Dr. Soon-Cheon Seo Profile

Dr. Soon-Cheon Seo

Sr. Engineer at IBM Thomas J Watson Research Ctr

SPIE Involvement:

Author

Publications (4)

Proceedings Article | 24 March 2020 Presentation

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

Iqbal Saraf, Shyam Sridhar, Christopher Catano, Sergey Voronin, 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

Read Abstract +

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

Read Abstract +

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 | 8 November 2005 Paper

Inspection and planarization of programmed pit masks for EUV lithography

S.-C. Seo, S.-I. Han, Y. Ikuta, P. Kearney, A. Ma, D. Krick

Proceedings Volume 5992, 59923Y (2005) https://doi.org/10.1117/12.633447

KEYWORDS: Multilayers, Inspection, Quartz, Atomic force microscopy, Photomasks, Scanning electron microscopy, Extreme ultraviolet lithography, Neodymium, Coating, Silicon

Read Abstract +

Proceedings Article | 10 May 2005 Paper

SEMATECH’s world class EUV mask blank metrology toolset

Soon-Cheon Seo, Jan Cavelaars, John Maltabes, Sang-In Han, Patrick Kearney, Dave Krick

Proceedings Volume 5752, (2005) https://doi.org/10.1117/12.598860

KEYWORDS: Extreme ultraviolet, Photomasks, Metrology, Reflectivity, Inspection, Multilayers, Defect inspection, Particles, Spatial frequencies, Extreme ultraviolet lithography

Read Abstract +

View contact details

UPDATE YOUR PROFILE

Is this your profile? Update it now.

Sign into your SPIE.org account

Don’t have a profile and want one?

Create an account on SPIE.org

Keywords/Phrases

Search In:

Publication Years