Dr. Clemens S. Utzny Profile

Dr. Clemens S. Utzny

Senior process engineer at KLA GmbH

SPIE Involvement:

Author | Instructor

Publications (26)

Proceedings Article | 10 April 2024 Poster + Paper

Advanced cross-layer overlay simulation with exposure grid re-calculation

Yueh-Feng Lu, Chao-Jen Tsou, Onur Demirer, Holger Bald, Siegfried Hille, Meng-Syun Li, Martin Freitag, Clemens Utzny, Scott Eitapence, Boris Habets, Cheng-Shuai Li, Kao-Tsai Tsai

Proceedings Volume 12955, 1295524 (2024) https://doi.org/10.1117/12.3009745

KEYWORDS: Optical alignment, Semiconducting wafers, Alignment modeling, Overlay metrology, Optical lithography, Scanners, Systems modeling, Performance modeling, Control systems, Computer simulations

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Proceedings Article | 10 April 2024 Poster + Paper

An alternative to M+3S overlay dispositioning metric: number-of-dies-within-spec

Alberto Lopez-Gomez, Stefan Buhl, Eric Jehnes, Patrick Lomtscher, Manuela Gutsch, Xaver Thrun, Clemens Utzny, Philip Groeger, Johannes Kowalewski

Proceedings Volume 12955, 129553T (2024) https://doi.org/10.1117/12.3022117

KEYWORDS: Semiconducting wafers, Overlay metrology, Scanners, Semiconductors, Modeling, Interpolation, High volume manufacturing, Data modeling, Performance modeling, Mixtures

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Proceedings Article | 10 April 2024 Presentation + Paper

Micro-scale in-device overlay

Boris Habets, Stefan Eyring, Mirko Wittkoetter, Frank Laske, Marc Filzen, Clemens Utzny

Proceedings Volume 12955, 1295518 (2024) https://doi.org/10.1117/12.3012648

KEYWORDS: Semiconducting wafers, Overlay metrology, Logic, Scanning electron microscopy, Design, Spatial resolution, Nonlinear optics, Interference (communication), Reproducibility, Target detection

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Proceedings Article | 10 April 2024 Poster + Paper

Overlay control improvements through dynamic sampling

Onur Demirer, Robin Maximilian Zech, Chao-Jen Tsou, W. Wang, C. Huang, Elvis Yang, Afu Chiu, Alexander Muehle, Holger Bald, Clemens Utzny, Scott Eitapence, Boris Habets

Proceedings Volume 12955, 1295527 (2024) https://doi.org/10.1117/12.3009830

KEYWORDS: Semiconducting wafers, Overlay metrology, Control systems, Simulations, Scanners, Metrology, Lithography, Advanced process control, High volume manufacturing

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Proceedings Article | 5 October 2023 Paper

The use of cross-validation for overlay model selection

Sungwoo Jung, Clemens Utzny

Proceedings Volume 12802, 128020R (2023) https://doi.org/10.1117/12.2675560

KEYWORDS: Data modeling, Overlay metrology, Semiconducting wafers, Overfitting, Cross validation, Statistical modeling, Performance modeling

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Proceedings Article | 28 April 2023 Poster + Paper

Focus spot monitoring: design of an automatable detection, classification, and impact analysis pipeline

Syed Naime Mohammad, Sven Boese, Philip Groeger, Holger Bald, Alberto Lopez-Gomez, Clemens Utzny, Stefan Buhl

Proceedings Volume 12494, 1249416 (2023) https://doi.org/10.1117/12.2657970

KEYWORDS: Semiconducting wafers, Lithography, Zernike polynomials, Particles, Optical lithography, Semiconductor manufacturing, Modeling, Design and modelling, Scanners, Yield improvement

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Proceedings Article | 28 April 2023 Poster + Paper

Development of novel focus spot detection methods for high-volume manufacturing

Syed Naime Mohammad, Chao-Jen Tsou, Afu Chiu, Norman Birnstein, Erick deGouw, Clemens Utzny, Philip Groeger, Stefan Buhl, W. Wang, C. Huang, Elvis Yang, T. Yang, K. Chen

Proceedings Volume 12494, 1249415 (2023) https://doi.org/10.1117/12.2657965

KEYWORDS: Semiconducting wafers, Scanners, Contamination, High volume manufacturing, Particles, Neodymium, Tunable filters, Defense systems, Sensors, Image enhancement

