Outlier Detection in semiconductor data analysis is the identification and handling of data points that are significantly different from the majority — distinguishing real process excursions (which need investigation) from measurement errors or artifacts (which need removal).

Key Outlier Detection Methods

• Statistical: Z-score ($|z| > 3$), IQR method ($< Q_1 - 1.5 cdot IQR$ or $> Q_3 + 1.5 cdot IQR$), Grubbs' test.
• Multivariate: Mahalanobis distance, PCA residuals (Q-statistic), robust covariance.
• ML-Based: Isolation forest, Local Outlier Factor (LOF), autoencoders.
• Domain-Specific: EE box (Equipment Engineering spec limits), out-of-control SPC rules.

Why It Matters

• Data Quality: Outliers can corrupt statistical models, virtual metrology, and SPC charts.
• Root Cause: Some outliers indicate real process issues — automatic removal without investigation risks missing critical signals.
• Balanced Approach: Industrial practice flags outliers for review rather than automatic deletion.

Outlier Detection is separating signal from noise — identifying abnormal data points that need investigation or removal for reliable analysis.