Dr. Robinhsinkuo Chao Profile

Dr. Robinhsinkuo Chao

Metrology Development Engineer at IBM Corp

SPIE Involvement:

Author

Publications (11)

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 | 19 March 2018 Presentation

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

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

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

Read Abstract +

Proceedings Article | 12 April 2017 Presentation + Paper

Electrical test prediction using hybrid metrology and machine learning

Mary Breton, Robin Chao, Gangadhara Raja Muthinti, Abraham de la Peña, Jacques Simon, Aron Cepler, Matthew Sendelbach, John Gaudiello, Susan Emans, Michael Shifrin, Yoav Etzioni, Ronen Urenski, Wei Ti Lee

Proceedings Volume 10145, 1014504 (2017) https://doi.org/10.1117/12.2261091

KEYWORDS: Copper, Metrology, Resistance, Machine learning, Metals, Back end of line, Semiconducting wafers, Scatterometry, X-rays, Process control

Read Abstract +

Electrical test measurement in the back-end of line (BEOL) is crucial for wafer and die sorting as well as comparing intended process splits. Any in-line, nondestructive technique in the process flow to accurately predict these measurements can significantly improve mean-time-to-detect (MTTD) of defects and improve cycle times for yield and process learning. Measuring after BEOL metallization is commonly done for process control and learning, particularly with scatterometry (also called OCD (Optical Critical Dimension)), which can solve for multiple profile parameters such as metal line height or sidewall angle and does so within patterned regions. This gives scatterometry an advantage over inline microscopy-based techniques, which provide top-down information, since such techniques can be insensitive to sidewall variations hidden under the metal fill of the trench. But when faced with correlation to electrical test measurements that are specific to the BEOL processing, both techniques face the additional challenge of sampling. Microscopy-based techniques are sampling-limited by their small probe size, while scatterometry is traditionally limited (for microprocessors) to scribe targets that mimic device ground rules but are not necessarily designed to be electrically testable. A solution to this sampling challenge lies in a fast reference-based machine learning capability that allows for OCD measurement directly of the electrically-testable structures, even when they are not OCD-compatible. By incorporating such direct OCD measurements, correlation to, and therefore prediction of, resistance of BEOL electrical test structures is significantly improved. Improvements in prediction capability for multiple types of in-die electrically-testable device structures is demonstrated. To further improve the quality of the prediction of the electrical resistance measurements, hybrid metrology using the OCD measurements as well as X-ray metrology (XRF) is used. Hybrid metrology is the practice of combining information from multiple sources in order to enable or improve the measurement of one or more critical parameters. Here, the XRF measurements are used to detect subtle changes in barrier layer composition and thickness that can have second-order effects on the electrical resistance of the test structures. By accounting for such effects with the aid of the X-ray-based measurements, further improvement in the OCD correlation to electrical test measurements is achieved. Using both types of solution incorporation of fast reference-based machine learning on nonOCD-compatible test structures, and hybrid metrology combining OCD with XRF technology improvement in BEOL cycle time learning could be accomplished through improved prediction capability.

Proceedings Article | 31 March 2017 Paper

Materials characterization for process integration of multi-channel gate all around (GAA) devices

Gangadhara Raja Muthinti, Nicolas Loubet, Robin Chao, Abraham de la Peña, Juntao Li, Michael Guillorn, Tenko Yamashita, Sivananda Kanakasabapathy, John Gaudiello, Aron Cepler, Matthew Sendelbach, Susan Emans, Shay Wolfling, Avron Ger, Daniel Kandel, Roy Koret, Wei Ti Lee, Peter Gin, Kevin Matney, Matthew Wormington

Proceedings Volume 10145, 101451U (2017) https://doi.org/10.1117/12.2261377

KEYWORDS: Germanium, Silicon, Diffractive optical elements, Gallium arsenide, Semiconducting wafers, Etching, Solids, X-ray diffraction, X-ray fluorescence spectroscopy, Materials processing

Read Abstract +

Proceedings Article | 30 March 2016 Paper

Advanced in-line metrology strategy for self-aligned quadruple patterning

Robin Chao, Mary Breton, Benoit L'herron, Brock Mendoza, Raja Muthinti, Florence Nelson, Abraham De La Pena, Fee li Le, Eric Miller, Stuart Sieg, James Demarest, Peter Gin, Matthew Wormington, Aron Cepler, Cornel Bozdog, Matthew Sendelbach, Shay Wolfling, Tom Cardinal, Sivananda Kanakasabapathy, John Gaudiello, Nelson Felix

Proceedings Volume 9778, 977813 (2016) https://doi.org/10.1117/12.2220601

KEYWORDS: Critical dimension metrology, Metrology, Transmission electron microscopy, Optical lithography, Semiconducting wafers, Etching, Process control, Scatterometry, Reactive ion etching, Diffractive optical elements

Read Abstract +

Self-Aligned Quadruple Patterning (SAQP) is a promising technique extending the 193-nm lithography to manufacture structures that are 20nm half pitch or smaller. This process adopts multiple sidewall spacer image transfers to split a rather relaxed design into a quarter of its original pitch. Due to the number of multiple process steps required for the pitch splitting in SAQP, the process error propagates through each deposition and etch, and accumulates at the final step into structure variations, such as pitch walk and poor critical dimension uniformity (CDU). They can further affect the downstream processes and lower the yield. The impact of this error propagation becomes significant for advanced technology nodes when the process specifications of device design CD requirements are at nanometer scale. Therefore, semiconductor manufacturing demands strict in-line process control to ensure a high process yield and improved performance, which must rely on precise measurements to enable corrective actions and quick decision making for process development. This work aims to provide a comprehensive metrology solution for SAQP.

