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Sébastien Bérard-Bergery

Researcher at University of Grenoble

Publications -  12
Citations -  46

Sébastien Bérard-Bergery is an academic researcher from University of Grenoble. The author has contributed to research in topics: Lithography & Resist. The author has an hindex of 2, co-authored 12 publications receiving 35 citations.

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Proceedings ArticleDOI

Template affinity role in CH shrink by DSA planarization

TL;DR: In this paper, the authors investigate the potential of DSA to address contact and via levels patterning with high resolution by performing either CD shrink or contact multiplication, and compare different DSA processes based on several success criteria such as: CD control, defectivity, and placement control.
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Rigorous Model-Based Mask Data Preparation Algorithm Applied to Grayscale Lithography for the Patterning at the Micrometer Scale

TL;DR: In this article, a rigorous lithographic model has been developed in Python to simulate the process of imaging, exposure and development of an i-line photoresist, and a mask data preparation algorithm capable of optimizing simultaneously both the size and position of the dots on a grayscale mask has been implemented.
Proceedings ArticleDOI

Complete data preparation flow for Massively Parallel E-Beam lithography on 28nm node full-field design

TL;DR: The complete MP-EBL data preparation flow was demonstrated for a 28 nm node Metal1 layout in 37 hours, and the final verification step shows that the Edge Placement Error (EPE) is kept below 2.25 nm over an exposure dose variation of 8%.
Proceedings ArticleDOI

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

TL;DR: This article proposes to use a dedicated edge detection algorithm to measure LER of 2D curvilinear patterns on CD-SEM images with excellent correlation between the input roughness parameters and the measured parameters for both 1D and 2D synthetic images.
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Tilted beam scanning electron microscopy, 3-D metrology for microelectronics industry

TL;DR: In this paper, the authors used Monte-Carlo simulations to reconstruct the topographic shape of the observed pattern from SEM images, and then used a low-complexity linear model to obtain the geometrical parameters of the structure.