This vendor solution brief explains how high-volume manufacturers use multivariate machine learning to detect subtle product anomalies that traditional univariate testing cannot identify. Focusing on semiconductor manufacturing, it describes how products with different characteristics can be unintentionally mixed during assembly yet still pass final tests because individual measurements remain within specification limits. The document outlines a multistep approach using principal component analysis and hierarchical clustering to amplify small differences across hundreds of test parameters. When cluster linkage distances exceed defined thresholds, the system automatically alerts engineers before products ship. Interactive dashboards allow rapid investigation, yield impact assessment, and co

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