Dr. Paul K. Isbester Profile

Dr. Paul K. Isbester

Application Scientist

SPIE Involvement:

Author

Publications (17)

Proceedings Article | 10 April 2024 Presentation + Paper

From lab to fab: in-line SIMS for process control in semiconductor manufacturing

Stefan Schoeche, Katherine Sieg, Daniel Schmidt, Mohsen Nasseri, Shogo Mochizuki, Marinus Hopstaken, Yaguang Zhu, Li Xiang, Julia Hoffman, Daniel Lewellyn, Paul Isbester, Sarah Okada

Proceedings Volume 12955, 1295508 (2024) https://doi.org/10.1117/12.3011666

KEYWORDS: Semiconducting wafers, Silicon, Epitaxy, Germanium, Nanosheets, Metrology, Annealing, Diffusion, Transistors

Read Abstract +

Proceedings Article | 13 June 2022 Presentation

In-line XPS metrology using unsupervised machine learning in high volume manufacturing

Paul Isbester, Ganesh Subramanian, Padraig Timoney, Taher Kagalwala, Dmitry Kislitsyn, Heath Pois, Mark Klare, Daniel Kandel, Michal Yachini, Wei Ti Lee, Vanessa Zhang, Mitch Shiver, Saurabh Singh, Parker Lund

Proceedings Volume PC12053, PC120530M (2022) https://doi.org/10.1117/12.2614116

KEYWORDS: Metrology, High volume manufacturing, Machine learning, Manufacturing, X-rays, X-ray characterization, Tolerancing, Semiconductors, Semiconductor manufacturing, Semiconducting wafers

Read Abstract +

Proceedings Article | 20 March 2020 Presentation + Paper

Advanced machine learning eco-system to address HVM optical metrology requirements

Padraig Timoney, Roma Luthra, Alex Elia, Haibo Liu, Paul Isbester, Avi Levy, Michael Shifrin, Barak Bringoltz, Eylon Rabinovich, Ariel Broitman, Eitan Rothstein, Ran Yacoby, Ilya Rubinovich, YongHa Kim, Ofer Shlagman, Barak Ben-Nahum, Marina Zolkin, Igor Turovets

Proceedings Volume 11325, 113251H (2020) https://doi.org/10.1117/12.2552058

KEYWORDS: Metrology, Back end of line, Semiconducting wafers, Scatterometry, Machine learning

Read Abstract +

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

Read Abstract +

Proceedings Article | 23 May 2019 Presentation + Paper

Full structure transistor process monitoring of boron and germanium in PFET EPI using in-line XPS

Jusang Lee, Ganesh Subramanian, Manasa Medikonda, Hossam Lazkani, Judson Holt, Churamani Gaire, Paul Isbester, Mark Klare

Proceedings Volume 10959, 109590N (2019) https://doi.org/10.1117/12.2515266

KEYWORDS: Boron, Fin field effect transistors, Semiconducting wafers, Germanium, Silicon, 3D metrology, Epitaxy, Transistors, Process control, Metrology

Read Abstract +

Proceedings Article | 21 March 2018 Presentation + Paper

Implementation of machine learning for high-volume manufacturing metrology challenges

Padraig Timoney, Taher Kagalwala, Edward Reis, Houssam Lazkani, Jonathan Hurley, Haibo Liu, Charles Kang, Paul Isbester, Naren Yellai, Michael Shifrin, Yoav Etzioni

Proceedings Volume 10585, 105850X (2018) https://doi.org/10.1117/12.2300167

KEYWORDS: Machine learning, Metrology, Metals, Copper, Back end of line, Semiconducting wafers, High volume manufacturing, Process control, Critical dimension metrology, Front end of line

Read Abstract +

In recent years, the combination of device scaling, complex 3D device architecture and tightening process tolerances have strained the capabilities of optical metrology tools to meet process needs. Two main categories of approaches have been taken to address the evolving process needs. In the first category, new hardware configurations are developed to provide more spectral sensitivity. Most of this category of work will enable next generation optical metrology tools to try to maintain pace with next generation process needs. In the second category, new innovative algorithms have been pursued to increase the value of the existing measurement signal. These algorithms aim to boost sensitivity to the measurement parameter of interest, while reducing the impact of other factors that contribute to signal variability but are not influenced by the process of interest. This paper will evaluate the suitability of machine learning to address high volume manufacturing metrology requirements in both front end of line (FEOL) and back end of line (BEOL) sectors from advanced technology nodes. In the FEOL sector, initial feasibility has been demonstrated to predict the fin CD values from an inline measurement using machine learning. In this study, OCD spectra were acquired after an etch process that occurs earlier in the process flow than where the inline CD is measured. The fin hard mask etch process is known to impact the downstream inline CD value. Figure 1 shows the correlation of predicted CD vs downstream inline CD measurement obtained after the training of the machine learning algorithm. For BEOL, machine learning is shown to provide an additional source of information in prediction of electrical resistance from structures that are not compatible for direct copper height measurement. Figure 2 compares the trench height correlation to electrical resistance (Rs) and the correlation of predicted Rs to the e-test Rs value for a far back end of line (FBEOL) metallization level across 3 products. In the case of product C, it is found that the predicted Rs correlation to the e-test value is significantly improved utilizing spectra acquired at the e-test structure. This paper will explore the considerations required to enable use of machine learning derived metrology output to enable improved process monitoring and control. Further results from the FEOL and BEOL sectors will be presented, together with further discussion on future proliferation of machine learning based metrology solutions in high volume manufacturing.

