Anomaly Detection in semiconductor manufacturing is the identification of abnormal process conditions, wafer measurements, or equipment behaviors — using statistical, model-based, or ML methods to flag observations that deviate significantly from normal operating patterns.

Key Anomaly Detection Approaches

• Multivariate SPC: Hotelling T² and Q-statistics detect multivariate outliers.
• Isolation Forest: Randomly partitions data and measures how quickly observations are isolated.
• Autoencoders: Neural networks trained to reproduce normal data — anomalies have high reconstruction error.
• One-Class SVM: Learns the boundary of normal operation and flags points outside it.

Why It Matters

• Excursion Detection: Catches process excursions before they produce wafers out of spec.
• Predictive Maintenance: Detects early equipment degradation signatures before failure.
• Rare Events: Anomaly detection is more practical than classification for rare failure modes (limited examples).

Anomaly Detection is the automatic alarm system — continuously monitoring process data to flag anything that doesn't look normal.