cluster analysis

**Cluster Analysis** in semiconductor manufacturing is the **unsupervised grouping of wafers, lots, or process runs into similar clusters** — identifying natural groupings in process data that may correspond to different process states, equipment conditions, or failure modes. **Common Clustering Methods** - **K-Means**: Partition data into $K$ clusters minimizing within-cluster variance. - **Hierarchical**: Build a dendrogram of nested clusters by iterative merging/splitting. - **DBSCAN**: Density-based clustering that finds arbitrary-shaped clusters and identifies outliers. - **Gaussian Mixture Models**: Probabilistic soft clustering with cluster shape flexibility. **Why It Matters** - **Process Grouping**: Identifies that wafers naturally fall into distinct groups (good vs. marginal vs. bad). - **Equipment Comparison**: Clusters tool-to-tool variation to identify systematic equipment differences. - **Failure Classification**: Groups defect signatures into categories for automated root cause analysis. **Cluster Analysis** is **finding natural groups in fab data** — letting the data reveal its own structure for equipment matching, failure classification, and process optimization.