Analysis of dissolution kinetics of poly(4-hydroxystyrene) with different molecular weight distributions in alkaline aqueous solution using machine learning

Naoki Tanaka; Kyoko Watanabe; Kyoko Matsuoka; Kazuki Azumagawa; Takahiro Kozawa; Takuya Ikeda; Yoshitaka Komuro; Daisuke Kawana

doi:10.1117/12.2584067

22 February 2021 Analysis of dissolution kinetics of poly(4-hydroxystyrene) with different molecular weight distributions in alkaline aqueous solution using machine learning

Naoki Tanaka, Kyoko Watanabe, Kyoko Matsuoka, Kazuki Azumagawa, Takahiro Kozawa, Takuya Ikeda, Yoshitaka Komuro, Daisuke Kawana

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Proceedings Volume 11612, Advances in Patterning Materials and Processes XXXVIII; 116120R (2021) https://doi.org/10.1117/12.2584067
Event: SPIE Advanced Lithography, 2021, Online Only

Conference Poster

Abstract

Photoresists have been widely used as patterning materials for electric devices such as displays and semiconductor. Understanding pattern formation mechanism is essential for the efficient development of resist materials. In this study, we investigated the dissolution kinetics of poly(4-hydroxystyrene) (PHS) with weight-average molecular weights (M_w) of 9000-30000 and molecular weight distribution (M_w/M_n) of 1.07-1.20. The dissolution kinetics of PHS films was observed in tetramethylammonium hydroxide (TMAH) aqueous developers using a quartz crystal microbalance (QCM) method. The TMAH concentration was changed from 0 to 2.38 wt%. The obtained data were analyzed using polynomial regression to clarify the effects of M_w and M_w/M_n on the dissolution kinetics of PHS films. From the results of analysis, both dissolving and swelling behavior largely depended on M_w/M_n. M_w had a little effect on the dissolving, and however, had a large effect on the swelling in dilute TMAH aqueous solution.

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Naoki Tanaka, Kyoko Watanabe, Kyoko Matsuoka, Kazuki Azumagawa, Takahiro Kozawa, Takuya Ikeda, Yoshitaka Komuro, and Daisuke Kawana "Analysis of dissolution kinetics of poly(4-hydroxystyrene) with different molecular weight distributions in alkaline aqueous solution using machine learning", Proc. SPIE 11612, Advances in Patterning Materials and Processes XXXVIII, 116120R (22 February 2021); https://doi.org/10.1117/12.2584067

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