Multivariate Analysis (MVA) in semiconductor manufacturing is the statistical analysis of high-dimensional process and metrology data — using techniques like PCA, PLS, and clustering to extract patterns, detect anomalies, and identify root causes from hundreds of correlated process variables.

Key MVA Techniques

• PCA (Principal Component Analysis): Reduces dimensionality, identifies dominant variation patterns.
• PLS (Partial Least Squares): Relates process variables to quality outcomes.
• MSPC (Multivariate SPC): Hotelling T² and Q-statistic for multivariate process monitoring.
• Contribution Plots: When MSPC detects an anomaly, contribution plots identify which variables caused it.

Why It Matters

• Hundreds of Variables: Modern process tools generate 100-1000+ sensor readings — univariate SPC cannot handle this.
• Correlated Variables: MVA naturally handles correlations between variables (temperature, pressure, flow are interdependent).
• Root Cause: Contribution analysis identifies which specific variables are responsible for detected anomalies.

MVA is seeing the big picture in process data — extracting meaningful patterns from the overwhelming dimensionality of modern fab data.