Mr. Jinhyuk Choi Profile

Jinhyuk Choi

at SK hynix Inc.

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Proceedings Article | 20 November 2024 Poster + Paper

Automatic classification of EUV mask defects inspected using DUV inspection optics

Aravindh Rajiv, Samir Bhamidipati, Mark Pereira, Sankaranarayanan Paninjath, Neha Razdan, Stephen Kim, Jinhyuk Choi, Jonghoon Lim, Sujeong Won, Dongwook Lee, Woojin Kim, Jongha Park

Proceedings Volume 13216, 132162I (2024) https://doi.org/10.1117/12.3034707

KEYWORDS: Inspection, Extreme ultraviolet, Deep ultraviolet, Polarization, Image classification, Image processing

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EUV lithography has been well-established over the past few years, to be the next technological milestone, to achieve lower pattern resolutions i.e. higher technology nodes. Ensuring a lower defect count however is particularly important for its use in mass-production, because of the considerably high cost of EUV masks. EUV wavelength (actinic EUV inspection) is necessary to be able to catch and characterize EUV defects correctly, particularly phase defects, originating due to imperfections in multi-layers. Defects on a patterned mask require EUV wavelengths (higher resolution) for accurate assessment of their CD impact. However, DUV optics are also employed by mask shops, along with SEM review, as a cost-effective option. Emphasizing the importance of defect handling in EUV masks, especially for EUV masks without pellicles in the memory industry, the most difficult part of EUV mask management is in controlling adder. Detecting smaller adder size is required as mask pattern size is getting smaller in higher technology nodes, which accompanies increased sensitivity of patterned mask inspection and unavoidable increase in noise signal that makes it hard to proper classify mask defects. This paper talks about the details of how DUV inspection optics are utilized to achieve an efficient pre-filtering of EUV mask defects. This, coupled with well-tested automatic defect classification algorithms of Calibre^® DefectClassify, results in a reliable solution to manage regular classification of mask defects as real or false, under such sensitive conditions.

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