26 October 2017 Classification and printability of EUV mask defects from SEM images
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Abstract
Classification and Printability of EUV Mask Defects from SEM images EUV lithography is starting to show more promise for patterning some critical layers at 5nm technology node and beyond. However, there still are many key technical obstacles to overcome before bringing EUV Lithography into high volume manufacturing (HVM). One of the greatest obstacles is manufacturing defect-free masks. For pattern defect inspections in the mask-shop, cutting-edge 193nm optical inspection tools have been used so far due to lacking any e-beam mask inspection (EBMI) or EUV actinic pattern inspection (API) tools. The main issue with current 193nm inspection tools is the limited resolution for mask dimensions targeted for EUV patterning. The theoretical resolution limit for 193nm mask inspection tools is about 60nm HP on masks, which means that main feature sizes on EUV masks will be well beyond the practical resolution of 193nm inspection tools. Nevertheless, 193nm inspection tools with various illumination conditions that maximize defect sensitivity and/or main-pattern modulation are being explored for initial EUV defect detection. Due to the generally low signal-to-noise in the 193nm inspection imaging at EUV patterning dimensions, these inspections often result in hundreds and thousands of defects which then need to be accurately reviewed and dispositioned. Manually reviewing each defect is difficult due to poor resolution. In addition, the lack of a reliable aerial dispositioning system makes it very challenging to disposition for printability. In this paper, we present the use of SEM images of EUV masks for higher resolution review and disposition of defects. In this approach, most of the defects detected by the 193nm inspection tools are first imaged on a mask SEM tool. These images together with the corresponding post-OPC design clips are provided to KLA-Tencor’s Reticle Decision Center (RDC) platform which provides ADC (Automated Defect Classification) and S2A (SEM-to-Aerial printability) analysis of every defect. First, a defect-free or reference mask SEM is rendered from the post-OPC design, and the defective signature is detected from the defect-reference difference image. These signatures help assess the true nature of the defect as evident in e-beam imaging; for example, excess or missing absorber, line-edge roughness, contamination, etc. Next, defect and reference contours are extracted from the grayscale SEM images and fed into the simulation engine with an EUV scanner model to generate corresponding EUV defect and reference aerial images. These are then analyzed for printability and dispositioned using an Aerial Image Analyzer (AIA) application to automatically measure and determine the amount of CD errors. Thus by integrating EUV ADC and S2A applications together, every defect detection is characterized for its type and printability which is essential for not only determining which defects to repair, but also in monitoring the performance of EUV mask process tools. The accuracy of the S2A print modeling has been verified with other commercially-available simulators, and will also be verified with actual wafer print results. With EUV lithography progressing towards volume manufacturing at 5nm technology, and the likelihood of EBMI inspectors approaching the horizon, the EUV ADC-S2A system will continue serving an essential role of dispositioning defects off e-beam imaging.
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Wonil Cho, Wonil Cho, Daniel Price, Daniel Price, Paul A. Morgan, Paul A. Morgan, Daniel Rost, Daniel Rost, Masaki Satake, Masaki Satake, Vikram L. Tolani, Vikram L. Tolani, } "Classification and printability of EUV mask defects from SEM images", Proc. SPIE 10450, International Conference on Extreme Ultraviolet Lithography 2017, 1045006 (26 October 2017); doi: 10.1117/12.2280837; https://doi.org/10.1117/12.2280837
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