Automated mask and wafer defect classification using a novel method for generalized CD variation measurements

Over the years, mask and wafer defect dispositioning has become an increasingly challenging and time-consuming task. With design rules getting smaller, OPC getting more complex and scanner illumination taking on free-form shapes - the possibility of a user to perform both accurate and repeatable classification of defects detected by inspection tools into pass/fail bins has reduced. The critical challenge of mask defect metrology for small nodes (< 30 nm) was reviewed in [1]. While Critical Dimension (CD) Variation measurement is still the method of choice for determining a mask defect future impact on wafer, the high complexity of OPCs combined with high variability in pattern shapes poses a challenge for any automated CD Variation (CDV) measurement method. In this study, a novel approach for measurement generalization is presented. CD Variation assessment performance is evaluated on multiple different complex shape patterns, and is benchmarked against an existing qualified measurement methodology.