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Automated X-ray recognition of solder bump defects based on ensemble-ELM

  • Lei SuEmail author
  • LingYu WangEmail author
  • Ke Li
  • JingJing Wu
  • GuangLan Liao
  • TieLin Shi
  • TingYu Lin
Article
  • 6 Downloads

Abstract

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.

automated recognition solder bump X-ray ensemble-ELM 

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Copyright information

© Science China Press and Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Jiangsu Key Laboratory of Advanced Food Manufacturing Equipment and TechnologyJiangnan UniversityWuxiChina
  2. 2.State Key Laborotary of Digital Manufacturing Equipment and TechnologyHuazhong University of Science and TechnologyWuhanChina
  3. 3.School of Mechanical EngineeringZhejiang UniversityHangzhouChina
  4. 4.National Center for Advanced PackagingWuxiChina

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