Semiconductor Yield Analysis and Multi-Chip Package (MCP) Die Pairing Optimization using Statistical-Learning

In this paper we discuss the advancement and applications of Tree based classification and regression methods to semiconductor data. We begin the paper with a description of the problem, followed by an overview of the statistical-learning techniques we use in our case studies. We then describe how the challenges presented by semiconductor data were addressed with original extensions to tree-based and kernel-based methods. Next, we review four case studies: home sales price prediction, signal identification/separation, final speed bin classification and die pairing optimization for multi-chip packages (MCP). Results from the case studies demonstrate how statistical-learning addresses the challenges presented by semiconductor manufacturing data and enables improved data discovery and prediction when compared to traditional statistical approaches