Dr. Romain Bange Profile

Dr. Romain Bange

at STMicroelectronics S.A.

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Author

Publications (6)

Proceedings Article | 5 October 2023 Paper

Training dataset optimization for deep learning applied to optical proximity correction on non-regular hole masks

Mathis Urard, Clément Paquet, Charlotte Beylier, Jean-Noël Pena, Alice Caplier, Mauro Dalla Mura, Romain Bange, Roberto Guizzetti

Proceedings Volume 12802, 128020A (2023) https://doi.org/10.1117/12.2675612

KEYWORDS: Education and training, Machine learning, Optical proximity correction, Design and modelling, Databases, Photomasks, Source mask optimization, Lithography, Data modeling, Deep learning

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Proceedings Article | 28 April 2023 Poster + Paper

Robust and automated methodology for scanner matching using contour-based analysis

Jean Gabriel Simiz, Romain Alestra, Romain Bange, Julien Ducoté, Florent Dettoni, Pierre Fanton

Proceedings Volume 12494, 1249412 (2023) https://doi.org/10.1117/12.2657686

KEYWORDS: Scanners, Light sources and illumination, Semiconducting wafers, Contour extraction, Image filtering, Lithography, Automation, Scanning electron microscopy

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Proceedings Article | 1 November 2022 Paper

Mask variability with extraction of SEM image contour and area measurements

Matthieu Piloto, Romain Bange, Frank Sundermann

Proceedings Volume 12472, 124720B (2022) https://doi.org/10.1117/12.2640124

KEYWORDS: Scanning electron microscopy, Metrology, Photomasks, Calibration, Reliability, Feature extraction, Critical dimension metrology, Matrices, Statistical analysis, Printing

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Proceedings Article | 14 June 2022 Poster + Paper

A proof of concept of remote contour-based SEM metrology integration in HVM environment

Bertrand Le Gratiet, Régis Bouyssou, Julien Ducoté, Florent Dettoni, Thibaut Bourguignon, Vincent Morin, Romain Bange, Nivea Schuch, Julien Nicoulaud, Guillaume Renault, Frederic Robert, Thiago Figueiro

Proceedings Volume PC12053, PC120530S (2022) https://doi.org/10.1117/12.2615199

KEYWORDS: Metrology, Semiconducting wafers, Data integration, Visualization, Time metrology, Scanning electron microscopy, Manufacturing, Image quality, Image processing software, Image processing

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The idea to better use the thousands of CD-SEM (critical dimension scanning electron microscope) images that are acquired daily in a fab has been a driver since few years now [1,2]. In previous publications, we advocated for a remote image computing toolbox integration into the wafer fab. Such system would be based on an image computing environment (outside the metrology tools and outside the wafer fab) connected to databases and data-analytic tools so that CD-SEM image information could be enriched by the usage of more complex image treatment tool such as a CNN (convolutional neural network) for qualitative information or image analysis and contour extraction for quantitative and qualitative information. Contour based metrology offers a wide range of “new” measurements capability solutions [2,3,4] which are of interest for the process support (R&D or Production) but also for metrology equipment owners. Expressing the added value of a remote metrology toolbox on top of the existing one from metrology tool themselves is not necessarily an easy task. It is often related to use cases that will really show good return on investment. Many examples of what can be achieved with contour metrology have been published, even with thousands of images computed, but it remains far from being seen as production worthy if not experienced in real time. Based on encouraging preliminary results on valuable data extracted using remote metrology software, a proof-of-concept integration on manufacturing data was put in place in order to gather direct feedback to the fab. This paper will describe how an image computing software has been integrated to the data infrastructure of our wafer fab so that every CD-SEM image is being processed outside the CD-SEM equipment to monitor the image quality on one side (so the CD-SEM tool stability) and perform measurements on the other side. Results extracted from inline contour-based image computing over several months’ worth of data will be presented showing some of the benefits of integrating edge computing solutions in wafer fab production flow.

Proceedings Article | 26 May 2022 Presentation + Paper

Enabling on-device and target-free overlay measurement from CD-SEM contours

Thibaut Bourguignon, Régis Bouyssou, Jonathan Pradelles, Sébastien Bérard-Bergery, Bertrand Le-Gratiet, Romain Bange, Nivea Schuch, Thiago Figueiro, Nicolas Possémé

Proceedings Volume 12053, 120530C (2022) https://doi.org/10.1117/12.2613327

KEYWORDS: Overlay metrology, Scanning electron microscopy, Metrology, Image processing, Algorithm development, Feature extraction, Semiconducting wafers

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Overlay is a critical parameter for any semi-conductor foundry, with a direct impact on the fabrication yield, on the quality, and on the performances of the product. Being able to full the overlay constraints has also a direct in influence on the capacity to scale down and to integrate vertically. In addition, the shift of semiconductor applications into more demanding markets such as spatial and automotive leads to higher specifications for the process control. In the semiconductor manufacturing, the overlay is usually measured optically using dedicated targets in the scribe lines. However, targets differ from the product by their dimensions of an order of magnitude larger and by their position up to a few millimeters far from it. This can lead to residual errors and mismatches in the correction sent to the scanner, thus lowering the fabrication yield and the global product quality. For years, many SEM in-device overlay techniques have been published, however they are generally related to new generation of SEM imaging and metrology equipment and require a new dedicated target. In our case, the ambition is to extend the usage of our current CD-SEM tool park. To do so, an on-device and target-free overlay measurement process has been developed. It is based on sub-pixel contour extractions from CD-SEM images and on the use of the design. From a single image, contours of the two layers of interest are extracted from the SEM image and a custom algorithm calculates the overlay as a difference of the realigned design centroids. This dedicated algorithm allows extracting various overlay measurements from one image and enables a production friendly implementation. To evaluate the performances of this method, it has been applied on the patterns of a SRAM with scanner-programmed overlay. The measurements are compared to the conventional optical measurements. On a basis of thousands on-device measurements, the developed method showed a promising 95% rate of successful measurements. Good correlation between the optical model and the CD-SEM measured overlay on the SRAM has been demonstrated, reaching a coefficient of correlation of 0.99 on a pattern where conventional centroid or CD based overlay are limited. Finally, the flexibility of the method to measure various patterns with an ease of recipe creation is shown. Contour-based metrology offers the capability to extract highly valuable information from SEM images while keeping the layers differentiation. The proposed measurement process, being automated and requiring relatively low human inputs is a promising solution for a SEM on device overlay metrology suitable in a high-volume manufacturing environment.

Proceedings Article | 26 May 2022 Presentation + Paper

Highlighting stochastic manifestations in 193 nm immersion lithography with contour-based metrology metrics

Elvire Soltani, Bertrand Le-Gratiet, Sébastien Bérard-Bergery, Jonathan Pradelles, Romain Bange, Nivea Schuch, Thiago Figueiro, Raluca Tiron

Proceedings Volume 12053, 1205308 (2022) https://doi.org/10.1117/12.2613322

KEYWORDS: Stochastic processes, Metrology, Semiconducting wafers, Statistical analysis, Critical dimension metrology, Deep ultraviolet

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