SUGISAKAMASANORI

Journal of Advances in Artificial Life Robotics
Vol. 1(2); September (2020), pp. 91–95
ON LINE ISSN 2435-8061; ISSN-L 2435-8061
https://alife-robotics.org/jallr.html

Research Article

Research on Online Detection of Wafer Surface Defects
Chundong Zhao1, Xiaoyan Chen1, Dongyang Zhang1, Jianyong Chen1, Kuifeng Zhu2, Yanjie Su2
College of Electronic Information and Automation, Tianjin University of Science and Technology, Tianjin, China
Tianjin Fly Tech Co., Ltd, Tianjin, China

12ARTICLE INFO
Article History
Received 10 November 2019
Accepted 17 July 2020
Keywords
Machine vision
Flood fill
Defect detection

ABSTRACTWith the development of integrated electronic circuit manufacturing technology, enterprises have
put forward higher requirements for the quality of silicon chips. Aiming at the low efficiency ofsilicon wafer defect detection, this paper proposes an automatic defect detection method based onmachine vision. The voiding algorithm based on flood fill can effectively extract the inner contour
information of the wafer profile. A rotation correction algorithm is proposed to correct the wafer
yaw angle. The actual wafer was used to verify the performance of the proposed method. The
results show that the proposed method is effective in detection accuracy.

© 2022 The Author. Published by Sugisaka Masanori at ALife Robotics Corporation Ltd.
This is an open access article distributed under the CC BY-NC 4.0 license(http://creativecommons.org/licenses/by-nc/4.0/).

Rotation correction

1. Introduction
Wafer manufacturing is the core process of thesemiconductor chip industry. The complexity ofsemiconductor silicon wafer processing technology has ledto more and more sources of defects [10], which puts
higher requirements on existing detection technologies.
The defect detection is done manually under themicroscope, which is affected by visual fatigue and humansubjective factors. The detection accuracy cannot be
guaranteed, and the artificial vision inspection cannot meetthe requirements of large-scale production andmanufacturing.
The machine vision detection method can largely
overcome the shortcomings of the manual detection
method such as low accuracy, poor real-time performance,
low efficiency, and high labor intensity, and has been
widely studied and applied in industrial testing [1]. Liapplies machine vision to the detection of surface defects
in integrated circuit wafers, and uses fuzzy logic to analyze
different shapes of surface pit defects [2]. Barni based onmachine vision system for quality testing of chicken.
According to the color information of the chicken image,the mathematical problem is used to extract the features ofthe potential problem area, and then classified according
to the predefined list of quality problems [3]. Hoang
introduced a method for detecting surface defects inleather, using OTSU method for defect segmentation [4].
With the development of computer technology and image
processing technology, computer vision detection
technology has been widely studied and applied in various
fields. Aiming at surface defects such as dirt and particles
generated during the manufacturing process of siliconwafers, this paper makes research on the technology ofsilicon wafer surface defect detection based on machine
vision. The rotation algorithm based on the cumulative
Hough linear transformation can correct the declination of
the wafer.
2. Image acquisition device
The image acquisition device consists of CMOS
industrial camera, lens, ring light source, and light sourcecontroller. The structure diagram of the image acquisition
device is shown in Fig. 1.

Corresponding author’s E-mail: cxywxr@tust.edu.cn, URL: www.tust.edu.cn

913.1 Hole filling algorithm
Hough transform requires detection of white straight lines.
Hole extraction can turn black grid lines into white andwhite grains into black. Hole extraction refers to extracting
information about the contour of a completely closed
contour and filling it with color. The white grid of theimage divided by the threshold is used as the outline, andthe information of its inner circumference is exacted.

Fig. 1. Structure diagram of the image acquisition device.
(a) is the industrial camera. (b) is the lens. (c) is the ringlight source. (d) is the silicon wafer.

3.1.1 Threshold processing
Threshold processing can be regarded as a statisticaldecision problem, the purpose of which is to minimize theaverage error introduced in the process of assigning to twoor more classes [9],[11]. The image has other interference
information besides the wafer. Threshold processing of theacquired image can filter out the interference information
to obtain the clearest wafer information. In this paper, themaximum inter-class variance method is used to thresholdthe wafer, which has a good adaptive ability. But thealgorithm is more sensitive to noise. Therefore, in thispaper, median filtering is used to remove the noiseinterference in the image before threshold segmentation.

This device uses a MD-UB1000 CMOS industrial camera.
The industrial camera does not need an additional image
acquisition card during the image acquisition process, andthe camera and the computer can use a professional camera
connection cable for data transmission. The camera imager
transmits data in parallel at the front end during datatransmission, and the bandwidth of the amplifier is very
low. The main parameters of the camera include: the pixel
size is 1.67um; the effective pixels are 10 million; theresolution ratio is 3664 × 2748; the pixel bit depth is 12bits. The lens is an MV-JT08 zoom lens. The main
parameters include: focal length of 8mm, distortion of lessthan 1%, closest distance of 0.1m. The ring light source isa FJI-RL150-A00-W ring light source with a diameter of15cm. The light is radiated vertically from differentdirections around it. Compared with parallel light, a better
quality image can be obtained.

3.1.2 Flood fill
Flood filling is a method of filling connected areas with aspecific color [5],[6]. By setting the upper and lower limitsof connectable pixels and the connection method, different
filling effects are achieved. In order to get the white lines
needed for Hough detection, we set the gray value of thefill color to 255. The comparison of the wafer before andafter the hole extraction is shown in Fig. 3.

