Open Access
11 July 2018 Stochastics in extreme ultraviolet lithography: investigating the role of microscopic resist properties for metal-oxide-based resists
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Abstract
Due to the high energy of extreme ultraviolet (EUV) photons, stochastic effects become more important at a constant dose when compared with deep ultraviolet exposures. Photoresists are used to transfer information from the aerial image into physical features and play an important role in the transduction of these stochastic effects. Recently, metal-oxide-based nonchemically amplified resists (non-CARs) have attracted a lot of attention. We study how the properties of these non-CARs impact the local critical dimension uniformity (LCDU) of a regular contact hole array printed with EUV lithography using Monte Carlo simulations and an analytical model. We benchmark both the simulations and the analytical model to experimental data, and then use the flexibility of both methods to systematically investigate the role of microscopic resist properties in the final LCDU. It is found that metal-oxide clusters should be <1  nm in diameter, otherwise granularity will have a significant contribution to LCDU. When varying resist properties to change the resist dose-to-size, we find that the LCDU scaling with dose depends on how the resist is modified. After performing an overall sensitivity analysis to identify the optimum scaling of LCDU with dose, we find a scaling of dose  −  0.5 when the development threshold is modified, and a scaling of dose  −  0.33 when core radius or the quantum efficiency is changed.
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.
Ruben Maas, M.-Claire van Lare, Gijsbert Rispens, and Sander F. Wuister "Stochastics in extreme ultraviolet lithography: investigating the role of microscopic resist properties for metal-oxide-based resists," Journal of Micro/Nanolithography, MEMS, and MOEMS 17(4), 041003 (11 July 2018). https://doi.org/10.1117/1.JMM.17.4.041003
Received: 26 January 2018; Accepted: 11 June 2018; Published: 11 July 2018
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Cited by 17 scholarly publications.
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KEYWORDS
Monte Carlo methods

Quantum efficiency

Photons

Stochastic processes

Data modeling

Extreme ultraviolet lithography

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