Auto-classification and simulation of mask defects using SEM and CAD images

Tung-Yaw Kang; Hsin-Chang Lee; H. Zhang; K. Yamada; Y. Kitayama; K. Kobayashi; Peter Fiekowsky

doi:10.1117/12.801411

17 October 2008 Auto-classification and simulation of mask defects using SEM and CAD images

Tung-Yaw Kang, Hsin-Chang Lee, H. Zhang, K. Yamada, Y. Kitayama, K. Kobayashi, Peter Fiekowsky

Proceedings Volume 7122, Photomask Technology 2008; 71221F (2008) https://doi.org/10.1117/12.801411
Event: SPIE Photomask Technology, 2008, Monterey, California, United States

Abstract

Mask defect disposition gets more difficult and time-consuming with each progressive lithography node. Mask inspection tools commonly use 250 nm wavelength, giving resolution of 180 nm, so critical defect sizes are far less than the optical resolution - too small for defect analysis. Thus the rate of false or nuisance defect detection is increasing rapidly and analysis of detected defects is increasingly difficult. As to judging the wafer printability of defects, AIMS (Aerial Image Measurement System) tools are commonly used but are also time-consuming if defect count is high. For improving the efficiency of mask defect disposition, we propose the combination of a SEM defect review tool and defect disposition and simulation software, which use high-resolution SEM images of defects to do defect review, defect disposition, and wafer printing simulation of defects automatically or manually. The SEM defect review tool, DIS-05 developed by Holon Co. Ltd., is designed for defect review and disposition using reference images derived from e-beam files or CAD database. This tool uses the Automated Defect Analysis Software (ADAS) developed from AVI LLC. to interface the inspection tool and the DIS-05. ADAS detects false defects before SEM imaging and performs aerial image simulation from the SEM and CAD images to estimate the wafer CD error caused by each defect. We report on its speed (>300 defects/hour), classification accuracy and simulation accuracy when used with masks at the 45 nm technology node and beyond. This combination of SEM and ADAS is expected to significantly accelerate process development and production for the 45 and 32 nm nodes. It will also increase the masksper- day throughput of inspection and AIMS tools by shifting most defect review to ADAS software using SEM images. At preliminary tests showed the combination tool can do auto defect disposition and simulation with promising results.

Citation Download Citation

Tung-Yaw Kang, Hsin-Chang Lee, H. Zhang, K. Yamada, Y. Kitayama, K. Kobayashi, and Peter Fiekowsky "Auto-classification and simulation of mask defects using SEM and CAD images", Proc. SPIE 7122, Photomask Technology 2008, 71221F (17 October 2008); https://doi.org/10.1117/12.801411

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