Hard failure prediction of ADI pattern using optical parameters and machine learning

Yeongchan Cho; Seongjeon Choi; Wonchan Lee; Hungbae Ahn; Sangoh Park; Dongho Kim; Seunghune Yang

doi:10.1117/12.3010633

10 April 2024 Hard failure prediction of ADI pattern using optical parameters and machine learning

Yeongchan Cho, Seongjeon Choi, Wonchan Lee, Hungbae Ahn, Sangoh Park, Dongho Kim, Seunghune Yang

Proceedings Volume 12953, Optical and EUV Nanolithography XXXVII; 129530D (2024) https://doi.org/10.1117/12.3010633
Event: SPIE Advanced Lithography + Patterning, 2024, San Jose, California, United States

Abstract

It is crucial to predict hard failure in photolithography process to determine design rules and process condition in the product development stage. Accurate prediction of hard failures through simulation have powerful effects such as shortening the product development period and improving mass production yield. Previously, parameters used to determine whether a pattern is expected to fail include NILS (Normalized Image Log-Slope), image contrast, or chemical distribution in the photoresist. However, these methods are almost infeasible because the accuracy becomes low as process condition changes and calibration process of chemical distribution is too complicated. In this paper, a novel method using optical parameters and machine learning is proposed to predict hard failures of ADI (After Development Inspection) patterns, and this methodology was evaluated in the process of applying inorganic photoresist.

Conference Presentation

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

Citation Download Citation

Yeongchan Cho, Seongjeon Choi, Wonchan Lee, Hungbae Ahn, Sangoh Park, Dongho Kim, and Seunghune Yang "Hard failure prediction of ADI pattern using optical parameters and machine learning", Proc. SPIE 12953, Optical and EUV Nanolithography XXXVII, 129530D (10 April 2024); https://doi.org/10.1117/12.3010633

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