Paper
18 March 2016 Energy deposition and charging in EUV lithography: Monte Carlo studies
Liam Wiseheart, Amrit Narasimhan, Steven Grzeskowiak, Mark Neisser, Leonidas E. Ocola, Greg Denbeaux, Robert L. Brainard
Author Affiliations +
Abstract
EUV photons expose photoresists by complex interactions including photoionization to create primary electrons (~80 eV), and subsequent ionization steps that create secondary electrons (10-60 eV). The mechanisms by which these electrons interact with resist components are key to optimizing the performance of EUV resists and EUV lithography as a whole. As these photoelectrons and secondary electrons are created, they deposit their energy within the resist, creating ionized atoms along the way. Because many photo- and secondary electrons can escape the resist through the surface, resists can become charged. Charging and energy deposition profiles within the resist may play a role in the sensitivity and line-edge roughness of EUV resists. In this paper, we present computational analysis of charging-influenced electron behavior in photoresists using LESiS (Low energy Electron Scattering in Solids), a software developed to understand and model electron-matter interactions. We discuss the implementation of charge and tracking and the model used to influence electron behavior. We also present the potential effects of charging on EUV and electron beam lithography by investigating secondary electron blur in charging and non-charging models.
© (2016) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Liam Wiseheart, Amrit Narasimhan, Steven Grzeskowiak, Mark Neisser, Leonidas E. Ocola, Greg Denbeaux, and Robert L. Brainard "Energy deposition and charging in EUV lithography: Monte Carlo studies", Proc. SPIE 9776, Extreme Ultraviolet (EUV) Lithography VII, 97762O (18 March 2016); https://doi.org/10.1117/12.2219849
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KEYWORDS
Electrons

Extreme ultraviolet lithography

Photons

Extreme ultraviolet

Ionization

Copper

Monte Carlo methods

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