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Proceedings Article | 26 May 2022 Poster + Presentation + Paper

A new development algorithm to optimize scanner alignment sampling for cross-chuck overlay performance optimization

Chieh-Chen Chiu, Feng Tian, Wei Feng, Mingqi Gao, Andy Lan, Chao-Jen Tsou, Shengyuan Zhong, Norman Birnstein, Robin Maximilian Zech, Clemens Utzny, Jin Zhu, Jincheng Pei, Kevin Huang

Proceedings Volume 12053, 1205329 (2022) https://doi.org/10.1117/12.2627283

KEYWORDS: Optical alignment, Optimization (mathematics), Scanners, Semiconducting wafers, Overlay metrology, Data modeling, Performance modeling, Semiconductors

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Proceedings Article | 26 September 2019 Presentation + Paper

Loading effect correction set up by supplementing CD measurement analysis with machine learning

Christian Bürgel, Martin Sczyrba, Clemens Utzny

Proceedings Volume 11148, 111480C (2019) https://doi.org/10.1117/12.2539821

KEYWORDS: Critical dimension metrology, Machine learning, Photomasks, Etching, Scanning electron microscopy, Convolution, Semiconductors, Quality measurement, Environmental sensing, Manufacturing

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With semiconductor technology approaching and exceeding 10 nm design rules the quality requirements for photomasks are continuously tightening. One of the crucial parameters is improved control of the critical dimension (CD) across the photomask. As long as linearity and through pitch effects are not involved, the quality measure is typically defined as CD uniformity. This parameter is normally measured on repeating structures of same size and shape, which are not necessarily placed in identical environments. Density dependent process effects, also called loading effects (LE), pose a challenge for the required CD control. There are several possible contributors to this kind of error within the mask manufacturing flow, such as etch driven loading effects, fogging effects during 50kV exposure and develop driven loading effects. All of these operate at different working ranges, starting at millimeters going down to only a few 100 μm scale. It is comparably easy to derive models for large scale phenomena like etch loading or fogging effects, in contrast to that it is not as straight forward to find suitable models for very short-range effects. A large amount of CD measurements taken by CD SEM is needed to identify such signals of low magnitude and short scales, which make the setup very resource intensive. Furthermore, this methodology requires artificial designs and test structures which aim to sample only the effect of interest. In this paper we present a strategy which combines CD SEM measurements from dedicated test masks with the results from regular product masks. The aim is the derivation and validation of the loading effect correction range and strength. In the first step the data from test masks is analyzed to set up the basic correction parameters. Following this, the approach is supplemented by product data where we combine mask CD and design data. The clear field distribution of the design is convoluted with respect to a hierarchy of length scales. This data is the input for a support vector machine analysis. Thus, we employ a flat machine learning algorithm. However, the input data has been set up to reflect multiple layers of convolution. This particular approach has been chosen, as each convolution length scale is associated with mask process properties, thus alleviating the burden of interpretation which typically mars the interpretation of models obtained by machine learning approaches.

Proceedings Article | 29 August 2019 Paper

Necessity is the mother of invention: support vector machines for CD control

Christian Bürgel, Martin Sczyrba, Clemens Utzny

Proceedings Volume 11177, 1117709 (2019) https://doi.org/10.1117/12.2535745

KEYWORDS: Machine learning, Data modeling, Critical dimension metrology, Performance modeling, Process control, Visualization, Data centers, Solids, Data processing, Optical inspection

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The currently increasing demand for photo-masks in the regime of the 14nm technology drives many initiatives towards capacity and throughput increase of existing production line. Such improvements are facilitated by improved control mechanisms of the tools and processes used within a production line. While process control of long range parameters such as the average CD behavior is demanding yet conceptually well understood, other parameters such as the small scales CD properties are quite often elusive to process control. These properties often require a dedicated test mask to be processed in order to be validated. In this paper we introduce a systematic approach towards a product based monitoring of small scale CD behavior which uses a CD characteristic extracted from the defect inspection process. This characteristic represents the influence of CD relevant processes starting from 200m up to 4000 m. Large variations in the scale and magnitude of the CD characteristic are induced by layout specific design variations. However, the shape of these distinct curves is remarkably similar, which enables their use for monitoring as well as controlling the mask processes on the above stated spatial scales. In this paper it is demonstrated, that a meaningful process evaluation can be performed by using the classification capabilities of the support vector machines. The small scales CD characteristics presented in figure 1 originate from two distinct tools. Matching of the two tools can be assessed by training a support vector machine to classify the small scales CD characteristics according to their origin. The classification performance on the resampled training set as well as on the validation set is a robust measure for tool matching. The results of this approach are depicted in figure 2. The left panel shows the AUC statistics of bootstrapping resamples for tool comparison “A”. In this case no noticeable difference between the two tools is found (an average AUC of 0.55 suggest no learnable difference). This is contrasted by the tool comparison “B”, here the classifier has an average AUC of 0.75, indicating a learnable difference in the tool performances. This result is backed by the process understand of both tool types.

