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

TLDR: 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.

Abstract: 2D curvilinear patterns are more and more present in the lithography landscape. For the related devices, the line edge roughness (LER) is, as well as for lines and spaces, a critical figure of merit. In this article we propose to use a dedicated edge detection algorithm to measure LER of 2D curvilinear patterns on CD-SEM images. We present an original method to validate the algorithm, in the context of roughness measurement. It is based on the generation of realistic synthetic CD-SEM images with programmed roughness and a precise PSD analysis flow. We show excellent correlation (average R2 = 0.988) between the input roughness parameters and the measured parameters for both 1D and 2D synthetic images. Using synthetic images for different number of frames, the contour extraction sensitivity to noise is also explored. Finally, the methodology is successfully applied to experimental CD-SEM images for two classes of applications : photonic devices and DSA fingerprint patterns.

Figures

Figure 3: (a), a ring oscillator design from a photonic device. (b) a detailed zoom of the associated SEM image, showing the variability of angles that have to be considered for the roughness measurement. (c), the unbiased PSD for the ring oscillator in (a).

Figure 2: 1D-pattern roughness measurement following protocol of [5] with excellent correlation with results of [6].

Figure 1: Schematic workflow used to measure roughness on curvilinear 2D patterns.

Figure 4: (a) a SEM image from a DSA fingerprint and zoom in detailed (highlighted in red). (b), the result of the contour extraction [4], and (c), the associated unbiased PSD.

Full text

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Submitted on 2 Mar 2021
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Roughness measurement of 2D curvilinear patterns:
challenges and advanced methodology
Jonathan Pradelles, Loïc Perraud, Aurélien Fay, Jean-Baptiste Henry, Jessy
Bustos, Estelle Guyez, Sébastien Berard-Bergery, Aurélie Le Pennec,
Mohamed Abaidi, Jordan Belissard, et al.
To cite this version:
Jonathan Pradelles, Loïc Perraud, Aurélien Fay, Jean-Baptiste Henry, Jessy Bustos, et al.. Roughness
measurement of 2D curvilinear patterns: challenges and advanced methodology. SPIE Advanced
Lithography„ Feb 2021, Online Only, France. pp.30, �10.1117/12.2583843�. �hal-03156564�

Roughness measurement of 2D curvilinear patterns: challenges
and advanced methodology
Jonathan Pradelles
a
, Loïc Perraud
a
, Aurélien Fay
a
, Jean-Baptiste Henry
a
, Jessy Bustos
a
,
Estelle Guyez
a
, Sébastien Berard-Bergery
a
, Aurélie Le Pennec
a
, Mohamed Abaidi
b
, Jordan
Belissard
b
, Nivea Schuch
b
, Thiago Figueiro
b
, Matthieu Millequant
b
, Patrick Schiavone
b,c
a
Univ. Grenoble Alpes, CEA, LETI, DPFT, Lithography Laboratory, F-38000 Grenoble, France
b
ASELTA Nanographics, 4 pl. Robert Schuman, F-38000 Grenoble, France
c
Univ. Grenoble Alpes, CNRS, CEA/LETI-Minatec, Grenoble INP, LTM, F-38054 Grenoble-France
The importance of measurement and analysis of pattern roughness in SEM (Scanning Electron
Microscope) images has continuously grown in the past few decades since it affects performances of
devices [1]. In parallel, non-Manhattan patterns – such as curvilinear and shapes with multiple angles –
are more and more present in recent lithography landscape [2], [3]. Key examples are photonic devices,
ILT (Inverse Lithography Technology) patterns in photomask fabrication, and DSA (Directed Self-
Assembly) fingerprint patterns for early material development. In this article the challenges associated
with roughness measurement on such curvilinear patterns will be presented and algorithms and
methodology to tackle these challenges will be proposed.
The proposed methodology relies on robust-to-noise contour extraction algorithms [4]. In this paper, an
original method to evaluate and validate contour extraction algorithms, in the context of roughness
measurement, is proposed. The schematic workflow is illustrated in Figure 1. Both a SEM model and a
noise model are calibrated using experimental SEM images from state-of-the-art equipment and
processes. This allows the generation of synthetic SEM images with arbitrary layout, programmed
roughness and controlled noise (equivalent to ‘frame by frame’ SEM image acquisition). Edge detection
algorithms and advanced ‘PSD analysis’ are applied to the images for roughness measurement. Same
strategy may then be applied to ‘real’ SEM images.
The method is first carried out in 1D patterns (straight lines). For that, synthetic SEM images are
generated, following the protocol of ref [5]. From this step, an excellent correlation with the results
obtained in [6] is found. Additionally, to that, a very low noise sensitivity of the edge contour extraction
algorithm is noticed (Figure 2).
The methodology is then extended to 2D patterns. As a first step, we make use of synthetic SEM images
to discuss the potential problems raised by curvilinear patterns, such as reference determination, data
interpolation and closing contours. In the same way as for 1D patterns, a programmed roughness, entered
as input in the workflow of Figure 1, is found back in the analysis after contour extraction and PSD
analysis. Using synthetic images for different number of frames, the contour extraction sensitivity to
noise can also be explored.
Finally, the methodology is successfully applied to experimental SEM images: unbiased PSD of 2D
curved patterns are determined for two classes of applications: photonic devices (as shown in Figure 3)
and DSA fingerprint (as shown in Figure 4).
Keywords: 2D roughness metrology, curvilinear patterns, contour metrology, PSD analysis, SEM
model, synthetic images

Figure 1: Schematic workflow used to measure roughness on curvilinear 2D patterns.
Figure 2: 1D-pattern roughness measurement following protocol of [5] with excellent correlation
with results of [6].
(a)
(b)
(c)
Figure 3: (a), a ring oscillator design from a photonic device. (b) a detailed zoom of the associated
SEM image, showing the variability of angles that have to be considered for the roughness
measurement. (c), the unbiased PSD for the ring oscillator in (a).

(a)
(b)
(c)
Figure 4: (a) a SEM image from a DSA fingerprint and zoom in detailed (highlighted in red). (b),
the result of the contour extraction [4], and (c), the associated unbiased PSD.
REFERENCES
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