Dr. Elie Sezestre Profile

Dr. Elie Sezestre

at CEA Grenoble

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Publications (5)

Proceedings Article | 27 April 2023 Presentation + Paper

Self-supervised deep learning neural network for CD-SEM image denoising using reduced dataset

Alexandre Moly, Nivea Schuch, Frederic Robert, Thiago Figueiro, Jessy Bustos, Loïc Perraud, Jonathan Pradelles, Elie Sezestre

Proceedings Volume 12496, 124961D (2023) https://doi.org/10.1117/12.2659168

KEYWORDS: Contour extraction, Scanning electron microscopy, Neural networks, Image processing, Calibration, Deep learning, Image denoising, Image acquisition, Machine learning

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Proceedings Article | 27 April 2023 Presentation + Paper

Can remote SEM contours be used to match various SEM tools in fabs?

Jonathan Pradelles, Loïc Perraud, Elie Sezestre, Aurélien Fay, Nivea Schuch, Thiago Figueiro, Frédéric Robert

Proceedings Volume 12496, 124960U (2023) https://doi.org/10.1117/12.2658426

KEYWORDS: Scanning electron microscopy, Calibration, Metrology, Contour extraction, Contour modeling, Critical dimension metrology, Data modeling, Measurement uncertainty, Image processing

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CD-SEM is one of the workhorse metrology tools for evaluating the patterning performances in the semiconductor industry. With the continuous shrink of device feature sizes, measurement uncertainty, of such devices should improve accordingly, including tool-to-tool matching. Great efforts are made in fabs to ensure matching between CD-SEMs. Basically, there are two approaches : tool matching based on statistical CD measurements and tool matching based on physical tool parameters. The first methods require long recipe run on each tool for data collection while the second methods require dedicated maintenance tool time. In parallel, the emergence of SEM contour metrology brought new opportunities, for example in OPC modeling, process assessment or overlay metrology. Recently, a step has been made to demonstrate that offline SEM contour metrology can be matched with standard CD-SEM. In this paper we evaluate the possibility to use SEM contour metrology to perform a remote CD matching on images coming from two different CD-SEM. A large set of patterns of various dimensions and shapes (both 1D and 2D) is imaged with two different CD-SEM after etch process step. For each image the contours are extracted using a remote software. A matching model is calibrated to match contour measurements of the same pattern imaged with the two CD-SEMs. The matching model can be calibrated using either one or both inline SEM measurement or using additional metrology data (typically AFM measurement or any reference metrology measurement). The matching model parameters include both parameters for the contour extraction calibration and measurement matching parameters. Two modeling approaches are compared : one matching model averaging multiple measurement matching sites and a multiple (per pattern type) contour calibrations/ measurement matching sites model. These different matching models are assessed using as main metrics the remaining CD difference between SEM contour measurements on images coming from the two CD-SEM (ΔCD_matching) and TMU analysis. We show that an overall ΔCD_matching variability of 0.6 nm and a TMU below 2 nm can be obtained with the dataset. We evaluated a strategy to achieve matching of two CD-SEM using a remote contour extraction solution and a matching model.

Proceedings Article | 13 June 2022 Presentation

Hybrid-contours: a strategy for improving CD-SEM metrology over complex 2D patterns

Nivea Schuch, Charles Valade, Alexandre Moly, Frederic Robert, Elie Sezestre, Juline Scoarnec, Jonathan Pradelles, Loic Perraud, Olivier Dubreuil, Thiago Figueiro

Proceedings Volume PC12053, PC1205308 (2022) https://doi.org/10.1117/12.2615831

KEYWORDS: Metrology, Signal detection, Scanning electron microscopy, Model-based design, Reliability, Signal to noise ratio, Semiconductors, Semiconductor manufacturing, Process control, Optical proximity correction

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Proceedings Article | 26 May 2022 Poster + Paper

Model-based contour extraction: an enabler for very low-frame SEM images metrology

Élie Sezestre, Juline Scoarnec, Jonathan Pradelles, Loïc Perraud, Aurélien Fay, Sébastien Bérard-Bergery, Jessy Bustos, Jean-Baptiste Henry, Olivier Dubreuil, Ivanie Mendes, Charles Valade, Alexandre Moly, Alice Batte, Nivea Schuch, Frederic Robert, Thiago Figueiro

Proceedings Volume 12053, 120531G (2022) https://doi.org/10.1117/12.2616527

KEYWORDS: Scanning electron microscopy, Metrology, Image processing, Calibration, Optical proximity correction, Edge detection, Mathematical modeling, Evolutionary algorithms, Detection and tracking algorithms

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Among metrology tools in the semi-conductor manufacturing, critical dimension scanning electron microscopes (CD-SEM) are the most broadly used, especially due to their high resolution, low destructivity, and high throughput. Contour metrology on CD-SEM images has become essential for characterization, modelling, and control of advanced lithography processes. In particular, OPC model’s accuracy can be highly improved using contours metrology. One of the issues when dealing with CD-SEM metrology is that the results are noise sensitive. Moreover, diminishing noise in CD-SEM acquisition leads to resist shrinkage due to exposure time increase. In addition, post-treatment of these shrinkage effects requires compensation algorithms such as artificial intelligence (AI)- driven algorithms, that are another contributor to the error budget of metrology systems. There is thus a need for an accurate, robust to noise, and purely deterministic edge detection algorithm. In this article, we evaluate the benefits of relying on a model-based contour extraction approach for performing measurements. This approach is applied onto both synthetic and experimental CD-SEM images with various patterns (mostly 2D) and noise levels to assess the influence of image integration (frame number) on the contour detection and CD measurement. We demonstrate that a model-based contour extraction algorithm is able to precisely characterize SEM-induced 2D resist shrinkage. We observe that this model-based approach is more robust to noise than standard algorithms by 21% on synthetic data and by 36% on experimental data. Another way of seeing it is, while keeping the same precision, a model-based contour extraction approach can significantly reduce the requested image frame number. The benefits of adopting this approach range from reducing the shrinkage effects to improving SEM image acquisition time. Eventually, no step of shrinkage modelling calibration nor AI-driven image post processing are needed which implies a gain on simplicity and avoids modelling errors.

Proceedings Article | 22 February 2021 Presentation + Paper

Roughness measurement of 2D curvilinear patterns: challenges and advanced methodology

Jonathan Pradelles, Loïc Perraud, Aurélien Fay, Elie Sezestre, Jean-Baptiste Henry, Jessy Bustos, Estelle Guyez, Sébastien Berard-Bergery, Aurélie Le Pennec, Mohamed Abaidi, Jordan Belissard, Nivea Schuch, Matthieu Millequant, Thiago Figueiro, Patrick Schiavone

Proceedings Volume 11611, 1161110 (2021) https://doi.org/10.1117/12.2583843

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