Data driven CDSEM fleet matching in sub-Å era

Mor Baram; Ran Alkoken; Noam Teomim; Noam Tal; Bobin Mathew; Ilan Ben Harush; Eyal Angel; Shmuel Mizrachi; Liraz Gershtein; David Ulliel; Gadi Oron; Anna Levant

doi:10.1117/12.3010756

9 April 2024 Data driven CDSEM fleet matching in sub-Å era

Mor Baram, Ran Alkoken, Noam Teomim, Noam Tal, Bobin Mathew, Ilan Ben Harush, Eyal Angel, Shmuel Mizrachi, Liraz Gershtein, David Ulliel, Gadi Oron, Anna Levant

Author Affiliations +

Proceedings Volume 12955, Metrology, Inspection, and Process Control XXXVIII; 1295519 (2024) https://doi.org/10.1117/12.3010756
Event: SPIE Advanced Lithography + Patterning, 2024, San Jose, California, United States

Abstract

Technology nodes shrink leads to a very tight process control window. As an inline metrology tool, CD-SEM (Critical Dimension Scanning Electron Microscope) matching error for the entire fleet should be less than 10% of the process control window. The tight matching is also obligated to be coupled with HVM (High-Volume Manufacturing) requirements of high tool availability and fast recipe creation. To meet these challenging restrictions, iterative improvements on existing methodologies are no longer sufficient and a complimentary approach needs to be adopted. The operation of any fab tool, and specifically CD-SEM holds an enormous amount of information. The tool’s various modules parameters such as: currents and voltages and environmental status (temperature, noise etc.), the working point and beam parameters: scan rate, beam current, pixel size, landing energy, beam shape and size, etc., and the specific wafer characteristics: materials, pattern, charging effects etc. Encapsulating all these together contributes to the final matching error sensitivity. As we are moving towards a data-driven era, this information can be utilized to better predict, correct, and improve matching (i.e., by neural network, machine learning etc.). Furthermore, today’s matching metrics, which are based on 1-D CD measurements from SEM images (a convolution of the final SEM image and algorithm), are indirect and are not sufficient for understanding the whole story. In this paper, we propose a complementary approach that combines iterative improvements with data-driven methodologies that can enable a matching level suitable for EUV requirements in the Å era.

Conference Presentation

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Mor Baram, Ran Alkoken, Noam Teomim, Noam Tal, Bobin Mathew, Ilan Ben Harush, Eyal Angel, Shmuel Mizrachi, Liraz Gershtein, David Ulliel, Gadi Oron, and Anna Levant "Data driven CDSEM fleet matching in sub-Å era", Proc. SPIE 12955, Metrology, Inspection, and Process Control XXXVIII, 1295519 (9 April 2024); https://doi.org/10.1117/12.3010756

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