Dr. Sang-il Park Profile

Dr. Sang-il Park

at Park Systems Corp

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Publications (11)

Proceedings Article | 13 March 2018 Paper

Automated AFM for small-scale and large-scale surface profiling in CMP applications

Ardavan Zandiatashbar, Byong Kim, Young-kook Yoo, Keibock Lee, Ahjin Jo, Ju Suk Lee, Sang-Joon Cho, Sang-il Park

Proceedings Volume 10585, 105852U (2018) https://doi.org/10.1117/12.2297521

KEYWORDS: Scanners, Profiling, Chemical mechanical planarization, Semiconducting wafers, Imaging systems, Enhanced vision, Metrology, Polishing, Surface roughness, Atomic force microscopy

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Proceedings Article | 8 March 2016 Paper

Studying post-etching silicon crystal defects on 300mm wafer by automatic defect review AFM

Ardavan Zandiatashbar, Patrick Taylor, Byong Kim, Young-kook Yoo, Keibock Lee, Ahjin Jo, Ju Suk Lee, Sang-Joon Cho, Sang-il Park

Proceedings Volume 9778, 97782P (2016) https://doi.org/10.1117/12.2220369

KEYWORDS: Atomic force microscopy, Scanning electron microscopy, Semiconducting wafers, Crystals, Silicon, Light scattering, Inspection, Optical alignment, Etching, Enhanced vision

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Single crystal silicon wafers are the fundamental elements of semiconductor manufacturing industry. The wafers produced by Czochralski (CZ) process are very high quality single crystalline materials with known defects that are formed during the crystal growth or modified by further processing. While defects can be unfavorable for yield for some manufactured electrical devices, a group of defects like oxide precipitates can have both positive and negative impacts on the final device. The spatial distribution of these defects may be found by scattering techniques. However, due to limitations of scattering (i.e. light wavelength), many crystal defects are either poorly classified or not detected. Therefore a high throughput and accurate characterization of their shape and dimension is essential for reviewing the defects and proper classification. While scanning electron microscopy (SEM) can provide high resolution twodimensional images, atomic force microscopy (AFM) is essential for obtaining three-dimensional information of the defects of interest (DOI) as it is known to provide the highest vertical resolution among all techniques [1]. However AFM’s low throughput, limited tip life, and laborious efforts for locating the DOI have been the limitations of this technique for defect review for 300 mm wafers. To address these limitations of AFM, automatic defect review AFM has been introduced recently [2], and is utilized in this work for studying DOI on 300 mm silicon wafer. In this work, we carefully etched a 300 mm silicon wafer with a gaseous acid in a reducing atmosphere at a temperature and for a sufficient duration to decorate and grow the crystal defects to a size capable of being detected as light scattering defects [3]. The etched defects form a shallow structure and their distribution and relative size are inspected by laser light scattering (LLS). However, several groups of defects couldn’t be properly sized by the LLS due to the very shallow depth and low light scattering. Likewise, SEM cannot be used effectively for post-inspection defect review and classification of these very shallow types of defects. To verify and obtain accurate shape and three-dimensional information of those defects, automatic defect review AFM (ADR AFM) is utilized for accurate locating and imaging of DOI. In ADR AFM, non-contact mode imaging is used for non-destructive characterization and preserving tip sharpness for data repeatability and reproducibility. Locating DOI and imaging are performed automatically with a throughput of many defects per hour. Topography images of DOI has been collected and compared with SEM images. The ADR AFM has been shown as a non-destructive metrology tool for defect review and obtaining three-dimensional topography information.

Proceedings Article | 23 October 2015 Paper

Automatic defect review for EUV photomask reticles by atomic force microscope

Ardavan Zandiatashbar, Byong Kim, Young-kook Yoo, Keibock Lee, Ahjin Jo, Ju Suk Lee, Sang-Joon Cho, Sang-il Park

Proceedings Volume 9635, 96351A (2015) https://doi.org/10.1117/12.2197382

KEYWORDS: Atomic force microscopy, Reticles, Photomasks, Enhanced vision, Extreme ultraviolet, Inspection, Scanners, Nondestructive evaluation, Atomic force microscope, Electron microscopes

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Defects on a reticle are inspected, reviewed, and repaired by different tools. They are located by automated optical inspection (AOI); however, if the characteristic size of defects is similar to that of light and electron beam wavelengths, they are often unclassified or misclassified by AOI. Atomic force microscopes (AFM) along with electron microscopes are used for investigating defects located by AOI to distinguish false defects from real defects and effectively classify them. Both AFM and electron microscopes provide high resolution images. However, electron microscopy is known to be destructive and have less accuracy in 3rd dimension measurement compared to AFM [1]. On the other hand, AFM is known to have low throughput and limited tip life in addition to requiring significant effort to finding the defects. These limitations emanate from having to perform multiple large scans to find the defect locations, to compensate for stage coordinate inaccuracies, and to correct the mismatch between the AFM and the AOI tools.

