Semiconductor defect data reduction for process automation and characterization

Abstract

Automation tools for semiconductor defect data analysis are becoming necessary as device density and wafer sizes continue to increase. These tools are needed to efficiently and robustly process the increasing amounts of data to quickly characterize manufacturing processes and accelerate yield learning. An image-based method is presented for analyzing process signatures from defect data distributions. Applications are presented of enhanced statistical process control, automatic process characterization, and intelligent sub-sampling of event distributions for off-line high-resolution defect review.