Mr. Stefan Meusemann Profile

Stefan Meusemann

at Advanced Mask Technology Center GmbH & Co KG

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Publications (6)

Proceedings Article | 5 October 2023 Paper

Divide et impera: a short talk about the importance of feature and model engineering

Jacob König-Otto, Stefan Meusemann

Proceedings Volume 12802, 128020I (2023) https://doi.org/10.1117/12.2673254

KEYWORDS: Analytic models, Machine learning, Data modeling, Critical dimension metrology, Engineering, Photomasks

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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 | 16 September 2014 Paper

Intra-field CDU map correlation between SEMs and aerial image characterization

Guoxiang Ning, Peter Philipp, Lloyd Litt, Stefan Meusemann, Thomas Thaler, Kristian Schulz, Martin Tschinkl, Paul Ackmann

Proceedings Volume 9235, 92350I (2014) https://doi.org/10.1117/12.2066173

KEYWORDS: Semiconducting wafers, Reticles, Metrology, Critical dimension metrology, Scanning electron microscopy, Metals, Optical proximity correction, Wafer-level optics, Inspection, Scanners

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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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Showing 5 of 6 publications

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