Proceedings Article | 27 June 2019 Paper

Deal gently with the bird you are trying to catch: small scale CD control with machine learning

Christian Bürgel, Martin Sczyrba, Clemens Utzny

Proceedings Volume 11178, 111780L (2019) https://doi.org/10.1117/12.2534978

KEYWORDS: Critical dimension metrology, Machine learning, Data modeling, Process control, Visualization, Data centers, Solids, Detection and tracking algorithms, Data processing, Optical inspection

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Proceedings Article | 19 September 2018 Paper

Machine learning methods applied to process qualification

Mark Herrmann, Stefan Meusemann, Clemens Utzny

Proceedings Volume 10775, 1077510 (2018) https://doi.org/10.1117/12.2323605

KEYWORDS: Machine learning, Principal component analysis, Statistical analysis, Performance modeling, Data modeling, Photomasks, Statistical modeling, Critical dimension metrology, Distance measurement

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With the substantial surge in the need for high-end masks it becomes increasingly important to raise the capacity of the corresponding production lines. To this end the efficient qualification of matching tools and processes within a production line is of utmost relevance. Matching is typically judged by the processing of dedicated lots on the new tool and process. The amount of qualification lots should on the one hand be very small, as the production of qualification plates is expensive and uses capacity of the production corridor. On the other hand the strict requirements of high-end products induce very tight specification limits on the matching criteria. It is thus often very difficult to assess tool or process matching on the basis of a small amount of lots. In this paper we expound on a machine learning based strategy which assesses the mask characteristics of a qualification plate by learning the typical behavior of these characteristics within the production line variations. We show that by careful selection of reference production plates as well as by setting specification limits based on the production behavior we can manage the qualification tasks efficiently by using a small number of masks. The specification characteristics as well as the specific limits are selected and determined using a Naïve Bayes learner. The resulting performance for prediction of tool and process matching is assessed by considering the resulting receiving operator curve. As a result we obtain an approach towards the assessment of qualification data which enables engineers to assess the tool and process matching using a small amount of matching data under the constraint of substantial measurement uncertainties. As an outlook we discuss how this approach can be used to examine the reverse question of detecting process failures, i.e. the automated ability to raise a flag when the current production characteristics start to deviate from their typical characteristics. Overall, in this paper we show how the rapidly evolving field of machine learning increasingly impacts the semiconductor production process.

Proceedings Article | 28 September 2017 Paper

Splendidly blended: a machine learning set up for CDU control

Clemens Utzny

Proceedings Volume 10446, 104460N (2017) https://doi.org/10.1117/12.2279430

KEYWORDS: Machine learning, Manufacturing, Photomasks, Process control, Critical dimension metrology, Artificial intelligence, Internet, Semiconductors, Evolutionary algorithms, Statistical analysis

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Proceedings Article | 20 October 2016 Paper

Smart mask ship to control for enhanced on wafer CD performance

Clemens Utzny, Karl Schumacher, Rolf Seltmann

Proceedings Volume 10032, 100320I (2016) https://doi.org/10.1117/12.2248889

KEYWORDS: Photomasks, Critical dimension metrology, Semiconducting wafers, Manufacturing, Scanning electron microscopy, Error analysis, Semiconductors, Process control, Lithography, Control systems

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Proceedings Article | 20 October 2016 Paper

CD process control through machine learning

Clemens Utzny

Proceedings Volume 10032, 100320R (2016) https://doi.org/10.1117/12.2248903

KEYWORDS: Machine learning, Process control, Receivers, Critical dimension metrology, Data modeling, Photomasks, Principal component analysis, Binary data, Performance modeling, Statistical analysis

