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ORIGINAL PAPER
Quantitative Analysis of X-ray Lithographic Pores by SEM ImageProcessing
U. Phromsuwan1, Y. Sirisathitkul2, C. Sirisathitkul1*, P. Muneesawang3 and
B. Uyyanonvara4
1Molecular Technology Research Unit, School of Science, Walailak University, Nakhon Si Thammarat 80161,
Thailand
2School of Informatics, Walailak University, Nakhon Si Thammarat 80161, Thailand
3Department of Electrical and Computer Engineering, Faculty of Engineering, Naresuan University,
Phitsanulok 65000, Thailand
4School of Information, Computer and Communication Technology, Sirindhorn International Institute of
Technology (SIIT), Thammasat University, Pathumthani 12000, Thailand
Received: 15 October 2013 / Accepted: 01 December 2013 / Published online: 12 December 2013
� Metrology Society of India 2013
Abstract: Arrays of regular macropores in electronic, magnetic, photonic and sensing devices can be patterned by X-ray
lithography. Such structures inevitably contain some irregularity and require time-consuming pattern inspections. In this
work, a pattern inspection by intensity-based digital image processing procedure is proposed and tested on scanning
electron microscopy images of porous SU-8 polymer resist. The Otsu’s thresholding converted grayscale to binary images
and the closing morphology algorithm was applied to reduce noise in the images. The Canny edge detector was used to
identify the contour of each pore by detecting abrupt intensity changes in the binary image. Pores were detected and their
sizes were subsequently evaluated. The morphological distributions analyzed by this procedure are comparable to those
carried out by the one-by-one human inspection.
Keywords: Macropore; X-ray lithography; SEM; Image processing; Image segmentation
1. Introduction
Microscale measurements are crucial steps in both scien-
tific study of organism and industrial fabrication of artifi-
cial devices. Microstructures are commonly visualized by
scanning electron microscope (SEM). However, the inter-
pretation of microscope images is susceptible to human
errors and subjective in nature [1]. Moreover, the quanti-
tative analysis conventionally based on the one-by-one
human inspection for statistically sufficient number is time-
consuming. To overcome these limitations, digital imaging
processing has been implemented to facilitate the analysis
and yield the statistically significant data. It follows that
image processing has become an essential tool for quanti-
tative SEM analysis in the past decade [2, 3]. Different
algorithms and techniques were implemented in a variety
of porous structures including anodic alumina membranes
[4], porous fuel cell materials [5], activated carbon fibers
[6], tissue scaffolds [7], solid state nanopores [8], sintered
ZnO [9] and GaN [10]. SEM image processing has also
widely adopted in the characterizations of minerals and
construction materials [11–13]. Following the growing
demand, several image processing packages have been
developed including Image J freeware [14] but quantifying
parameters from different circumstances is still a topic
under research and development.
In this work, the intensity-based image segmentation is
proposed to analyze SEM images of periodic pores. High
aspect ratio pores on SU-8 photoresist with regular spacing
can be patterned by X-ray lithography and have applica-
tions in electronic, magnetic, photonic and sensing devices
[15]. Since deviations in morphology and position affect
the properties, the pattern inspection by SEM is therefore
mandatory. In the edge detection process, the intensity in
the image is probed and the contours with large intensity*Corresponding author, E-mail: [email protected]
M �APAN-Journal of Metrology Society of India (December 2013) 28(4):327–333
DOI 10.1007/s12647-013-0089-2
123
gradients are identified as the edge of objects. Pixels rep-
resenting an object of interest can then be separated from
the background and the array of objects can be analyzed
once their boundaries are located. To this end, the algo-
rithms referred to as edge detectors are available in the
image processing toolbox of Matlab. By comparing the
detection of cross-sectional areas of periodic magnetic
micropillars in SEM micrographs [16], the Canny operator
showed better performance than other algorithms including
Laplacian of Gaussian, Prewitt, Sobel and Roberts Cross.
In the case of micrographs with low contrast, objects of
interest are not clearly separated from the background and
the efficiency of edge detector is predictably reduced. The
preprocessing is therefore required prior to the intensity-
based image segmentation. After the edge detection, noises
in SEM images are also removed. The morphology of pores
can eventually be quantified.
