Novel method for precise curved EUV mask CD characterization by adopting machine learning

Jaeseo Lee; Inhwan Noh; Youngsu Sung; Heebom Kim; Ji-hoon Kang; Seunghye Kim; Jin Choi

doi:10.1117/12.2687781

21 November 2023 Novel method for precise curved EUV mask CD characterization by adopting machine learning

Jaeseo Lee, Inhwan Noh, Youngsu Sung, Heebom Kim, Ji-hoon Kang, Seunghye Kim, Jin Choi

Author Affiliations +

Proceedings Volume 12751, Photomask Technology 2023; 127510B (2023) https://doi.org/10.1117/12.2687781
Event: SPIE Photomask Technology + EUV Lithography, 2023, Monterey, California, United States

Abstract

With the introduction of the multi-beam mask writing (MBMW) technology, efficient processing and precise patterning of curvilinear mask shapes are becoming increasingly important due to the wafer lithography advantages associated with the shapes. However, as the complexity of the curvilinear mask shapes increases, it becomes difficult to precisely characterize the curvilinear mask shapes. Barrier to this is prediction and reflection of the nature of curvilinear mask shapes. Therefore, in the industry, a novel algorithm method for accurate patterning is a major concern. In this study, we discuss the status of curvilinear mask shapes and patterning technology. By adopting machine learning, we develop a novel algorithm with considering the nature of curvilinear mask shapes. To evaluate practical use and accuracy of model, we demonstrate that the algorithm has significant value to guarantee the mask critical dimension (CD).

Conference Presentation

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Jaeseo Lee, Inhwan Noh, Youngsu Sung, Heebom Kim, Ji-hoon Kang, Seunghye Kim, and Jin Choi "Novel method for precise curved EUV mask CD characterization by adopting machine learning", Proc. SPIE 12751, Photomask Technology 2023, 127510B (21 November 2023); https://doi.org/10.1117/12.2687781

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