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Proceedings Article | 10 April 2013 Paper

When things go pear shaped: contour variations of contacts

Clemens Utzny

Proceedings Volume 8681, 868109 (2013) https://doi.org/10.1117/12.2013911

KEYWORDS: Critical dimension metrology, Shape analysis, Metrology, Extreme ultraviolet, Principal component analysis, Scanning electron microscopy, Statistical analysis, EUV optics, Visualization, Control systems

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Proceedings Article | 8 November 2012 Paper

CD control with defect inspection: you can teach an old dog a new trick

Clemens Utzny, Albrecht Ullrich, Jan Heumann, Elias Mohn, Stefan Meusemann, Rolf Seltmann

Proceedings Volume 8522, 85220B (2012) https://doi.org/10.1117/12.965531

KEYWORDS: Critical dimension metrology, Inspection, Photomasks, Scanning electron microscopy, Defect inspection, Spatial resolution, Semiconducting wafers, Control systems, Manufacturing, Data modeling

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Achieving the required critical dimensions (CD) with the best possible uniformity (CDU) on photo-masks has always played a pivotal role in enabling chip technology. Current control strategies are based on scanning electron microscopy (SEM) based measurements implying a sparse spatial resolution on the order of ~ 10^-2 m to 10^-1 m. A higher spatial resolution could be reached with an adequate measurement sampling, however the increase in the number of measurements makes this approach in the context of a productive environment unfeasible. With the advent of more powerful defect inspection tools a significantly higher spatial resolution of 10^-4 m can be achieved by measuring also CD during the regular defect inspection. This method is not limited to the measurement of specific measurement features thus paving the way to a CD assessment of all electrically relevant mask patterns. Enabling such a CD measurement gives way to new realms of CD control. Deterministic short range CD effects which were previously interpreted as noise can be resolved and addressed by CD compensation methods. This in can lead to substantial improvements of the CD uniformity. Thus the defect inspection mediated CD control closes a substantial gap in the mask manufacturing process by allowing the control of short range CD effects which were up till now beyond the reach of regular CD SEM based control strategies. This increase in spatial resolution also counters the decrease in measurement precision due to the usage of an optical system. In this paper we present detailed results on a) the CD data generated during the inspection process, b) the analytical tools needed for relating this data to CD SEM measurement and c) how the CD inspection process enables new dimension of CD compensation within the mask manufacturing process. We find that the inspection based CD measurement generates typically around 500000 measurements with a homogeneous covering of the active mask area. In comparing the CD inspection results with CD SEM measurement on a single measurement point base we find that optical limitations of the inspection tool play a substantial role within the photon based inspection process. Once these shift are characterized and removed a correlation coefficient of 0.9 between these two CD measurement techniques is found. This finding agrees well with a signature based matching approach. Based on these findings we set up a dedicated pooling algorithm which performs on outlier removal for all CD inspections together with a data clustering according to feature specific tool induced shifts. This way tool induced shift effects can be removed and CD signature computation is enabled. A statistical model of the CD signatures which relates the mask design parameters on the relevant length scales to CD effects thus enabling the computation CD compensation maps. The compensation maps address the CD effects on various distinct length scales and we show that long and short range contributions to the CD variation are decreased. We find that the CD uniformity is improved by 25% using this novel CD compensation strategy.

Proceedings Article | 8 November 2012 Paper

Comparison of critical dimension measurements of a mask inspection system with a CD-SEM

Jan Heumann, Albrecht Ullrich, Clemens Utzny, Stefan Meusemann, Frank Kromer, John Whittey, Edgardo Garcia, Mark Wagner, Norbert Schmidt

Proceedings Volume 8522, 85220G (2012) https://doi.org/10.1117/12.977248

KEYWORDS: Critical dimension metrology, Photomasks, Inspection, Semiconducting wafers, Scanning electron microscopy, Optical testing, Electron microscopes, Opacity, Manufacturing, Wafer manufacturing

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Proceedings Article | 30 June 2012 Paper

Tales of scales: how to enable backup process tool qualification for high-end photomasks

G. Cantrell, Christian Bürgel, Martin Sczyrba, Jan Heumann, Stefan Meusemann, Clemens Utzny

Proceedings Volume 8441, 844103 (2012) https://doi.org/10.1117/12.964671

KEYWORDS: Critical dimension metrology, Reticles, Photoresist processing, Photomasks, Principal component analysis, Inspection, Scanning electron microscopy, Spatial resolution, Lithography, Manufacturing

