Dr. Pietro Montanini Profile

Dr. Pietro Montanini

at IBM Corp

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Author

Publications (3)

Proceedings Article | 10 April 2024 Presentation + Paper

EUV patterned gate variation reduction in next generation transistor architectures

Gopal Sankar Kenath, Martin Burkhardt, Nikhil Jain, Anna Lin, Jennifer Church, Gen Tsutsui, Stephanie Reynoso, Xuan Liu, Chris Sheraw, Pietro Montanini, Eric Miller, Indira Seshadri, Luciana Meli, Nelson Felix

Proceedings Volume 12953, 1295305 (2024) https://doi.org/10.1117/12.3012023

KEYWORDS: Line width roughness, Light sources and illumination, Nanoimprint lithography, Optical lithography, Line edge roughness, Etching, Transistors, Extreme ultraviolet, Reactive ion etching, Extreme ultraviolet lithography

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Proceedings Article | 25 March 2020 Presentation

A comparative analysis of EUV sheet and gate patterning for beyond 7nm gate all around stacked nanosheet FET’s (Conference Presentation)

Indira Seshadri, Praveen Joseph, Stuart Sieg, Tao Li, Wenyu Xu, Eric Miller, Dan Dechene, Andrew Greene, Carl Radens, Jingyun Zhang, Yann Mignot, Pietro Montanini, Mary Breton, Veeraraghavan Basker, Nelson Felix

Proceedings Volume 11328, 113280B (2020) https://doi.org/10.1117/12.2552159

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Proceedings Article | 5 September 2018 Presentation + Paper

In-line characterization of non-selective SiGe nodule defects with scatterometry enabled by machine learning

Dexin Kong, Robin Chao, Mary Breton, Chi-chun Liu, Gangadhara Raja Muthinti, Soon-cheon Seo, Nicolas Loubet, Pietro Montanini, John Gaudiello, Veeraraghavan Basker, Aron Cepler, Susan Ng-Emans, Matthew Sendelbach, Itzik Kaplan, Gilad Barak, Daniel Schmidt, Julien Frougier

Proceedings Volume 10585, 1058510 (2018) https://doi.org/10.1117/12.2297377

KEYWORDS: Scatterometry, Semiconducting wafers, Scanning electron microscopy, Machine learning, Metrology, Data modeling, Scatter measurement, Mathematical modeling, Transmission electron microscopy, Field effect transistors

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As device scaling continues, controlling defect densities on the wafer becomes essential for high volume manufacturing (HVM). One type of defect, the non-selective SiGe nodule, becomes more difficult to control during SiGe epitaxy (EPI) growth for p-type field effect transistor (pFET) source and drain. The process window for SiGe EPI growth with low nodule density becomes extremely tight due to the shrinking of contact poly pitch (CPP). Any tiny process shift or incoming structure shift could introduce a high density of nodules, which could affect device performance and yield. The current defect inspection method has a low throughput, so a fast and quantitative characterization technique is preferred for measuring and monitoring this type of defect. Scatterometry is a fast and non-destructive in-line metrology technique. In this work, novel methods were developed to accurately and comprehensively measure the SiGe nodules with scatterometry information. Top-down critical dimension scanning electron microscopy (CD-SEM) images were collected and analyzed on the same location as scatterometry measurement for calibration. Machine learning (ML) algorithms are used to analyze the correlation between the raw spectra and defect density and area fraction. The analysis showed that the defect density and area fractions can be measured separately by correlating intensity variations. In addition to the defect density and area fraction, we also investigate a novel method – model-based scatterometry hybridized with machine learning capabilities – to quantify the average height of the defects along the sidewall of the gate. Hybridizing the machine learning method with the model-based one could also eliminate the possibility of misinterpreting the defect as some structural parameters. Furthermore, cross-sectional TEM and SEM measurement are used to calibrate the model-based scatterometry results. In this work, the correlation between the SiGe nodule defects and the structural parameters of the device is also studied. The preliminary result shows that there is strong correlation between the defect density and spacer thickness. Correlations between the defect density and the structural parameters provides useful information for process engineers to optimize the EPI growth process. With the advances in the scatterometry-based defect measurement metrology, we demonstrate such fast, quantitative, and comprehensive measurement of SiGe nodule defects can be used to improve the throughput and yield.

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