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: chitnarong.siri@gmail.com

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.

References

[1] S. Singh, M. Arora and R. Kishore, Estimation of measurement

uncertainty in characterization of materials using transmission

electron microscope (TEM) at NPL, India, MAPAN-J. Metrol.

Soc. India, 22 (2007) 263–268.

[2] N. Bonnet, Some trends in microscope image processing,

Micron, 35 (2004) 635–653.

[3] K.P. Chaudhary, C. Shakher and S.K. Singh, Measurement of

the size and spacing of standard wire sieves using an image

processing system and wavelet transform, MAPAN-J. Metrol.

Soc. India, 26 (2011) 15-27.

[4] K.S. Choudhari, P. Jidesh, P. Sudheendra and S.D. Kulkarni,

Quantification and morphology studies of nanoporous alumina

membranes: a new algorithm for digital image processing, Mi-

crosc. Microanal., 19 (2013) 1061–1072.

[5] E.A. Wargo, T. Kotaka, Y. Tabuchi and E.C. Kumbur, Com-

parison of focused ion beam versus nano-scale X-ray computed

tomography for resolving 3-D microstructures of porous fuel cell

materials, J. Power Sources, 241 (2013) 608–618.

[6] Y. Zhu, T. Zuo, L.J. Jiang, Z.T. Cai, C. Yan, X. Liu, X. Malcolm

and Q.L. Wu, Pore structure analysis on activated carbon fibers-

By cluster and watershed transform method, Appl. Surf. Sci.,

256 (2009) 587–592.

[7] V. Guarino, A. Guaccio, P.A. Netti and L. Ambrosio, Image

processing and fractal box counting: user-assisted method for

multi-scale porous scaffold characterization, J. Mater. Sci.

Mater. Med., 21 (2010) 3109–3118.

[8] T.R. Wojcik and D. Krapf, Solid-state nanopore recognition and

measurement using Shannon entropy, IEEE Phot. J., 3 (2011)

337–343.

[9] B. Chen, Z.B. Xia and K. Lu, Understanding sintering charac-

teristics of ZnO nanoparticles by FIB-SEM three-dimensional

analysis, J. Eur. Ceram. Soc., 33 (2013) 2499–2507.

[10] N.M. Ahmed, A. Ramizy, Z. Hassan, A. Amer, K. Omar, Y. Al-

Douri and O.S. Alattas, Nano and microporous GaN character-

ization using image processing method, Optik, 123 (2012)

1074–1078.

[11] J. Klaver, G. Desbois, J.L. Urai and R. Littke, BIB-SEM study of

the pore space morphology in early mature Posidonia Shale from

the Hils area, Germany, Int. J. Coal Geol., 103 (2012) 12–25.

[12] M.S. Jouini, S. Vega and E.A. Mokhtar, Multiscale character-

ization of pore spaces using multifractals analysis of scanning

electronic microscopy images of carbonates, Nonlinear Proc.

Geoph., 18 (2011) 941–953.

[13] H.S. Wong, M.K. Head and N.R. Buenfeld, Pore segmentation

of cement-based materials from backscattered electron images,

Cem. Concr. Res., 36 (2006) 1083–1090.

[14] G.H. Woehrle, J.E. Hutchison, S. Ozkar and R.G. Finke, Ana-

lysis of nanoparticle transmission electron microscopy data

using a public-domain image processing program, Image, Turk.

J. Chem., 30 (2006) 1–13.

[15] A. Tirta, E.R. Baek, S.S. Chang and J.H. Kim, Fabrication of porous

material for micro component application by direct X-ray lithogra-

phy and sintering, Microelectron. Eng., 98 (2012) 297–300.

[16] U. Phromsuwan, C. Sirisathitkul, Y. Sirisathitkul and C. Srip-

hung, Implementation of edge detection algorithms to charac-

terize magnetic micropillars patterned by X-ray lithography, Int.

J. Phys. Sci., 7 (2012) 1959–1963.

[17] Y. Arai, M. Ando, S. Kanameishi and S. Yokozeki, Micro 3D

measurement method using SEM, MAPAN-J. Metrol. Soc.

India, 26 (2011) 69–78.

Quantitative Analysis of X-ray Lithographic Pores 333

123