In this work we introduce automatic defect review (ADR) AFM for defect study and classification of EUV mask reticles that overcomes the aforementioned limitations of traditional AFM. This metrology solution is based on an AFM configuration with decoupled Z and XY scanners that makes it possible to collect large survey images with minimum out of plane motion. To minimize the stage errors and mismatch between the AFM and the AOI coordinates, the coordinates of fiducial markers are used for coarse alignment. In addition, fine alignment of the coordinates is performed using enhanced optical vision on marks on the reticle. The ADR AFM is used to study a series of phase defects identified by an AOI tool on a reticle. Locating the defects, imaging, and defect classification are performed using the ADR automation software and with the throughput of several defects per hour. In order to preserve tip life and data consistency, AFM imaging is performed in non-contact mode. The ADR AFM provides high throughput, high resolution, and non-destructive means for obtaining 3D information for defect review and classification. Therefore this technology can be used for in-line defect review and classification for mask repair.

Proceedings Article | 19 March 2015 Paper

High-throughput automatic defect review for 300mm blank wafers with atomic force microscope

Ardavan Zandiatashbar, Byong Kim, Young-kook Yoo, Keibock Lee, Ahjin Jo, Ju Suk Lee, Sang-Joon Cho, Sang-il Park

Proceedings Volume 9424, 94241X (2015) https://doi.org/10.1117/12.2086042

KEYWORDS: Semiconducting wafers, Atomic force microscopy, Inspection, Optical alignment, Enhanced vision, Nondestructive evaluation, Atomic force microscope, Image resolution, Metrology, Scanners

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Proceedings Article | 18 April 2013 Paper

Introduction of next-generation 3D AFM for advanced process control

J. Foucher, R. Thérèse, Y. Lee, S.-I. Park, S.-J. Cho

Proceedings Volume 8681, 868106 (2013) https://doi.org/10.1117/12.2011463

KEYWORDS: Atomic force microscopy, Process control, Metrology, Semiconductors, 3D metrology, Scatterometry, Lithography, Critical dimension metrology, Resolution enhancement technologies, Semiconducting wafers

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Proceedings Article | 10 April 2013 Paper

3D AFM method for characterization of resist effect of aerial image contrast on side wall roughness

Yong-ha Lee, Sang-Joon Cho, Sang-il Park, R. Ayothi, Y. Hishiro

Proceedings Volume 8681, 86812Y (2013) https://doi.org/10.1117/12.2013657

KEYWORDS: Line edge roughness, Photoresist materials, Atomic force microscopy, Line width roughness, Scanning electron microscopy, 3D image processing, 3D scanning, Scanners, Laser scanners, Semiconductors

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Proceedings Article | 5 April 2012 Paper

High-throughput and non-destructive sidewall roughness measurement using 3-dimensional atomic force microscopy

Yueming Hua, Cynthia Buenviaje-Coggins, Yong-ha Lee, Sang-il Park

Proceedings Volume 8324, 83240I (2012) https://doi.org/10.1117/12.918377

KEYWORDS: Atomic force microscopy, Photoresist materials, 3D image processing, 3D metrology, 3D scanning, Image resolution, Laser scanners, Metrology, Line edge roughness, Scanners

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Proceedings Article | 20 April 2011 Paper

New three-dimensional AFM for CD measurement and sidewall characterization

Yueming Hua, Cynthia Buenviaje-Coggins, Yong-ha Lee, Jung-min Lee, Kyung-deuk Ryang, Sang-il Park

Proceedings Volume 7971, 797118 (2011) https://doi.org/10.1117/12.879545

KEYWORDS: Atomic force microscopy, Scanners, Photoresist materials, Critical dimension metrology, Line edge roughness, Line width roughness, Image resolution, Scanning electron microscopy, 3D metrology, Metrology

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Proceedings Article | 3 June 2010 Paper

Advanced 3D metrology atomic force microscope with crosstalk eliminated

Yueming Hua, Cynthia Coggins, Sung Park, Sang-il Park

Proceedings Volume 7729, 77290N (2010) https://doi.org/10.1117/12.853679

KEYWORDS: Atomic force microscopy, Scanners, 3D metrology, 3D image processing, Metrology, Atomic force microscope, Laser scanners, 3D scanning, Reflectivity, LCDs

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Proceedings Article | 24 August 1999 Paper

Automatic recognition of SAR targets using directional filter banks and higher-order neural networks

Sang-Il Park, Mark Smith, Russell Mersereau

Proceedings Volume 3718, (1999) https://doi.org/10.1117/12.359942

KEYWORDS: Image filtering, Neural networks, Synthetic aperture radar, Automatic target recognition, Visualization, Target recognition, Optical filters, Databases, Information visualization, Linear filtering

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Proceedings Article | 22 March 1996 Paper

Multidimensional wavelets for target detection and recognition

Sang-Il Park, Romain Murenzi, Mark Smith

Proceedings Volume 2762, (1996) https://doi.org/10.1117/12.235991

KEYWORDS: Continuous wavelet transforms, Wavelets, Automatic target recognition, Target detection, Target recognition, Binary data, Feature extraction, Detection and tracking algorithms, Databases, Synthetic aperture radar

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Showing 5 of 11 publications

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