Predictive Maintenance Across Fab Equipment

In semiconductor fabs, unplanned tool downtime affects far more than the tool itself — it disrupts lot flow, creates yield excursion risk from process interruptions, and can take days to recover. Predictive maintenance converts tool health management from reactive to proactive.

The Challenge

Semiconductor equipment operates under extreme process conditions with thousands of monitored parameters per tool. Identifying which signals predict impending failure — versus normal process variation — requires sophisticated pattern recognition across massive real-time data streams that preventive maintenance schedules alone cannot address.

Our Approach

• Equipment health baselines — Statistical models of normal equipment operating signatures across all tool types, enabling detection of early-stage degradation patterns.
• Predictive failure models — ML models trained on historical tool failure data that predict chamber and component failures 24-72 hours in advance.
• Maintenance scheduling integration — Predictive alerts linked to PM scheduling systems, enabling maintenance windows to be planned during low-utilisation periods rather than after failures.

The Outcome

Equipment engineers shift focus from emergency response to planned maintenance execution — maximising tool availability and protecting the yield-critical process windows that unplanned downtime disrupts.