摘要
Solder bumps realize the mechanical and electrical interconnection between chips and substrates in surface mount components,such as flip chip, wafer level packaging and three-dimensional integration. With the trend to smaller and lighter electronics,solder bumps decrease in dimension and pitch in order to achieve higher I/O density. Automated and nondestructive defect inspection of solder bumps becomes more difficult. Machine learning is a way to recognize the solder bump defects online and overcome the effect caused by the human eye-fatigue. In this paper, we proposed an automated and nondestructive X-ray recognition method for defect inspection of solder bumps. The X-ray system captured the images of the samples and the solder bump images were segmented from the sample images. Seven features including four geometric features, one texture feature and two frequency-domain features were extracted. The ensemble-ELM was established to recognize the defects intelligently. The results demonstrated the high recognition rate compared to the single-ELM. Therefore, this method has high potentiality for automated X-ray recognition of solder bump defects online and reliable.
Solder bumps realize the mechanical and electrical interconnection between chips and substrates in surface mount components,such as flip chip, wafer level packaging and three-dimensional integration. With the trend to smaller and lighter electronics,solder bumps decrease in dimension and pitch in order to achieve higher I/O density. Automated and nondestructive defect inspection of solder bumps becomes more difficult. Machine learning is a way to recognize the solder bump defects online and overcome the effect caused by the human eye-fatigue. In this paper, we proposed an automated and nondestructive X-ray recognition method for defect inspection of solder bumps. The X-ray system captured the images of the samples and the solder bump images were segmented from the sample images. Seven features including four geometric features, one texture feature and two frequency-domain features were extracted. The ensemble-ELM was established to recognize the defects intelligently. The results demonstrated the high recognition rate compared to the single-ELM. Therefore, this method has high potentiality for automated X-ray recognition of solder bump defects online and reliable.
基金
supported by the National Key Basic Research Special Fund of China(Grant No.2015CB057205)
the National Natural Science Foundation of China(Grant Nos.51705203,51775243,51675250)
the Natural Science Foundation of Jiangsu Province(Grant Nos.BK20160183,BK20160185)
the project funded by China Postdoctoral Science Foundation(Grant No.2017M611690)
“111” Project(Grant No.B18027)
the Open Foundation of State Key Lab of Digital Manufacturing Equipment Technology(Grant No.DMETKF2018022)
