Roughness power spectral density as a function of aerial image and basic process/resist parameter

Charlotte Cutler; Choong Bong Lee; James W. Thackeray; John Nelson; Jason DeSisto; Rochelle Rena; Peter Trefonas; Chris Mack

doi:10.1117/12.2515073

25 March 2019 Roughness power spectral density as a function of aerial image and basic process/resist parameter

Charlotte Cutler, Choong Bong Lee, James W. Thackeray, John Nelson, Jason DeSisto, Rochelle Rena, Peter Trefonas, Chris Mack

Author Affiliations +

Proceedings Volume 10960, Advances in Patterning Materials and Processes XXXVI; 109600I (2019) https://doi.org/10.1117/12.2515073
Event: SPIE Advanced Lithography, 2019, San Jose, California, United States

Abstract

Linewidth Roughness (LWR) remains a difficult challenge for improvement in all resist materials. In previous work we focused on the impact of key components of LWR by analyzing the Power Spectral Density (PSD) curves which can be obtained using Fractilia’s MetroLER computational software. [1] By measuring the unbiased PSD (with SEM image noise removed), accurate assessment of PSD(0) (the low-frequency limit of the PSD) and correlation length (the length scale of the transition from white to correlated noise) is possible. We showed there was an important relationship between ArF resist frequency components and LWR through lithographic process (before and after a resist trim step) as a function of resist formulation. In this paper we will study how key frequency components such as PSD(0) and correlation length change as we vary basic resist properties such as diffusion. The impact of aerial image on LWR and its frequency components will also be studied with particular attention to how correlation length affects LWR as feature size decreases. We will also look at the impact of diffusion or resist blur on PSD(0) as a function of aerial image Normalized Image Log-Slope (NILS). Understanding the relationship between PSD(0) and correlation length and how to manipulate these variables to minimize LWR for different features is crucial for more rapid LWR improvement at different nodes. [1] Charlotte Cutler, et al., “Roughness power spectral density as a function of resist parameters and its impact through process,” Proc. SPIE 10587, Optical Microlithography XXXI, 1058707 (23 March 2018).

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Charlotte Cutler, Choong Bong Lee, James W. Thackeray, John Nelson, Jason DeSisto, Rochelle Rena, Peter Trefonas, and Chris Mack "Roughness power spectral density as a function of aerial image and basic process/resist parameter", Proc. SPIE 10960, Advances in Patterning Materials and Processes XXXVI, 109600I (25 March 2019); https://doi.org/10.1117/12.2515073

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