Dr. Nathan G. Greeneltch Profile

Dr. Nathan G. Greeneltch

at Siemens EDA

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Author

Publications (4)

Proceedings Article | 10 April 2024 Poster + Paper

Fast and accurate automatic wafer defect detection and classification using machine learning based SEM image analysis

Sanghyun Choi, Qian Xie, Nathan Greeneltch, Hyung Joo Lee, Mohan Govindaraj, Srividya Jayaram, Mark Pereira, Sayani Biswas, Samir Bhamidipati, Ilhami Torunoglu

Proceedings Volume 12955, 129553M (2024) https://doi.org/10.1117/12.3012184

KEYWORDS: Education and training, Scanning electron microscopy, Data modeling, Performance modeling, Defect detection, Machine learning, Object detection, Semiconducting wafers, Image analysis, Image quality

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Proceedings Article | 10 April 2024 Poster + Paper

Advanced process control by machine learning-based virtual metrology for high product mix manufacturing

Hyung Joo Lee, Sanghyun Choi, Nathan Greeneltch, Srividya Jayaram

Proceedings Volume 12955, 1295526 (2024) https://doi.org/10.1117/12.3009796

KEYWORDS: Advanced process control, Vacuum chambers, Chemical vapor deposition, Manufacturing, Control systems, Film thickness, Data modeling, Metrology, Simulations, Machine learning

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Proceedings Article | 28 April 2023 Paper

Design-aware virtual metrology and process recipe recommendation

Nathan Greeneltch, Haizhou Yin, J. Andres Torres, Melody Tao, Steven Lubin, Srividya Jayaram, Ivan Kissiov, Martin Niehoff, Marcus Wolf, Paul Jungmann, Todd Bailey

Proceedings Volume 12495, 124951V (2023) https://doi.org/10.1117/12.2661150

KEYWORDS: Data modeling, Semiconducting wafers, Metrology, Modeling, Machine learning, Fabrication, Data processing, Semiconductors, Process modeling, Deposition processes

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Proceedings Article | 27 April 2023 Paper

AI-guided reliability diagnosis for 5,7nm automotive process

Dongin Kim, Hyung Joo Lee, Sanghyun Choi, Seungpyo Hong, Seungjae Lee, Doohwan Kwak, Srividya Jayaram, Seungwon Paek, Minho Kwon, Yeongdo Kim, Hyobe Jung, Ivan Kissiov, Melody Tao, Andres Torres, Nathan Greeneltch, Ho Lee

Proceedings Volume 12496, 124963J (2023) https://doi.org/10.1117/12.2662880

KEYWORDS: Reliability, Semiconducting wafers, Data modeling, Manufacturing, Fabrication, Semiconductors, Industry, High volume manufacturing, Model-based design, Metrology

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