Open Access
11 December 2014 Impact of B4C capping layer for extreme ultraviolet mask on the sensitivity of patterned mask inspection using a projection electron microscope
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Abstract
The inspection sensitivity of a patterned extreme ultraviolet mask with B4C-capped multilayer (ML) was investigated using a simulated projection electron microscope (PEM) image. Extrusion and intrusion defects with 16-nm size were detected with their intensity of >10 times the standard deviation of the background level on a half-pitch 64-nm line-and-space pattern. The defect detection sensitivity in this case was higher than that of a Ru-capped ML sample and has a potential to meet the requirement for beyond 16-nm node generation from the standpoint of patterned mask inspection using the PEM technique. These results indicate that the B4C capping layer, besides its good durability, has an advantage for high sensitivity of patterned mask inspection. The optimal condition of the incident beam energy was found to be 500 and 1,000 eV for the samples of B4C-capped ML and B4C-buffered Ru-capped ML, respectively. The sensitivity of defect detection was strongly affected by the difference of secondary electron emission coefficients (SEECs) between the absorber layer and capping layer. However, the small incident beam energy was found to be preferable when the SEEC difference was relatively high.
CC BY: © The Authors. Published by SPIE under a Creative Commons Attribution 4.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.
Susumu Iida, Ryoichi Hirano, Tsuyoshi Amano, and Hidehiro Watanabe "Impact of B4C capping layer for extreme ultraviolet mask on the sensitivity of patterned mask inspection using a projection electron microscope," Journal of Micro/Nanolithography, MEMS, and MOEMS 13(4), 043015 (11 December 2014). https://doi.org/10.1117/1.JMM.13.4.043015
Published: 11 December 2014
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CITATIONS
Cited by 8 scholarly publications.
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KEYWORDS
Inspection

Extreme ultraviolet

Defect detection

Ruthenium

Electron microscopes

Monte Carlo methods

Signal detection

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