Adaptive Inspection for Void Defects Inside Solder Joints of Chip Resistors

CAI Nian; XIAO Meng; XIAO Pan; ZHOU Shuai; QIU Baojun; WANG Han

doi:10.11999/JEIT211246

Volume 44 Issue 5

May 2022

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Article Navigation > Journal of Electronics & Information Technology > 2022 > 44(5): 1617-1624

CAI Nian, XIAO Meng, XIAO Pan, ZHOU Shuai, QIU Baojun, WANG Han. Adaptive Inspection for Void Defects Inside Solder Joints of Chip Resistors[J]. Journal of Electronics & Information Technology, 2022, 44(5): 1617-1624. doi: 10.11999/JEIT211246

Citation:

CAI Nian, XIAO Meng, XIAO Pan, ZHOU Shuai, QIU Baojun, WANG Han. Adaptive Inspection for Void Defects Inside Solder Joints of Chip Resistors[J]. Journal of Electronics & Information Technology, 2022, 44(5): 1617-1624. doi: 10.11999/JEIT211246

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Adaptive Inspection for Void Defects Inside Solder Joints of Chip Resistors

doi: 10.11999/JEIT211246

CAI Nian^{1, 5
,
,},
XIAO Meng¹,
XIAO Pan⁴,
ZHOU Shuai^{2, 3},
QIU Baojun³,
WANG Han⁴

1.
School of Information Engineering, Guangdong University of Technology, Guangzhou 510006, China
2.
Tianjin University, Tianjin 300072, China
3.
China Electronic Product Reliability and Environmental Testing Research Institute, Guangzhou 510006, China
4.
School of Electromechanical Engineering, Guangdong University of Technology, Guangzhou 510006, China
5.
Huizhou Guangdong University of Technology IoT Cooperative Innovation Institute Co., Ltd., Huizhou 516025, China

Funds: The National Natural Science Foundation of China (62171142), The Natural Science Foundation of Guangdong Province (2021A1515011908), The Research Fund for Colleges and Universities in Huizhou (2019HZKY003)

Received Date: 2021-11-10
Accepted Date: 2022-01-12
Rev Recd Date: 2022-01-09

Available Online: 2022-02-01

Publish Date: 2022-05-25

Abstract

Abstract

In the process of reflow soldering, void defects inevitably emerge inside solder joints of chip resistors, which will influence reliability of the device. In this paper, an adaptive inspection method for void defects inside solder joints of chip resistors is proposed by combining a Local Pre-Fitted (LPF) active contour model and circular convolutions with adaptive kernels. First, since the image of chip resistor has two distinct regions, dark and bright regions are adaptively separated from the image after solving the optimization problem with the largest difference between the average gray level values of the two regions. Then, considering low contrast between voids and the image background, sparse distribution and large areas of voids in the dark region, LPF active contour model is used to inspect voids. As for the obvious difference between voids and the image background, dense distribution and small areas of voids in the bright region, circular convolutions with adaptive kernels are proposed to inspect voids. Finally, false detection can be eliminated by the shape factor and an average gray strategy to realize accurate void inspection. Experimental results show that the proposed method is superior to other inspection methods with an average Dice coefficient of 0.8846.
- Void detection,
- Chip resistor,
- Local Pre-Fitted (LPF) model,
- Circular convolution with adaptive kernel,
- Average-gray strategy

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References(20)

