The Importance of Process Time Prediction for Factories

The Importance of Process Time Prediction for Factories

Semiconductor fabs are more complex than ever and fabs are managing thousands of lots, hundreds of tools, and constant pressure to deliver on time. At the center of this operational puzzle is a deceptively simple question: How long will each process step actually take?

For decades, fabs have relied on historical averages to estimate these times. While serviceable in stable environments, these methods break down as variability increases. Lot size, recipe complexity, chamber configuration, sequence effects, and even concurrent tool usage can dramatically shift actual processing durations. When predictions drift, routing decisions compound those errors, leading to AMHS congestion, suboptimal tool loading, missed delivery targets, and added manual effort.

This is where modern machine-learning models are changing the equation. By capturing far richer contextual data such as tool behavior, chamber combinations, lot characteristics, and time-dependent patterns, ML-based predictions offer significantly higher accuracy. Recent work at INFICON shows error reductions of more than 2.6× compared to traditional approaches, directly translating into better scheduling precision, higher utilization, and earlier detection of tool performance issues.

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Equally important is what accurate prediction enables: smarter digital twins, more adaptive dispatching, and real-time decision engines that guide WIP through the fab with fewer bottlenecks. In one deployment, integrating predictive models with scheduling logic cut material transport times by over 50%, demonstrating how deeply these improvements affect fab-wide performance.

As fabs continue to scale in complexity, process time prediction is shifting from a useful enhancement to a core operational requirement. It forms the backbone of intelligent manufacturing and will increasingly define competitive advantage in high-volume semiconductor production.