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

Read Abstract +

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.

Proceedings Article | 28 March 2017 Presentation + Paper

Complex metrology on 3D structures using multi-channel OCD

Taher Kagalwala, Sridhar Mahendrakar, Alok Vaid, Paul Isbester, Aron Cepler, Charles Kang, Naren Yellai, Matthew Sendelbach, Mihael Ko, Ovadia Ilgayev, Yinon Katz, Lilach Tamam, Ilya Osherov

Proceedings Volume 10145, 101451C (2017) https://doi.org/10.1117/12.2261419

KEYWORDS: 3D metrology, Metrology, Thin films, Process control, Scatterometry, Thin film devices, Semiconducting wafers, Photomasks, Chemical species, Dielectrics, Transmission electron microscopy, Diffractive optical elements, Solids, Channel projecting optics, Etching

Read Abstract +

SPIE Journal Paper | 25 October 2016

Measuring self-aligned quadruple patterning pitch walking with scatterometry-based metrology utilizing virtual reference

Taher Kagalwala, Alok Vaid, Sridhar Mahendrakar, Michael Lenahan, Fang Fang, Paul Isbester, Michael Shifrin, Yoav Etzioni, Aron Cepler, Naren Yellai, Prasad Dasari, Cornel Bozdog

JM3, Vol. 15, Issue 04, 044004, (October 2016) https://doi.org/10.1117/12.10.1117/1.JMM.15.4.044004

KEYWORDS: Semiconducting wafers, Metrology, Optical lithography, Scatterometry, Critical dimension metrology, Finite element methods, Scatter measurement, Diffractive optical elements, Transmission electron microscopy, Reactive ion etching

Read Abstract +

Proceedings Article | 24 March 2016 Paper

Hybrid enabled thin film metrology using XPS and optical

Alok Vaid, Givantha Iddawela, Sridhar Mahendrakar, Michael Lenahan, Mainul Hossain, Padraig Timoney, Abner Bello, Cornel Bozdog, Heath Pois, Wei Ti Lee, Mark Klare, Michael Kwan, Byung Cheol Kang, Paul Isbester, Matthew Sendelbach, Naren Yellai, Prasad Dasari, Tom Larson

Proceedings Volume 9778, 97780M (2016) https://doi.org/10.1117/12.2220299

KEYWORDS: Metrology, Thin films, X-rays, X-ray optics, Semiconducting wafers, Wafer-level optics, Process control, Ellipsometry, Diffractive optical elements, Deposition processes, Optical testing, Dielectrics, Optical properties

Read Abstract +

Proceedings Article | 24 March 2016 Paper

Scatterometry-based metrology for SAQP pitch walking using virtual reference

Taher Kagalwala, Alok Vaid, Sridhar Mahendrakar, Michael Lenahan, Fang Fang, Paul Isbester, Michael Shifrin, Yoav Etzioni, Aron Cepler, Naren Yellai, Prasad Dasari, Cornel Bozdog

Proceedings Volume 9778, 97781W (2016) https://doi.org/10.1117/12.2228329

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

Read Abstract +

Proceedings Article | 19 March 2015 Paper

Comprehensive BEOL control using scatterometry and APC

Padraig Timoney, Jamie Tsai, Sudhir Baral, Laertis Economikos, Alok Vaid, Haibo Lu, Byungcheol Kang, Paul Isbester, Prasad Dasari, Roy Kort, Naren Yellai

Proceedings Volume 9424, 94241G (2015) https://doi.org/10.1117/12.2087320

KEYWORDS: Copper, Chemical mechanical planarization, Scatterometry, Metrology, Semiconducting wafers, Resistance, Reactive ion etching, Etching, 3D modeling, Back end of line

Read Abstract +

Copper interconnects have been adopted in advanced semiconductor manufacturing due to benefits of reduced RC delay, cross talk and power consumption. With each technology node, interconnects reduce in size resulting in increased line resistivity, a critical metric in determining the device performance. Reactive Ion Etching (RIE) and Copper Chemical Mechanical Polishing (Cu CMP) are two of the key back end of the line (BEOL) processes that affect the interconnect performance. Due to variations from incoming processes and the inherent variability induced by these processes, dielectric trench depth and resulting copper line height variations that can potentially result from these processes have direct impact to RC delay.

