Feature identification for parameter extraction and defect detection using machine learning

Y. Guo; H. Pahlavani; A. Khachaturiants; K. Elsayed; J. van de Laar; E. Simons; N. Saikumar; H. Sadeghian

doi:10.1117/12.3011237

9 April 2024 Feature identification for parameter extraction and defect detection using machine learning

Y. Guo, H. Pahlavani, A. Khachaturiants, K. Elsayed, J. van de Laar, E. Simons, N. Saikumar, H. Sadeghian

Proceedings Volume 12955, Metrology, Inspection, and Process Control XXXVIII; 129553I (2024) https://doi.org/10.1117/12.3011237
Event: SPIE Advanced Lithography + Patterning, 2024, San Jose, California, United States

Conference Poster

Abstract

Process control of advanced semiconductor nodes is not only pushing the limits of metrology equipment requirements in terms of resolution and throughput but also in terms of the richness of data to be extracted to enable engineers to finetune the process steps for increased yield. The move towards 3D structures requires extraction of critical dimension parameters from structures which can vary largely from layer to layer. For in-line process control, the necessary automation forces the development of layer and equipment-specific dedicated image processing algorithms. Similarly, with the increase in stochastic defects in the EUV era, detection of defects at the nm scale requires the identification of features captured in low resolution to meet the throughput requirements of HVM fabs, which can again lead to custom algorithm development. With the emergence of ML-based image processing methods, this process of algorithm development for both cases can be accelerated. In this work, we provide the general framework under which the images obtained from high-speed scanning probe microscopy-based systems can be used to train a network for either feature detection for parameter extraction or defect identification.

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

Citation Download Citation

Y. Guo, H. Pahlavani, A. Khachaturiants, K. Elsayed, J. van de Laar, E. Simons, N. Saikumar, and H. Sadeghian "Feature identification for parameter extraction and defect detection using machine learning", Proc. SPIE 12955, Metrology, Inspection, and Process Control XXXVIII, 129553I (9 April 2024); https://doi.org/10.1117/12.3011237

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