Predict metrology values in real time — expand coverage, cut cycle time, and see drift earlier without adding measurements
Applied SmartFactory AI Predictive Metrology uses machine learning to predict metrology values in real time, helping manufacturers reduce dependence on physical measurements, improve sampling strategies, and detect process drift earlier. Confidence-scored predictions, automated model management, continuous retraining, and native quality system connectivity let teams make faster, more reliable process control decisions. For engineering teams, this changes what metrology coverage costs. Instead of choosing between measuring more wafers and protecting cycle time, predicted values fill the gaps between physical measurements.
Applied SmartFactory AI Predictive Metrology reduces cycle time by returning predicted metrology results in real time instead of waiting for physical measurements, and by optimizing the sampling strategies that create metrology bottlenecks. Deployment takes about five months on one unified platform rather than a set of fragmented point solutions. Because every prediction carries a confidence score, engineers know which results are solid enough to act on.
Predictive Metrology helps teams deploy faster, consolidate fragmented point solutions, and fine-tune measurement specifications with confidence.
Predictive Metrology predicts metrology values in real time using machine learning models trained on production data. Each prediction carries a confidence score, so engineers can tell how much weight a given result should carry in a process control decision.
No. It reduces dependence on physical measurements and improves sampling strategies rather than eliminating measurement. The practical gain is increased metrology coverage without adding measurement capacity.
Because predicted values are available in real time rather than after a measurement queue, drift shows up before it impacts production. Confidence-scored predictions also support tighter process control, so smaller shifts can be acted on with less risk of chasing noise.
The solution automatically identifies key features to accelerate model creation, continuously monitors data quality and model performance, and auto-retrains models when drift or performance changes are detected. Model upkeep is part of the platform rather than a separate data science project.
Deployment runs about five months. It lands on one unified platform instead of the fragmented point solutions that typically accumulate around metrology prediction.
It offers native quality system connectivity, so predicted values reach the systems that act on them and accelerate decision-making across the production flow. That connectivity is also what supports optimized UVA specifications.
Predictive Metrology gives engineering teams real-time, confidence-scored values so sampling decisions stop being a trade-off against cycle time.
