Dr. Dexin Kong Profile

Dr. Dexin Kong

Metrology Engineer at Meta

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Publications (6)

Proceedings Article | 4 May 2020 Presentation + Paper

Measuring local CD uniformity in EUV vias with scatterometry and machine learning

Aron Cepler, Dexin Kong, Daniel Schmidt, Jennifer Church, Chi-Chun Liu, Mary Breton, Cody Murray, Luciana Meli, John Sporre, Nelson Felix, Ishtiaq Ahsan, Marjorie Cheng, Roy Koret, Igor Turovets, Eric Miller

Proceedings Volume 11325, 113251I (2020) https://doi.org/10.1117/12.2551498

KEYWORDS: Scatterometry, Machine learning, Semiconducting wafers, Critical dimension metrology, Etching, Photoresist materials, Lithography, Metrology, Scatter measurement, Data modeling

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Proceedings Article | 2 April 2020 Presentation + Paper

AFM characterization for Gate-All-Around (GAA) devices

Mary Breton, Jennifer Fullam, Dexin Kong, Daniel Schmidt, Andrew Greene, David Fey, Sean Hand, Julien Frougier, Jason Osborne, Weijie Wang

Proceedings Volume 11325, 1132514 (2020) https://doi.org/10.1117/12.2551931

KEYWORDS: Atomic force microscopy, Metrology, Transmission electron microscopy, Gallium arsenide, 3D metrology, Measurement devices, Scanning electron microscopy, Semiconducting wafers, Visualization, X-ray optics

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Proceedings Article | 24 March 2020 Presentation

Challenges in the patterning of RRAM devices for analog computing applications (Conference Presentation)

Iqbal Saraf, Shyam Sridhar, Sergey Voronin, Christopher Catano, Dexin Kong, Soon-Cheon Seo, Youngseok Kim, Takashi Ando, Nicole Saulnier, Vijay Narayanan

Proceedings Volume 11329, 113290N (2020) https://doi.org/10.1117/12.2552035

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Proceedings Article | 2 July 2019 Presentation + Paper

Machine learning and hybrid metrology using scatterometry and LE-XRF to detect voids in copper lines

Shay Wolfing, Avron Ger, Kavita Shah, Dexin Kong, Koichi Motoyama, Abraham Arceo de la peña, Huai Huang, Brock Mendoza, Mary Breton, Gangadhara Raja Muthinti, Hosadurga Shobha, John Gaudiello, Aron Cepler, Matthew Sendelbach, Susan Emans, Juntao Li, James Demarest, Gauri Karve, Paul Isbester, Liying Jiang

Proceedings Volume 10959, 109590A (2019) https://doi.org/10.1117/12.2515257

KEYWORDS: Metrology, Machine learning, Scatterometry, Copper

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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.

Proceedings Article | 19 March 2018 Presentation

Novel hybrid metrology for process integration of gate all around (GAA) devices (Conference Presentation)

Gangadhara Muthinti, Dexin Kong, Susan Ng-Emans, Matthew Sendelbach, Nicolas Loubet, Robinhsinkuo Chao, Abraham Arceo De La Pena, Juntao Li, Brock Mendoza, Veeraraghavan Basker, Tenko Yamashita, John Gaudiello, Aron Cepler, Wei Ti Lee, Gilad Barak

Proceedings Volume 10585, 105850Z (2018) https://doi.org/10.1117/12.2297500

KEYWORDS: Metrology, Gallium arsenide, Etching, Silicon, Measurement devices, Geometrical optics, X-ray fluorescence spectroscopy, Semiconductors, Nanolithography

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Showing 5 of 6 publications

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