3. Wafer image correction algorithm
There is a deviation in the process of manually placing thewafer, so that the vertical and horizontal intersection linesof the wafer cannot completely match the actual horizontaland vertical directions. In order to accurately locate thegrain position coordinates, the tilt angle must be calculated
by a certain algorithm, and the wafer is rotated andcorrected according to the tilt angle. Rotation algorithm
include graying, thresholding, hole extraction, Houghtransform of cumulative probability, and calculation ofrotation angle. The flowchart of the algorithm is shown inFig. 2.

(a)

(b)

Fig. 3. The comparison of the wafer before and after thehole filling. (a) is threshold image. (b) is hole extraction
image
3.2 Hough transform to detect straight lines
Hough transform is a feature extraction technology inimage processing [7],[8], which is widely used in image
analysis and computer vision. In the parameter space, aHough result is obtained by calculating a local maximum

Fig. 2. The flowchart of the algorithm
92

4. Extract grainsof the accumulated results to obtain a set that conforms tothe specific shape. Huff transform uses a transformation
between two coordinate systems to map a curve or linewith the same shape in one space to a point in anothercoordinate space to form a peak, thereby transforming theproblem of detecting any shape into a statistical peak
problem. By performing a Hough transform on the wafer
image after the hole filling, the linear distribution of thewafer can be detected.

Before detecting the physical defects of the grains, thegrains needs to be extracted from the wafer. The contourof the corrected wafer is extracted and the contour isfiltered according to the contour area. If the area is too
small, the contour is regarded as noise. According to theprofile information, the grain is cut and extracted. The
grains are shown in Fig. 5.

3.3 Angle correction
The coordinates of the two ends of the line segment
detected by the Hough transform are subtracted to obtainthe size v(x1 − x2, y1 − y2) of the line segment vector,
and the coordinate components v.x and v.y of the vector
on the X and Y axes are determined according to the vector
size.
Let angle be the positive angle of the vector v and the Xaxis. Then the angle value should satisfy formula (2).
𝑣𝑣.𝑦𝑦(2)angle = arctan( )Fig. 5 Extracted grains
5. Defect detection
In this paper, a progressive s-shape is used to traverse eachgrain. The grain defect is composed of a plurality of black
pixels in the threshold map. Hole extraction algorithm isperformed for each grain to calculate the number of nonzero pixels in the image. The number of pixels in the grain
is multiplied by a set threshold, and the number of nonzero pixel points in the grain is more than the number ofpixels in the rectangular element, multiplied by the set
threshold, the grain is identified as defective.
In this experiment, a total of 1243 grains and 94 defective
grains were found in six randomly selected test areas. The
total amount of grains and the number of defective grains
are different in each test area. The data are shown in Table
1.
Table 1 Statistical results of machine vision
detection

𝑣𝑣.𝑥𝑥

The included angle between each line segment and thepositive direction of the X axis is not exactly the same, so
all the angles need to be further carefully divided.
Hierarchical clustering is used to divide a set of data into
several categories. After sorting, the angle groupcontaining the largest number of angles is obtained, andthe average value of all angles in the array is taken as thefinal corrected angle.
The affine transformation can realize the rotation
correction of the wafer image. Affine transformation refers
to the process in which a vector space is transformed intoanother vector space by completing a linear transformationin geometric space. The affine transformation can maintainthe "flatness" and "parallelism" of a two-dimensional
image. The comparison before and after correction isshown in Fig. 4.

(a)

Area
1234
5
6Total(b)Fig. 4. The comparison before and after correction. (a) is
before correction. (b) is after correction.

93

Actual defect
15
1119
20
18
1194

Detected defects
15
1018
19
16
1189

Accuracy
100％
90.9％
94.7％
95.0％
88.9％
100％
94.7％

6. Conclusion

8.

With the rapid development of the semiconductor industry,
it is of great significance to apply machine vision
technology to detect wafer surface defects. This paper
designs an image rotation correction and adaptive gridreconstruction algorithm based on the VS platform and theopencv vision library. The image rotation correction caneliminate the deviation generated in the film placement
process and facilitate the positioning of the grain position.
In this paper, the online detection of defective grains isrealized, but there are still some shortcomings. Imageacquisition is susceptible to interference from the external
environment, and the algorithm does not work well forimages with uneven light reception.

9.

10.

11.

Authors Introduction
Dr. Xiaoyan Chen
She received the M.S. degree from theTianjin University of Science andTechnology, in 1999, and the Ph.D. degree inmeasurement technology and automation
devices from Tianjin University, in 2009.
From 2009 to 2015, she held a Post-doctoral
position at Tianjin University. She wasinvited by RPI, USA, as a Visiting Scholar, from 2009 to 2010,and Kent, U.K., in 2012. She is currently a Professor and an
Advisor of Postgraduate and Doctorate students with theTianjin University of Science and Technology

Acknowledgements
The authors would like to appreciate for the financial
support from Tianjin Municipal Science and Technology
Bureau (Grant NO.18YFZCGX00360) and from theNational Natural Science Foundation of China (GrantNO.61903274 and NO.41704131), the same appreciate for
Tianjin FLY Tech Co., Ltd for offering samples and forIntelligent Edge Computing Joint LAB of TUST for thetesting result analyzing.

Mr. Chundong Zhao
He received the B.S. degree in automationfrom Tianjin University of Science andTechnology, China, in 2018. He is currently
pursuing the M.S. degree in control scienceand engineering with the Department ofElectronic Information and Automation,
Tianjin University of Science andTechnology, China, under the supervision of Dr. Chen. His
current research interests include digital image processing,pattern recognition and machine learning.

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Mr. Jianyong Che

He received the B.S. degree in automationfrom Tianjin University of Science andTechnology, China, in 2018. He is currently
pursuing the M.S. degree in control scienceand engineering with the Department ofElectronic Information and Automation,
Tianjin University of Science andTechnology, China, under the supervisionof Dr. Chen. His current research interests include digital
image processing, pattern recognition and machine learning.

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