Effect of Defects in Silicon Carbide Epitaxial Layers on Yield and Reliability

Abstract

Inline metrology tools are widely used to detect defects in SiC epitaxial layers. The defect statistics are used in a variety of ways to determine quality, pass/fail and screen affected die. In this work, we document the automated detection and classification of various epitaxial defects based on type and origin. We further classify these categories into killer and non-killer defects and compare them to the electrical yield of Schottky Diodes. The origins of these defects are determined in broad categories, resulting in a clustering and yield-scaling model, which agrees very closely to experimental data. Further, we look at on-wafer screening techniques of potential weak die by both defect tagging and unclamped inductive switching (UIS) stress testing. Successful 1000-hr reliability tests show the robustness of our detection and screening methods.