Predictive Maintenance

Sensor Data & Feature Engineering

Equipment traces, FDC data, time-series features, and health indicators

Understanding FDC Data

Understanding FDC Data

FDC (Fault Detection and Classification) data is the foundation of PdM in fabs. Understanding its structure is essential:

  • Trace data: Time-series sensor readings during each process run. Example: chamber pressure sampled at 10 Hz during a 60-second etch step = 600 data points per parameter per run.
  • Summary statistics: Aggregated values per run — mean, std dev, max, min, slope of each parameter during each recipe step. This is often the starting point for ML models.
  • Context data: Which chamber, which recipe, lot ID, wafer slot, timestamp, PM history, consumable age.

Key Concept: Feature Engineering for Equipment Data

Raw sensor time series must be transformed into meaningful features. Common approaches:

  • Step-level statistics (mean, std, trend, range per recipe step)
  • Deviation from golden trace (DTW distance, residual analysis)
  • Rolling statistics over recent runs (moving average, EWMA)
  • Time since last PM, cumulative RF hours, wafer count
  • Rate of change features (how fast is a parameter drifting?)

Building Health Indicators

Building Health Indicators

A health indicator (HI) is a derived metric that tracks equipment degradation over time. Good HIs should:

  • Monotonically degrade: Consistently trend in one direction as the component ages
  • Be prognostic: Start changing well before failure occurs, giving time to plan maintenance
  • Be interpretable: Engineers should understand what physical degradation the HI reflects

Example HIs for a PVD chamber:

  • Target life remaining (based on deposition rate trend and voltage drift)
  • Chamber matching score (statistical distance from reference chamber)
  • Particle adder trend (from periodic blank wafer inspections)

Analogy: Blood Pressure for Machines

Health indicators are like vital signs for equipment. Just as a doctor monitors blood pressure, heart rate, and cholesterol to predict cardiovascular risk, a PdM system monitors equipment vitals to predict maintenance needs.

Knowledge Check

Knowledge Check

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What is FDC data in semiconductor manufacturing?