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Proceedings Paper

Edge placement errors in EUV from aberration variation
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Paper Abstract

With several foundries/IDMs committed to using EUV to manufacture devices at the 7 nm and 5 nm nodes, the success of EUVL will depend critically on the ability of manufacturers to meet extremely tight edge placement error (EPE) budgets. EPE is affected by many factors and it becomes important to identify and address all systematic sources of edge placement error. One major source of this error, which hasn’t been given a lot of attention, continues to be the magnitude and variation of aberrations across the exposure field and between different scanners. EUV scanners are known to have significantly higher level of aberrations than DUV scanners due to the substantial drop in wavelength requiring tighter specifications on lens roughness, as well as a move to reflective optics producing double pass impact of surface roughness.

While the EPE from variation in aberrations across the exposure field is correctable in OPC software, there are no known ways to address tool-to-tool aberration variation. Given that foundries are expected to have multiple EUV tools for high volume manufacturing, the degree of tool-matching between different machines is expected to play a critical role to the success of EUV. This work seeks to further the study by quantifying the simulated edge placement error on realistic 7 nm / 5 nm node designs resulting from a fleet consisting of multiple EUV tools, under the assumption of single OPC model / mask for multiple tools and whether such assumptions are valid. Given the importance of tool-to-tool aberration matching in EUVL, this study investigates the amount of variation in tool-to-tool aberration that can be tolerated before foundries must consider tool dedicated OPC mask sets. This study statistically analyzes different metrics such as EPEs, image shifts and worst case excursions to understand which single tool in the fleet should be best used in model calibration to generate the OPC mask shapes. In addition, an effort to rank relative quality of the verification solutions is investigated, to be used to tool allocation.

Paper Details

Date Published: 16 October 2017
PDF: 9 pages
Proc. SPIE 10451, Photomask Technology 2017, 104510W (16 October 2017);
Show Author Affiliations
Ananthan Raghunathan, Mentor Graphics Corp. (United States)
Germain Fenger, Mentor Graphics Corp. (United States)
Michael Lam, Mentor Graphics Corp. (United States)
Chris Clifford, Mentor Graphics Corp. (United States)
Kostas Adam, Mentor Graphics Corp. (United States)
John Sturtevant, Mentor Graphics Corp. (United States)

Published in SPIE Proceedings Vol. 10451:
Photomask Technology 2017
Peter D. Buck; Emily E. Gallagher, Editor(s)

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