During SAQP process development, the challenges in conventional in-line metrology techniques start to surface. For instance, critical-dimension scanning electron microscopy (CDSEM) is commonly the first choice for CD and pitch variation control. However, it is found that the high aspect ratio at mandrel level processes and the trench variations after etch prevent the tool from extracting the true bottom edges of the structure in order to report the position shift. On the other hand, while the complex shape and variations can be captured with scatterometry, or optical CD (OCD), the asymmetric features, such as pitch walk, show low sensitivity with strong correlations in scatterometry. X-ray diffraction (XRD) is known to provide useful direct measurements of the pitch walk in crystalline arrays, yet the data analysis is influenced by the incoming geometry and must be used carefully.

A successful implementation of SAQP process control for yield improvement requires the metrology issues to be addressed. By optimizing the measurement parameters and beam configurations, CDSEM measurements distinguish each of the spaces corresponding to the upstream mandrel processes and report their CDs separately to feed back to the process team for the next development cycle. We also utilize the unique capability in scatterometry to measure the structure details in-line and implement a “predictive” process control, which shows a good correlation between the “predictive” measurement and the cross-sections from our design of experiments (DOE). The ability to measure the pitch walk in scatterometry was also demonstrated. This work also explored the frontier of in-line XRD capability by enabling an automatic RSM fitting on tool to output pitch walk values. With these advances in metrology development, we are able to demonstrate the impacts of in-line monitoring in the SAQP process, to shorten the patterning development learning cycle to improve the yield.

Proceedings Article | 29 March 2016 Paper

Advanced in-line optical metrology of sub-10nm structures for gate all around devices (GAA)

Raja Muthinti, Nicolas Loubet, Robin Chao, John Ott, Michael Guillorn, Nelson Felix, John Gaudiello, Parker Lund, Aron Cepler, Matthew Sendelbach, Oded Cohen, Shay Wolfling, Cornel Bozdog, Mark Klare

Proceedings Volume 9778, 977810 (2016) https://doi.org/10.1117/12.2220379

KEYWORDS: Nanowires, Silicon, Germanium, Metrology, Gallium arsenide, Optical lithography, Scatterometry, Metals, Semiconducting wafers, Multilayers

Read Abstract +

Proceedings Article | 19 March 2015 Paper

Scatterometry-based defect detection for DSA in-line process control

Robin Chao, Chi-Chun Liu, Cornel Bozdog, Aron Cepler, Matthew Sendelbach, Oded Cohen, Shay Wolfling, Todd Bailey, Nelson Felix

Proceedings Volume 9424, 942419 (2015) https://doi.org/10.1117/12.2087093

KEYWORDS: Defect detection, Scatterometry, Semiconducting wafers, Scanning electron microscopy, Process control, Inspection, Metrology, Optical lithography, Image analysis, Directed self assembly

Read Abstract +

Proceedings Article | 19 March 2015 Paper

Hybridization of XRF/XPS and scatterometry for Cu CMP process control

Benoit L'Herron, Robin Chao, Kwanghoon Kim, Wei Ti Lee, Koichi Motoyama, Bartlet Deprospo, Theodorus Standaert, John Gaudiello, Cindy Goldberg

Proceedings Volume 9424, 94241M (2015) https://doi.org/10.1117/12.2086155

KEYWORDS: Copper, Scatterometry, Metrology, Chemical mechanical planarization, Critical dimension metrology, X-rays, Semiconducting wafers, Process control, X-ray fluorescence spectroscopy, Transmission electron microscopy

Read Abstract +

SPIE Journal Paper | 29 October 2014

Multitechnique metrology methods for evaluating pitch walking in 14 nm and beyond FinFETs

Robin Chao, Kriti Kohli, Yunlin Zhang, Anita Madan, Gangadhara Raja Muthinti, Augustin Hong, David Conklin, Judson Holt, Todd Bailey

JM3, Vol. 13, Issue 04, 041411, (October 2014) https://doi.org/10.1117/12.10.1117/1.JMM.13.4.041411

KEYWORDS: Metrology, Scatterometry, Silicon, Diffraction, X-rays, Data modeling, Semiconducting wafers, X-ray diffraction, 3D modeling

Read Abstract +

Proceedings Article | 2 April 2014 Paper

Novel in-line metrology methods for Fin pitch walking monitoring in 14nm node and beyond

Robin Chao, Kriti Kohli, Yunlin Zhang, Anita Madan, G. Raja Muthinti, Augustin Hong, David Conklin, Judson Holt, Todd Bailey

Proceedings Volume 9050, 90501E (2014) https://doi.org/10.1117/12.2057402

KEYWORDS: Scatterometry, Silicon, Metrology, Data modeling, X-ray diffraction, Diffraction, X-rays, Semiconducting wafers, Diffraction gratings, Critical dimension metrology

Read Abstract +

Proceedings Article | 28 March 2014 Paper

Towards electrical testable SOI devices using Directed Self-Assembly for fin formation

Chi-Chun Liu, Cristina Estrada-Raygoza, Hong He, Michael Cicoria, Vinayak Rastogi, Nihar Mohanty, Hsinyu Tsai, Anthony Schepis, Kafai Lai, Robin Chao, Derrick Liu, Michael Guillorn, Jason Cantone, Sylvie Mignot, Ryoung-Han Kim, Joy Cheng, Melia Tjio, Akiteru Ko, David Hetzer, Mark Somervell, Matthew Colburn

Proceedings Volume 9049, 904909 (2014) https://doi.org/10.1117/12.2046462

KEYWORDS: Etching, Silicon, Polymethylmethacrylate, Semiconducting wafers, Scatterometry, Image processing, 3D modeling, Line edge roughness, Defect detection, Directed self assembly

Read Abstract +

Showing 5 of 11 publications

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