2. Experimental
A layer of 50 lm thick SU-8 was spin-coated on a graphite
substrate and soft-baked at 95 �C for 40 min to improve
the adhesion and remove the solvent. After drying at room
Fig. 1 Schematic diagram of samples after a masked irradiation, b resist development and c cobalt deposition
Fig. 2 Original test images;
a hand-drawn image, b SEM
micrograph of patterned
macropores on graphite
substrate, c, d SEM
micrographs of cobalt-coated
patterned macropores
328 U. Phromsuwan et al.
123
temperature for 24 h, three samples were exposed to the
X-ray at the beam line BL6a of the Synchrotron Light
Research Institute, Thailand. To transfer a pattern from the
graphite mask to a layer of SU-8, X-ray of wavelength
1.24 nm was irradiated onto the substrate placed under the
mask for 10 min (Fig. 1a). After the mask irradiation, the
exposed resist in an area about 5 9 5 mm was left at room
temperature for 24 h and then developed (Fig. 1b). The
macropore arrays on harden SU-8 photoresist layer were
finally inspected by SEM.
Three SEM images of pore arrays (96 dot per inch,
1,024 9 943 pixels) were tested. The SEM micrograph in
Fig. 2b was taken from the SU-8 photoresist on a graphite
substrate patterned by X-ray lithography. Two other sam-
ples were sputter-deposited by cobalt layers (Fig. 1c) and
then photographed as shown in Figs. 2c, d. In addition to
SEM images, an RGB image of a 6 9 11 array of circles
drawn by Paint on Windows 7 (96 dot per inch, 768 9 480
pixels) shown in Fig. 2a was also tested to compare the
results.
The SEM image processing procedure run on Matlab
7.11 consists of 8 steps.
Step 1: The grayscale image in TIF format was read.
Step 2: The image was converted to a binary image by
using the Otsu’s thresholding value derived from
the intensity difference between objects (pores)
and the background.
Step 3: The morphological closing process was
employed in order to remove the noise and
enhance the edge.
Step 4: Each individual object was then detected by
using the Canny edge detector.
Step 5: The detected objects were then filled because the
intensity of pixels surrounding them was non-
uniform. The pixel was added from one side to
the other until the area was enclosed by pixels of
comparable intensity.
Step 6: The small unfilled objects of less than 2,000
pixels were subsequently removed.
Fig. 3 Outputs from each step of the proposed image processing procedure for the hand-drawn image in Fig. 2a
Table 1 Mean and standard deviations of diameter and cross-sectional area of patterned macropores obtained from the image processing
procedure and the one-by-one human inspection
Image Image processing procedures One-by-one inspection
Number of pores Average diameter Average area Number of pores Average diameter Average area
Fig. 2a 66 44.61 ± 0.00 pixels 1,563 ± 0 pixels 66 44.00 ± 0.00 pixels 1,521.1 ± 0.3 pixels
Fig. 2b 36 17.63 ± 0.75 lm 244.5 ± 21.1 lm2 36 17.16 ± 0.88 lm 244.2 ± 21.2 lm2
Fig. 2c 41 15.54 ± 0.46 lm 189.7 ± 11.1 lm2 42 15 47 ± 0.81 lm 189.9 ± 11.4 lm2
Fig. 2d 39 12.86 ± 1.15 lm 130.9 ± 22.9 lm2 39 12.84 ± 1.28 lm 130.6 ± 22.3 lm2
Quantitative Analysis of X-ray Lithographic Pores 329
123
Step 7: The centroid, diameter and cross-sectional area
were computed from the pixels comprising each
detected object.
Step 8: The computed areas were shown with the
average value and standard deviation.
The size analysis from this image processing procedure
was compared to that obtained by measuring the objects
one-by-one in Photoshop CS5. For the hand-drawn image
in Fig. 2a, the conversion from an RGB to a grayscale
image was needed prior to the Otsu’s thresholding but the
closing and removing small objects can be omitted.
3. Results and discussion
The image processing procedure is initially tested on the
hand-drawn image of periodic black circles on white
background (Fig. 2a). The output from each step is suc-
cessively shown in Fig. 3. Compared to the original image,
the white lines surrounding the circular areas on the black
background indicated that all drawn circles are successfully
detected by the Canny operator. After the areas are filled,
the objects in the image turn into white circles on the black
background. The computed size yield identical values for
all 66 circles as indicated by average diameter and area
without uncertainty in Table 1. These average diameter
and area are comparable to those obtained by the one-by-
one human inspection. The manual pixel counting by leads
to a small deviation in the area.
For the SEM micrograph in Fig. 2b, the result from the
file conversion from grayscale to binary shown in the first
picture in Fig. 4 contains broken contours and missing
pixels. The closing step with disk structuring elements of 3
pixels is therefore necessary before performing the edge
detection. Cross sections of all pores are then captured by
the Canny edge detector and expressed as white lines. The
fill area step improves the contrast between detected
objects and the background. Since some false detections
and noise are still present, the procedure for SEM micro-
graphs incorporates the removal of small objects of less
than 2,000 pixels. The incomplete pores near the edges of
Fig. 4 Outputs from each step of the proposed image processing procedure for the SEM image in Fig. 2b
330 U. Phromsuwan et al.
123
the image are also removed in the process. The diameter
and area averaged from detected pores are remarkably
close to those from the one-by-one human inspection.
