Fast Global Sparse Principal Component Analysis for Sensor Reduction in Virtual Metrology

In semiconductor manufacturing, virtual metrology (VM) leverages high-dimensional sensor data for real-time quality estimation. However, excessive sensor deployment leads to increased operational costs, data redundancy, and system complexity due to substantial infrastructure, installation, and maintenance requirements. To address these manufacturing challenges, we propose a Fast Global Sparse Principal Component Analysis (FGS-PCA) framework for systematic sensor reduction in VM applications. FGS