Ms. Susan Ng-Emans Profile

Susan Ng-Emans

at Nova Measuring Instruments Inc

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Author

Publications (6)

Proceedings Article | 2 July 2019 Presentation + Paper

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

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

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, 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

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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

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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

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Proceedings Article | 28 March 2017 Presentation + Paper

Hybrid scatterometry measurement for BEOL process control

Padraig Timoney, Alok Vaid, Byeong Cheol Kang, Haibo Liu, Paul Isbester, Marjorie Cheng, Susan Ng-Emans, Naren Yellai, Matt Sendelbach, Roy Koret, Oram Gedalia

Proceedings Volume 10145, 1014506 (2017) https://doi.org/10.1117/12.2261452

KEYWORDS: Scatterometry, Back end of line, Metrology, 3D metrology, 3D modeling, Dielectrics, Copper, Process control, Etching, Semiconducting wafers, Photomasks

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Scaling of interconnect design rules in advanced nodes has been accompanied by a reducing metrology budget for BEOL process control. Traditional inline optical metrology measurements of BEOL processes rely on 1-dimensional (1D) film pads to characterize film thickness. Such pads are designed on the assumption that solid copper blocks from previous metallization layers prevent any light from penetrating through the copper, thus simplifying the effective film stack for the 1D optical model. However, the reduction of the copper thickness in each metallization layer and CMP dishing effects within the pad, have introduced undesired noise in the measurement. To resolve this challenge and to measure structures that are more representative of product, scatterometry has been proposed as an alternative measurement. Scatterometry is a diffraction based optical measurement technique using Rigorous Coupled Wave Analysis (RCWA), where light diffracted from a periodic structure is used to characterize the profile. Scatterometry measurements on 3D structures have been shown to demonstrate strong correlation to electrical resistance parameters for BEOL Etch and CMP processes. However, there is significant modeling complexity in such 3D scatterometry models, in particlar due to complexity of front-end-of-line (FEOL) and middle-of-line (MOL) structures. The accompanying measurement noise associated with such structures can contribute significant measurement error. To address the measurement noise of the 3D structures and the impact of incoming process variation, a hybrid scatterometry technique is proposed that utilizes key information from the structure to significantly reduce the measurement uncertainty of the scatterometry measurement. Hybrid metrology combines measurements from two or more metrology techniques to enable or improve the measurement of a critical parameter. In this work, the hybrid scatterometry technique is evaluated for 7nm and 14nm node BEOL measurements of interlayer dielectric (ILD) thickness, hard mask thickness and dielectric trench etch in complex 3D structures. The data obtained from the hybrid scatterometry technique demonstrates stable measurement precision, improved within wafer and wafer to wafer range, robustness in cases where 3D scatterometry measurements incur undesired shifts in the measurements, accuracy as compared to TEM and correlation to process deposition time. Process capability indicator comparisons also demonstrate improvement as compared to conventional scatterometry measurements. The results validate the suitability of the method for monitoring of production BEOL processes.

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