Artificial intelligence measurement in imaging-based overlay metrology for performance boost

Sveta Grechin; Shlomit Katz; Kei Maeda; Tsumugi Hirasawa; Hisashi Otsubo; Seigo Aoki; Atsushi Takahashi; Atsushi Miyafuji; Ofer Manos; Yu Yang; Tal Levinson; Ran Trifon; Dor Yehuda; Hamode Hagaze; Yuval Lamhot; Yair Vardi; Yoav Grauer; Avner Safrani; Cindy Kato; Iwata Yasuhisa; Imura Koichi; Ito Kosuke; Hayashi Masanobu; Eunjoong An; Nana Lan; Simon Ahn; Nadav Gutman

doi:10.1117/12.3010281

9 April 2024 Artificial intelligence measurement in imaging-based overlay metrology for performance boost

Sveta Grechin, Shlomit Katz, Kei Maeda, Tsumugi Hirasawa, Hisashi Otsubo, Seigo Aoki, Atsushi Takahashi, Atsushi Miyafuji, Ofer Manos, Yu Yang, Tal Levinson, Ran Trifon, Dor Yehuda, Hamode Hagaze, Yuval Lamhot, Yair Vardi, Yoav Grauer, Avner Safrani, Cindy Kato, Iwata Yasuhisa, Imura Koichi, Ito Kosuke, Hayashi Masanobu, Eunjoong An, Nana Lan, Simon Ahn, Nadav Gutman

Author Affiliations +

Proceedings Volume 12955, Metrology, Inspection, and Process Control XXXVIII; 129552P (2024) https://doi.org/10.1117/12.3010281
Event: SPIE Advanced Lithography + Patterning, 2024, San Jose, California, United States

Conference Poster

Abstract

Advanced semiconductor devices target sub-2nm on-product overlay (OPO) and manufacturers utilize dense overlay (OVL) sampling and non-zero offset (NZO) control to enable such strict performance. Accurate optical OVL metrology systems with fast move-and-measurement (MAM) utilized at the after-develop inspection (ADI) step are required to support this OPO trend. This work presents an innovative Artificial Intelligence (AI) based, ultra-high-speed, overlay target focusing and centering approach on imaging-based overlay (IBO) measurements in the ADI step. The algorithm uses pre-trained image features and a deep learning model. The algorithm allows the measurement of every site across the wafer in its best centering and contrast focus position and thus overcomes intra-wafer process variations and enhanced measurement accuracy. The data will include results from multi-lot advanced DRAM process with basic performance analysis such as total measurement uncertainty (TMU), tool-to-tool matching (TTTM) and additional key performance indicators (KPIs).

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Sveta Grechin, Shlomit Katz, Kei Maeda, Tsumugi Hirasawa, Hisashi Otsubo, Seigo Aoki, Atsushi Takahashi, Atsushi Miyafuji, Ofer Manos, Yu Yang, Tal Levinson, Ran Trifon, Dor Yehuda, Hamode Hagaze, Yuval Lamhot, Yair Vardi, Yoav Grauer, Avner Safrani, Cindy Kato, Iwata Yasuhisa, Imura Koichi, Ito Kosuke, Hayashi Masanobu, Eunjoong An, Nana Lan, Simon Ahn, and Nadav Gutman "Artificial intelligence measurement in imaging-based overlay metrology for performance boost", Proc. SPIE 12955, Metrology, Inspection, and Process Control XXXVIII, 129552P (9 April 2024); https://doi.org/10.1117/12.3010281

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