New method of detection and classification of yield-impacting EUV mask defects

Extreme ultraviolet lithography (EUV) advances printability of small size features for both memory and logic semiconductor devices. It promises to bring relief to the semiconductor manufacturing industry, removing the need for multiple masks in rendering a single design layer on wafer. However, EUV also brings new challenges, one of which is of mask defectivity. For this purpose, much of the focus in recent years has been in finding ways to adequately detect, characterize, and reduce defects on both EUV blanks and patterned masks. In this paper we will present an efficient way to classify and disposition EUV mask defects through a new algorithm developed to classify defects located on EUV photomasks. By processing scanning electronmicroscopy images (SEM) of small regions of a photomask, we extract highdimensional local features Histograms of Oriented Gradients (HOG). Local features represent image contents compactly for detection or classification, without requiring image segmentation. Using these HOGs, a supervised classification method is applied which allows differentiating between nondefective and defective images. In the new approach we have developed a superior method of detection and classification of defects, using mask and supporting mask printed data from several metallization masks. We will demonstrate that use of the HOG method allows realtime identification of defects on EUV masks regardless of geometry or construct. The defects identified by this classifier are further divided into subclasses for mask defect disposition: foreign material, foreign material from previous step, and topological defects. The goal of disposition is to categorize on the images into subcategories and provide recommendation of prescriptive actions to avoid impact on the wafer yield.