Traditional inline metrology methods used are time consuming and do not provide the needed wafer level metrics. In addition, measurement of remaining dielectric thickness on solid pads is not a good representative of the actual device structures and has been inaccurate for process due to dishing of the copper pads. Efficient control of BEOL processes requires measurement of metal line thickness and other critical profile parameters from which resistance can be extracted. In order to relate BEOL process steps and understand their interactions, it is necessary to have a directly comparable measurement methodology on a similar measurement structure.

Over the past several years, scatterometry has been proven as the only metrology method to provide the full profile information of the Cu lines. 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 details of profile. Unique algorithms, such as Holistic Metrology can be used to make the scatterometry development process faster.

In this paper, we will present how scatterometry can be used to measure copper line height on 3D structures and how feed forward from RIE can be applied for control of Cu CMP process for 20nm technology node. The importance of incoming trench depth variations is demonstrated for CMP polish time control in order to stabilize the copper line height. Validation data is presented for different scatterometry models including accuracy, repeatability and DoE tracking. Electrical resistance is shown to correlate to the copper trench profile measured by scatterometry. The paper will demonstrate the capability for reducing copper line height variation and the correlation of the reducing trench height variation to improved stabilization of electrical resistance.

Proceedings Article | 19 March 2015 Paper

Improved scatterometry time-to-solution using virtual reference

Alok Vaid, Givantha Iddawela, Jamie Tsai, Gilad Wainreb, Paul Isbester, Byung Cheol (Charles) Kang, Michael Klots, Yinon Katz, Cornel Bozdog, Matt Sendelbach

Proceedings Volume 9424, 94240X (2015) https://doi.org/10.1117/12.2087232

KEYWORDS: Scatterometry, Metrology, Data modeling, Etching, Time metrology, Silicon, Model-based design, Transmission electron microscopy, Geometrical optics

Read Abstract +

SPIE Journal Paper | 19 December 2014

Effect of measurement error budgets and hybrid metrology on qualification metrology sampling

Matthew Sendelbach, Niv Sarig, Koichi Wakamoto, Hyang Kyun (Helen) Kim, Paul Isbester, Masafumi Asano, Kazuto Matsuki, Carmen Osorio, Chas Archie

JM3, Vol. 13, Issue 04, 041414, (December 2014) https://doi.org/10.1117/12.10.1117/1.JMM.13.4.041414

KEYWORDS: Metrology, Transmission electron microscopy, Statistical analysis, Semiconducting wafers, Error analysis, Manufacturing, Scatterometry, Semiconductors, Uncertainty analysis, Quality measurement

Read Abstract +

SPIE Journal Paper | 6 November 2014

Hybrid metrology: from the lab into the fab

Alok Vaid, Alexander Elia, Givantha Iddawela, Cornel Bozdog, Matthew Sendelbach, Byungcheol Kang, Paul Isbester, Shay Wolfling

JM3, Vol. 13, Issue 04, 041410, (November 2014) https://doi.org/10.1117/12.10.1117/1.JMM.13.4.041410

KEYWORDS: Line edge roughness, Metrology, Scatterometry, Data modeling, Semiconducting wafers, Critical dimension metrology, Photoresist materials, Transmission electron microscopy, Etching, Scatter measurement

Read Abstract +

Proceedings Article | 2 April 2014 Paper

Impact of shrinking measurement error budgets on qualification metrology sampling and cost

Matthew Sendelbach, Niv Sarig, Koichi Wakamoto, Hyang Kyun (Helen) Kim, Paul Isbester, Masafumi Asano, Kazuto Matsuki, Alok Vaid, Carmen Osorio, Chas Archie

Proceedings Volume 9050, 90501M (2014) https://doi.org/10.1117/12.2048933

KEYWORDS: Metrology, Transmission electron microscopy, Statistical analysis, Error analysis, Semiconducting wafers, Scatterometry, Semiconductors, Statistical modeling, Calibration, Measurement devices

Read Abstract +

Proceedings Article | 10 April 2013 Paper

Implementation of hybrid metrology at HVM fab for 20nm and beyond

Alok Vaid, Lokesh Subramany, Givantha Iddawela, Carl Ford, John Allgair, Gaurav Agrawal, John Taylor, Carsten Hartig, Byung Cheol (Charles) Kang, Cornel Bozdog, Matthew Sendelbach, Paul Isbester, Limor Issascharoff

Proceedings Volume 8681, 868103 (2013) https://doi.org/10.1117/12.2012339

KEYWORDS: Metrology, Semiconducting wafers, Critical dimension metrology, Transmission electron microscopy, Etching, Process control, Reactive ion etching, Data modeling, High volume manufacturing

Read Abstract +

Showing 5 of 17 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