References

[1]	ILLÉS B, KRAMMER O, and GÉCZY A. Reflow Soldering: Apparatus and Heat Transfer Processes[M]. Amsterdam: Elsevier, 2020: 5–8.
[2]	SAID A F, BENNETT B L, KARAM L J, et al. Automated void detection in solder balls in the presence of vias and other artifacts[J]. IEEE Transactions on Components, Packaging and Manufacturing Technology, 2012, 2(11): 1890–1901. doi: 10.1109/TCPMT.2011.2182613
[3]	WILD P, LORENZ D, GRÖZINGER T, et al. Effect of voids on thermo-mechanical reliability of chip resistor solder joints: Experiment, modelling and simulation[J]. Microelectronics Reliability, 2018, 85: 163–175. doi: 10.1016/j.microrel.2018.04.014
[4]	BUŠEK D, DUŠEK K, RŮŽIČKA D, et al. Flux effect on void quantity and size in soldered joints[J]. Microelectronics Reliability, 2016, 60: 135–140. doi: 10.1016/j.microrel.2016.03.009
[5]	WANG Yu, WANG Mingquan, and ZHANG Zhijie. Microfocus X-ray printed circuit board inspection system[J]. Optik, 2014, 125(17): 4929–4931. doi: 10.1016/j.ijleo.2014.04.027
[6]	PENG Shaohu and DO NAM H. Void defect detection in ball grid array X-ray images using a new blob filter[J]. Journal of Zhejiang University SCIENCE C, 2012, 13(11): 840–849. doi: 10.1631/jzus.C1200065
[7]	MOURI M, KATO Y, YASUKAWA H, et al. A study of using nonnegative matrix factorization to detect solder-voids from radiographic images of solder[C]. The 2014 IEEE 23rd International Symposium on Industrial Electronics, Istanbul, Turkey, 2014: 1074–1079.
[8]	NUANPRASERT S, BABA S, and SUZUKI T. A simple automated void defect detection for poor contrast x-ray images of BGA[C]. The 3rd International Conference on Industrial Application Engineering, Kitakyushu, Japan, 2015.
[9]	MOORE T D, VANDERSTRAETEN D, and FORSSELL P M. Three-dimensional X-ray laminography as a tool for detection and characterization of BGA package defects[J]. IEEE Transactions on Components and Packaging Technologies, 2002, 25(2): 224–229. doi: 10.1109/TCAPT.2002.1010010
[10]	NEELURU V K and AHUJA V. Void region segmentation in ball grid array using u-net approach and synthetic data[J]. arXiv: 1907.04222, 2019.
[11]	AKDENİZ C T, DOKUR Z, and ÖLMEZ T. Detection of BGA solder defects from X-ray images using deep neural network[J]. Turkish Journal of Electrical Engineering & Computer Sciences, 2020, 28(4): 2020–2029. doi: 10.3906/elk-1910-135
[12]	DING Keyan, XIAO Linfang, and WENG Guirong. Active contours driven by local pre-fitting energy for fast image segmentation[J]. Pattern Recognition Letters, 2018, 104: 29–36. doi: 10.1016/j.patrec.2018.01.019
[13]	HE Kaiming and SUN Jian. Fast guided filter[J]. arXiv: 1505.00996, 2015.
[14]	LI Chunming, XU Chenyang, GUI Changfeng, et al. Distance regularized level set evolution and its application to image segmentation[J]. IEEE Transactions on Image Processing, 2010, 19(12): 3243–3254. doi: 10.1109/TIP.2010.2069690
[15]	DING Keyan, XIAO Linfang, and WENG Guirong. Active contours driven by region-scalable fitting and optimized Laplacian of Gaussian energy for image segmentation[J]. Signal Processing, 2017, 134: 224–233. doi: 10.1016/j.sigpro.2016.12.021
[16]	GAO Shangbing, YANG Jian, and YAN Yunyang. A novel multiphase active contour model for inhomogeneous image segmentation[J]. Multimedia Tools and Applications, 2014, 72(3): 2321–2337. doi: 10.1007/s11042-013-1553-2
[17]	WANG Lei, CHANG Yan, WANG Hui, et al. An active contour model based on local fitted images for image segmentation[J]. Information Sciences, 2017, 418/419: 61–73. doi: 10.1016/j.ins.2017.06.042
[18]	ZHAO Wencheng, XU Xianze, ZHU Yanyan, et al. Active contour model based on local and global Gaussian fitting energy for medical image segmentation[J]. Optik, 2018, 158: 1160–1169. doi: 10.1016/j.ijleo.2018.01.004
[19]	罗钧, 杨永松, 侍宝玉. 基于改进的自适应差分演化算法的二维Otsu多阈值图像分割[J]. 电子与信息学报, 2019, 41(8): 2017–2024. doi: 10.11999/JEIT180949 LUO Jun, YANG Yongsong, and SHI Baoyu. Multi-threshold image segmentation of 2D Otsu based on improved adaptive differential evolution algorithm[J]. Journal of Electronics &Information Technology, 2019, 41(8): 2017–2024. doi: 10.11999/JEIT180949
[20]	SHANG Caijie, ZHANG Dong, and YANG Yan. A gradient-based method for multilevel thresholding[J]. Expert Systems with Applications, 2021, 175: 114845. doi: 10.1016/j.eswa.2021.114845