Surface Defect Classification in Silicon Wafer Manufacturing Using the Linear-Based Channeling and Rule-Based Binning Algorithms

Developing an accurate means of classifying defects, such as crystal-originated pits, surface-adhered foreign particles, and process-induced defects, using scanning surface inspection systems (SSIS) is of paramount importance because it provides the opportunity to determine the root causes of defects, which is valuable for yield enhancement. This report presents a novel defect classification approach developed by optimizing the linear-based channeling (LBC) and rule-based binning (RBB) algorithms that are applied to a commercially available SSIS (KLA-SP5), in combination with test sample selection including the signature defect patterns associated with the typical crystal growth process. The experimental results demonstrate that defect classification is possible with an accuracy and purity above 80% using the LBC algorithm and 90% using the RBB algorithm.