Multi-branch neural network for hybrid metrology improvement

P. Digraci; M. Besacier; P. Gergaud; G. Rademaker; J. Reche

doi:10.1117/12.2612798

26 May 2022 Multi-branch neural network for hybrid metrology improvement

P. Digraci, M. Besacier, P. Gergaud, G. Rademaker, J. Reche

Proceedings Volume 12053, Metrology, Inspection, and Process Control XXXVI; 120530W (2022) https://doi.org/10.1117/12.2612798
Event: SPIE Advanced Lithography + Patterning, 2022, San Jose, California, United States

Abstract

In the domain of advanced patterning, and especially at lithography steps achieve very small sizes becomes more and more crucial. This induces measurement challenges and thus requiring the development of new, precise and robust metrology techniques. To overcome the limited constraints of different techniques, one of the most promising approaches is hybrid metrology. It consists in gathering several metrology techniques to measure all the geometrical parameters which are processed them by an algorithm (mainly machine learning algorithm). This work stands out by using for deep learning a multi-branch neural network to increase the precision of predicts. With a particular attention made to the dataset generation and specific settings for each branch, we developed the potential of this approach which increase the precision of predicts.

Conference Presentation

Citation Download Citation

P. Digraci, M. Besacier, P. Gergaud, G. Rademaker, and J. Reche "Multi-branch neural network for hybrid metrology improvement", Proc. SPIE 12053, Metrology, Inspection, and Process Control XXXVI, 120530W (26 May 2022); https://doi.org/10.1117/12.2612798

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