Principal Component Analysis (PCA) is a powerful technique for reducing the dimensionality of complex datasets while preserving as much variance as possible. In the context of our chip inspections, PCA helps us identify patterns and relationships in the PSA signature data across different samples and inspections. By projecting the high-dimensional PSA data onto a lower-dimensional space, we can visualize clusters of similar signatures, detect outliers, and gain insights into underlying factors that may be influencing chip performance. The interactive PCA chart below allows you to explore these relationships and better understand the similarities and differences between samples based on their PSA signatures.