in-process recipe optimization, and the ability to trace quality deviations back through the stack of interacting systems to their origin. These capabilities reduce downtime, improve throughput, and ensure fabs can recover to 99% yield faster when anomalies occur.
The vision we are building at PhysicsX is a fab where every asset is part of a continuous learning system, autonomously modeling, predicting, and optimizing to support quality. Our work bridges the gap between machine-level behavior and process-level outcomes, unlocking new levels of traceability, adaptability, and yield resilience across the semiconductor manufacturing lifecycle.
