Paper
26 August 2024 12th eBeam initiative survey reports semiconductor industry luminaries are confident in high-NA EUV and curvilinear photomasks
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Abstract
The eBeam Initiative completed its 12th annual opinion survey in July 2023 with anonymous feedback from industry luminaries representing 47 companies from across the semiconductor ecosystem – including photomasks, electronic design automation (EDA), chip design, equipment, materials, manufacturing and research. Started in 2012, the Luminaries Survey is used each year to gather predictions of industry trends. 80 percent of survey respondents in 2023 believe that broad adoption of high-NA EUV lithography in high-volume manufacturing (HVM) by more than one company will occur by 2028, the same percentage as reported in last year’s survey. In another trend that was repeated from last year’s survey, confidence that leading-edge mask shops can handle curvilinear mask demand doubled in the 2023 results compared to 2022, while 87 percent predicted that leading-edge mask shops can handle at least a limited number of curvilinear masks. New questions were added to the 12th annual Luminaries Survey to gauge perceptions on EUV and non-EUV leading-edge masks and patterning. 70 percent say that curvilinear inverse lithography technology (ILT) is useful for non-EUV leading-edge nodes, while 75 percent agree that it’s needed for 2-nm, 0.33 NA EUV. The percentage of EUV masks inspected by actinic inspection is predicted to double in three years, from a weighted average of 30 percent in 2023 to 63 percent in 2026. In addition, 95 percent agree that multi-beam mask writers are needed to write EUV masks.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
Aki Fujimura and Jan Willis "12th eBeam initiative survey reports semiconductor industry luminaries are confident in high-NA EUV and curvilinear photomasks", Proc. SPIE 13177, Photomask Japan 2024: XXX Symposium on Photomask and Next-Generation Lithography Mask Technology, 131770A (26 August 2024); https://doi.org/10.1117/12.3031849
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KEYWORDS
Extreme ultraviolet

High volume manufacturing

Photomasks

Inspection

Pellicles

Deep learning

Semiconductors

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