EUV chemically amplified resist component distribution and efficiency for stochastic defect control

Dario L. Goldfarb; Olivia Wang; Conor R. Thomas; Heather Polgrean; Margaret C. Lawson; Alexander E. Hess; Anuja De Silva

doi:10.1117/12.2551967

23 March 2020 EUV chemically amplified resist component distribution and efficiency for stochastic defect control

Dario L. Goldfarb, Olivia Wang, Conor R. Thomas, Heather Polgrean, Margaret C. Lawson, Alexander E. Hess, Anuja De Silva

Author Affiliations +

Proceedings Volume 11326, Advances in Patterning Materials and Processes XXXVII; 1132609 (2020) https://doi.org/10.1117/12.2551967
Event: SPIE Advanced Lithography, 2020, San Jose, California, United States

Abstract

In this work, we connected the analytical determination of the EUV Dill C parameter for different photodecomposable base quencher (PDB) architectures using a standard addition method, the influence of the underlying hardmask on postdevelop EUV resist residue formation, and the vertical PAG and PDB concentration profile throughout the depth of the film determined by GCIB-TOF-SIMS for a model EUV resist system. The collected experimental data was used to feed a resist patterning simulation engine, in order to understand the additive effect of component distribution and efficiency on EUV stochastics and its potential impact on defect control. Our results unveiled a link between PDB quantum yield and nanoscopic material distribution uniformity. In parallel, a differentiating behavior was observed among inorganic underlayers: metal oxide hardmasks (HMs) invariably induced more resist residue than non-metallic HMs. Last, a specific example of joint PAG and PDB concentration depletion at the resist-substrate interface was related to a potential increase in microbridge defectivity as a result of poor stochastic counts.

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Dario L. Goldfarb, Olivia Wang, Conor R. Thomas, Heather Polgrean, Margaret C. Lawson, Alexander E. Hess, and Anuja De Silva "EUV chemically amplified resist component distribution and efficiency for stochastic defect control", Proc. SPIE 11326, Advances in Patterning Materials and Processes XXXVII, 1132609 (23 March 2020); https://doi.org/10.1117/12.2551967

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KEYWORDS

Extreme ultraviolet

Polymers

Extreme ultraviolet lithography

Ions

Optical lithography

Stochastic processes

Data modeling

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