Defect detection and classification on imec iN5 node BEoL test vehicle with MultiSEM

Jens Timo Neumann; Abhilash Srikantha; Philipp Hüthwohl; Keumsil Lee; James William B.; Thomas Korb; Eugen Foca; Tomasz Garbowski; Daniel Boecker; Sayantan Das; Sandip Halder

doi:10.1117/12.2619766

26 May 2022 Defect detection and classification on imec iN5 node BEoL test vehicle with MultiSEM

Jens Timo Neumann, Abhilash Srikantha, Philipp Hüthwohl, Keumsil Lee, James William B., Thomas Korb, Eugen Foca, Tomasz Garbowski, Daniel Boecker, Sayantan Das, Sandip Halder

Author Affiliations +

Proceedings Volume 12053, Metrology, Inspection, and Process Control XXXVI; 120530I (2022) https://doi.org/10.1117/12.2619766
Event: SPIE Advanced Lithography + Patterning, 2022, San Jose, California, United States

Abstract

We present an automated application for defect detection and classification from ZEISS MultiSEM® images, based on Machine Learning (ML) technology. We acquire MultiSEM images of a semiconductor wafer suited for process window characterization at the imec iN5 logic node and use a dedicated application to train ML models for defect detection and classification. We show the user flow for training and execution, and the resulting capture and nuisance rates. Due to straightforward parallelization, the application is designed for the large amounts of data generated rapidly by the MultiSEM.

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Jens Timo Neumann, Abhilash Srikantha, Philipp Hüthwohl, Keumsil Lee, James William B., Thomas Korb, Eugen Foca, Tomasz Garbowski, Daniel Boecker, Sayantan Das, and Sandip Halder "Defect detection and classification on imec iN5 node BEoL test vehicle with MultiSEM", Proc. SPIE 12053, Metrology, Inspection, and Process Control XXXVI, 120530I (26 May 2022); https://doi.org/10.1117/12.2619766

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