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26 August 2024 NIL solutions using computational lithography for semiconductor device manufacturing
Yukio Nakano, Kenji Yamamoto, Sentaro Aihara, Hiromu Kijima, Satoru Jimbo, Humberto Evans, Shingo Ishida, Masayoshi Fujimoto, Shota Takami, Yuichiro Oguchi, Junichi Seki, Toshiya Asano, Osamu Morimoto
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Proceedings Volume 13177, Photomask Japan 2024: XXX Symposium on Photomask and Next-Generation Lithography Mask Technology; 1317709 (2024) https://doi.org/10.1117/12.3032446
Event: Photomask Japan 2024, 2024, Yokohama, Japan
Abstract
Computational technologies are still in the course of development for nanoimprint lithography (NIL). Only a few simulators are applicable to the nanoimprint process, and these simulators are desired by device manufacturers as part of their daily toolbox. The most challenging issue in NIL process simulation is the scale difference of each component of the system. The template pattern depth and the residual resist film thickness are generally of the order of a few tens of nanometers, while the process needs to work over the entire shot size, which is typically of the order of 10 mm square. This amounts to a scale difference of the order of 106. Therefore, in order to calculate the nanoimprint process with conventional fluid structure interaction (FSI) simulators, an enormous number of meshes is required, which results in computation times that are unacceptable. To support all lithographic systems, Canon has introduced “Lithography Plus”, a software solution capable of anomaly detection, automatic recovery, trouble flow prediction and remote support. The software is now under development specifically for NIL. Because NIL is a rheological process, to software must address a completely new work flow. In this paper, we introduce the methods used to create drop patterns and refinements to the NIL process simulator which can be applied to predict resist filling and, in the future, be used to make corrections to the drop pattern virtually, thereby eliminating time consuming on-tool verification. Finally, we discuss the development of virtual metrology software that incorporates artificial intelligence to provide fast feedback on key tool outputs such as overlay.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
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Yukio Nakano, Kenji Yamamoto, Sentaro Aihara, Hiromu Kijima, Satoru Jimbo, Humberto Evans, Shingo Ishida, Masayoshi Fujimoto, Shota Takami, Yuichiro Oguchi, Junichi Seki, Toshiya Asano, and Osamu Morimoto "NIL solutions using computational lithography for semiconductor device manufacturing", Proc. SPIE 13177, Photomask Japan 2024: XXX Symposium on Photomask and Next-Generation Lithography Mask Technology, 1317709 (26 August 2024); https://doi.org/10.1117/12.3032446
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KEYWORDS
Nanoimprint lithography
Artificial intelligence
Lithography
Metrology
Overlay metrology
Data modeling
Optical lithography
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