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Proceedings Article | 14 October 2011 Paper

More than monitoring: advanced lithographic process tuning

G. R. Cantrell, Jo Alvin Dumaya, Christian Bürgel, Axel Feicke, Martin Häcker, Clemens Utzny

Proceedings Volume 8166, 81660M (2011) https://doi.org/10.1117/12.896416

KEYWORDS: Critical dimension metrology, Photoresist processing, Lithography, Reticles, Principal component analysis, Photomasks, Etching, Cadmium, Statistical analysis, Chemically amplified resists

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Proceedings Article | 14 October 2011 Paper

The trouble starts with using electrons: putting charging effect correction models to the test

Timo Wandel, Clemens Utzny, Noriaki Nakayamada

Proceedings Volume 8166, 81661C (2011) https://doi.org/10.1117/12.898755

KEYWORDS: Photomasks, Electrons, Distortion, Solids, Photoresist processing, Metrology, Diamond, Mask making, Lithography, Semiconducting wafers

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Proceedings Article | 25 September 2010 Paper

CD inspection by Nuflare NPI 6000 tool

J. Richter, C. Utzny, J. Heumann, Shuichi Tamamushi, Noriyuki Takamatsu

Proceedings Volume 7823, 78232L (2010) https://doi.org/10.1117/12.864976

KEYWORDS: Critical dimension metrology, Inspection, Scanning electron microscopy, Metals, Photomasks, Time metrology, Metrology, Optical proximity correction, Defect inspection, Electron beams

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Proceedings Article | 25 September 2010 Paper

Using principal component analysis for photomask CD signature investigations

G. R. Cantrell, Christian Bürgel, Axel Feicke, Martin Sczyrba, Clemens Utzny

Proceedings Volume 7823, 78232O (2010) https://doi.org/10.1117/12.864139

KEYWORDS: Critical dimension metrology, Reticles, Principal component analysis, Photoresist processing, Photomasks, Error analysis, Data modeling, Manufacturing, Metrology, Process engineering

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Proceedings Article | 23 September 2009 Paper

Mask performance improvement with mapping

Clemens Utzny, Eric Cotte, Timo Wandel, Jan Hendrik Peters

Proceedings Volume 7488, 74880N (2009) https://doi.org/10.1117/12.834302

KEYWORDS: Photomasks, Critical dimension metrology, Signal to noise ratio, Principal component analysis, Process control, Image registration, Electron beam melting, Error analysis, Lithography, Tolerancing

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Proceedings Article | 14 May 2007 Paper

Matching of different CD-metrology tools for global CD signature on photomasks

E.-M. Zerbe, T. Marschner, J. Richter, C. Utzny

Proceedings Volume 6607, 660729 (2007) https://doi.org/10.1117/12.728992

KEYWORDS: Critical dimension metrology, Photomasks, Scanning electron microscopy, Metrology, Standards development, Semiconducting wafers, Calibration, Electron beams, Molecules

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Proceedings Article | 20 October 2006 Paper

Determination of spatial CD signatures on photomasks

Clemens Utzny, Martin Rößiger

Proceedings Volume 6349, 63490P (2006) https://doi.org/10.1117/12.685952

KEYWORDS: Critical dimension metrology, Photomasks, Statistical analysis, Stochastic processes, Semiconducting wafers, Lithography, Data analysis, Manufacturing, Cadmium, Standards development

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Showing 5 of 26 publications

Course Instructor

SC1350: Overlay Control: Error Budget and Metrology

This course focuses on overlay (OVL) control for product devices detailing the distinct factors affecting on-product overlay (OPO) performance. We delve into key characteristics relevant to OPO, including OVL residuals and non-zero offset (NZO). By exploring the complex, critical inter-relationships among scanner alignment schemes, after-development inspection measurements, and in-die overlay metrology, we build a foundation for better understanding and controlling OPO. Our goal is to enlighten and educate you to better optimize the OPO process for a single and multiple lithographic layers and products in your fab. The course clarifies the fundamental aspects of each metrology technique. This course will benefit those keen on comprehending the factors that shape the landscape of image-based or scatterometry-based overlay, the ongoing challenges of recipe optimization and self-referencing, the measurement and utilization of device overlay, and, most importantly, how all these elements interact and converge.

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