Unlike the hand-drawn image, macropores in the SEM
micrographs are varied in size and shape. Still, the standard
deviations from the image processing and the one-by-one
human inspection in Table 1 are only slightly different.
For the micrograph of a cobalt coated sample in Fig. 2c,
the contrast between the pores and the background is
reduced because the cobalt film appears darker than SU-8
in Fig. 2b. In the analysis shown step-by-step in Fig. 5
shows, one pore is excluded since the fill area step due to
residual deposits in this pore. Although the contrast and the
roughness in the images are changed as a result of the
deposition, the computed diameter and area from the image
processing procedure are still close to those obtained from
the one-by-one human inspection.
The area fraction of pores is further decreased in
Fig. 2d. Furthermore, the films exhibit increased roughness
from grainy surface. The analysis shown in Fig. 6 omitted
only incomplete pores at the edges of micrographs.
Interestingly, the comparison between results from the
image processing and the one-by-one human inspection in
Table 1 yields the smallest difference in this case. This can
be attributed to the smallest pore size. The highest mor-
phological variation in Fig. 2d can still be quantified in
terms of the standard deviation using this image processing
procedure.
It is worth to discuss the measurement uncertainly of the
proposed image processing method, as observed from the
experimental result. The processing in Step 2 and Step 4
shown in Figs. 4, 5, 6 works based on the thresholding
technique, their performances therefore vary according to
the quality of the input images. For high contrast images
such as the computer-generated image in Fig. 3, the con-
version from grayscale to binary image provides a high
contrast between black objects and the white background
according to the global thresholding value obtained by
Otsu’s method. However, a real image in Fig. 5 exhibits a
reduction in contrast between each isolated pore and the
background. Although most areas can be detected by the
successive Canny edge detector in Step 4, the false
Fig. 5 Outputs from each step of the proposed image processing procedure for the SEM image in Fig. 2c
Quantitative Analysis of X-ray Lithographic Pores 331
123
detections occurred, especially for the pores having the
weak contrast area. This result underlines the incapability
of the image processing algorithm to differentiate the
blurring edge from the background. In this case, we
observed that the automatic selection of threshold value
(i.e., the parameter ‘‘Thresh = empty’’ in Step 4) for the
Canny edge detector introduced broken edges. The Canny
edge detector employs an expanded threshold set in the so-
called hysteresis thresholding operation, where a signifi-
cant edge is defined as a connected series of pixels with the
edge magnitude of at least one member exceeding an upper
threshold (t2), and with the magnitudes of the other mem-
bers exceeding a lower threshold (t1). In addition to the
automatic selection, we adjusted these parameters in our
experiment by setting the variable ‘‘Thresh = [t1 t2]’’ in
Step 4. It was observed that the lower values of t1 and t2reveal more details but, at the same time, give rise to more
false positive detections. On the other hand, higher values
of threshold lead to missed features.
It can be observed from the result of Step 5 in Fig. 5 that
the fill area operator failed to detect the particle with the
broken contours. Therefore, it is desirable to obtain a new
design for the algorithm to perform linking edges before
Step 5. The algorithm may scan for the broken contours
and repair the edge pixels. This linking edge algorithm may
improve the detection accuracy of the pores captured by the
low contrast SEM images.
Finally, based on the image data conducted in this study,
we observed that the proposed image processing method
shows the capability to perform on surfaces with different
electrical conductivity, roughness and pore fraction. With
its rapid and accurate performance, the procedure can be
applied to the more challenging cases including micro 3D
measurements [17].
4. Conclusions
SEM images of X-ray lithographic macropore arrays of
SU-8 matrix can quantitatively be analyzed using image
processing procedure based on the Canny edge detector in
Matlab. The thresholding, closing and filling steps were
Fig. 6 Outputs from each step of the proposed image processing procedure for the SEM image in Fig. 2d
332 U. Phromsuwan et al.
123
also needed to reduce noise and enhance the image. With
sufficient background contrast, the size distribution of
macropores in the SEM images is efficiently determined
and the results are comparable to the human inspection.
The capability in the case of different surface layers and
pore fractions is also demonstrated.
Acknowledgments This work was financially supported by the
Industry/University Cooperative Research Center (I/UCRC) in HDD
Component, the Faculty of Engineering, Khon Kaen University and
National Electronics and Computer Technology Center, National
Science and Technology Development Agency with the approval of
Seagate Technology (Thailand). The authors would like to thank C.
Sriphung for the assistance in the sample preparation.
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