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9 July 2015 Automatic classification and accurate size measurement of blank mask defects
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A blank mask and its preparation stages, such as cleaning or resist coating, play an important role in the eventual yield obtained by using it. Blank mask defects’ impact analysis directly depends on the amount of available information such as the number of defects observed, their accurate locations and sizes. Mask usability qualification at the start of the preparation process, is crudely based on number of defects. Similarly, defect information such as size is sought to estimate eventual defect printability on the wafer. Tracking of defect characteristics, specifically size and shape, across multiple stages, can further be indicative of process related information such as cleaning or coating process efficiencies. At the first level, inspection machines address the requirement of defect characterization by detecting and reporting relevant defect information. The analysis of this information though is still largely a manual process. With advancing technology nodes and reducing half-pitch sizes, a large number of defects are observed; and the detailed knowledge associated, make manual defect review process an arduous task, in addition to adding sensitivity to human errors. Cases where defect information reported by inspection machine is not sufficient, mask shops rely on other tools. Use of CDSEM tools is one such option. However, these additional steps translate into increased costs. Calibre NxDAT based MDPAutoClassify tool provides an automated software alternative to the manual defect review process. Working on defect images generated by inspection machines, the tool extracts and reports additional information such as defect location, useful for defect avoidance[4][5]; defect size, useful in estimating defect printability; and, defect nature e.g. particle, scratch, resist void, etc., useful for process monitoring. The tool makes use of smart and elaborate post-processing algorithms to achieve this. Their elaborateness is a consequence of the variety and complexity of defects encountered. The variety arises due to factors such as defect nature, size, shape and composition; and the optical phenomena occurring around the defect. This paper focuses on preliminary characterization results, in terms of classification and size estimation, obtained by Calibre MDPAutoClassify tool on a variety of mask blank defects. It primarily highlights the challenges faced in achieving the results with reference to the variety of defects observed on blank mask substrates and the underlying complexities which make accurate defect size measurement an important and challenging task.
© (2015) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Samir Bhamidipati, Sankaranarayanan Paninjath, Mark Pereira, and Peter Buck "Automatic classification and accurate size measurement of blank mask defects", Proc. SPIE 9658, Photomask Japan 2015: Photomask and Next-Generation Lithography Mask Technology XXII, 96580X (9 July 2015);


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