Improvement in accuracy of defect size measurement by automatic defect classification

The blank mask defect review process involves detailed analysis of defects observed across a substrate's multiple preparation stages, such as cleaning and resist-coating. The detailed knowledge of these defects plays an important role in the eventual yield obtained by using the blank. Defect knowledge predominantly comprises of details such as the number of defects observed, and their accurate sizes. Mask usability assessment at the start of the preparation process, is crudely based on number of defects. Similarly, defect size gives an idea of eventual wafer defect printability. Furthermore, monitoring defect characteristics, specifically size and shape, aids in obtaining process related information such as cleaning or coating process efficiencies. Blank mask defect review process is largely manual in nature. However, the large number of defects, observed for latest technology nodes with reducing half-pitch sizes; and the associated amount of information, together make the process increasingly inefficient in terms of review time, accuracy and consistency. The usage of additional tools such as CDSEM may be required to further aid the review process resulting in increasing costs. Calibre (R) MDPAutoClassify (TM) provides an automated software alternative, in the form of a powerful analysis tool for fast, accurate, consistent and automatic classification of blank defects. Elaborate post-processing algorithms are applied on defect images generated by inspection machines, to extract and report significant defect information such as defect size, affecting defect printability and mask usability. The algorithm's capabilities are challenged by the variety and complexity of defects encountered, in terms of defect nature, size, shape and composition; and the optical phenomena occurring around the defect [1]. This paper mainly focuses on the results from the evaluation of Calibre (R) MDPAutoClassify (TM) product. The main objective of this evaluation is to assess the capability of accurately estimating the size of the defect from the inspection images automatically. The sensitivity to weak defect signals, filtering out noise to identify the defect signals and locating the defect in the images are key success factors. The performance of the tool is assessed on programmable defect masks and production masks from HVM production flow. Implementation of Calibre (R) MDPAutoClassify (TM) is projected to improve the accuracy of defect size as compared to what is reported by inspection machine, which is very critical for production, and the classification of defects will aid in arriving at appropriate dispositions like SEM review, repair and scrap.