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An improved adaptive genetic algorithm for image segmentation and vision alignment used in microelectronic bonding
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Wang, Fujun, Li, Junlan, Liu, Shiwei, Zhao, Xingyu, Zhang, Dawei and Tian, Yanling (2014) An improved adaptive genetic algorithm for image segmentation and vision alignment used in microelectronic bonding. IEEE/ASME Transactions on Mechatronics, 19 (3). pp. 916-923. doi:10.1109/TMECH.2013.2260555 ISSN 1083-4435.
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WRAP_1371255-es-290116-an_improved_adaptive_genetic_algorithm.pdf - Accepted Version - Requires a PDF viewer. Download (856Kb) | Preview |
Official URL: http://dx.doi.org/10.1109/TMECH.2013.2260555
Abstract
In order to improve the precision and efficiency of microelectronic bonding, this paper presents an improved adaptive genetic algorithm (IAGA) for the image segmentation and vision alignment of the solder joints in the microelectronic chips. The maximum between-cluster variance (OTSU) threshold segmentation method was adopted for the image segmentation of microchips, and the IAGA was introduced to the threshold segmentation considering the features of the images. The performance of the image segmentation was investigated by computational and experimental tests. The results show that the IAGA has faster convergence and better global optimality compared with standard genetic algorithm (SGA), and the quality of the segmented images becomes better by using the OTSU threshold segmentation method based on IAGA. On the basis of moment invariant approach, the microvision alignment was realized. Experiments were carried out to implement the microvision alignment of the solder joints in the microelectronic chips, and the results indicate that there are no alignment failures using the OTSU threshold segmentation method based on IAGA, which is superior to the OTSU method based on SGA in improving the precision and speed of the vision alignments.
Item Type: | Journal Article | ||||||
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Subjects: | T Technology > TA Engineering (General). Civil engineering (General) | ||||||
Divisions: | Faculty of Science, Engineering and Medicine > Engineering > Engineering | ||||||
Library of Congress Subject Headings (LCSH): | Image segmentation, Genetic algorithms, Integrated circuits, Convergence | ||||||
Journal or Publication Title: | IEEE/ASME Transactions on Mechatronics | ||||||
Publisher: | Institute of Electrical and Electronics Engineers | ||||||
ISSN: | 1083-4435 | ||||||
Official Date: | 20 June 2014 | ||||||
Dates: |
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Volume: | 19 | ||||||
Number: | 3 | ||||||
Number of Pages: | 8 | ||||||
Page Range: | pp. 916-923 | ||||||
DOI: | 10.1109/TMECH.2013.2260555 | ||||||
Status: | Peer Reviewed | ||||||
Publication Status: | Published | ||||||
Access rights to Published version: | Restricted or Subscription Access | ||||||
Date of first compliant deposit: | 4 February 2016 | ||||||
Date of first compliant Open Access: | 5 February 2016 | ||||||
Funder: | Guo jia zi ran ke xue ji jin wei yuan hui (China) [National Natural Science Foundation of China] (NSFC), Tianjin (China) | ||||||
Grant number: | 51205279 (NSFC), 51275337 (NSFC), 51175372 (NSFC), 13JCQNJC04100 (T(C)), 10ZCKFGX03200 (T(C)) |
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