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Defect inspection of flip chip package using SAM technology and fuzzy C-means algorithm

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Abstract

Solder bumps are widely used in surface mount components, which provide electrical and mechanical connection between the chip/package and the substrate. As the solder bump getting smaller in dimension and pitch, it becomes more difficult to inspect the solder defects hidden in the IC package. In this paper, an intelligent inspection method using the scanning acoustic microscopy (SAM) and the fuzzy C-means (FCM) algorithm was investigated. A flip chip package of FA10 was chosen as the test sample. The SAM tests of FA10 were carried out in C-scan mode. The sub-image of every solder bump was segmented from the SAM image. The statistical features were then calculated and adopted for clustering of solder bumps using the FCM algorithm. The recognition results of FCM reached a high accuracy of 94.3%. The intelligent system is effective for defect inspection in high density packages.

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References

  1. Chen J K, Xu Z L, Huang Y A, et al. Analytical investigation on thermal-induced warpage behavior of ultrathin chip-on-flex (UTCOF) assembly. Sci China Tech Sci, 2016, 59: 1646–1655

    Article  Google Scholar 

  2. Lu X, Shi T, Xia Q, et al. Thermal conduction analysis and characterization of solder bumps in flip chip package. Appl Thermal Eng, 2012, 36: 181–187

    Article  Google Scholar 

  3. Asgari R. Challenges in 3D inspection of micro bumps used in 3D packaging. In: Proceedings of 45th International Symposium on Microelectronics. San Diego, 2012. 542–547

    Google Scholar 

  4. Liao G, Chen P, Du L, et al. Using SOM neural network for X-ray inspection of missing-bump defects in three-dimensional integration. Microelectron Reliab, 2015, 55: 2826–2832

    Article  Google Scholar 

  5. Shen J, Chen P, Su L, et al. X-ray inspection of TSV defects with selforganizing map network and Otsu algorithm. Microelectron Reliab, 2016, 67: 129–134

    Article  Google Scholar 

  6. Ahi K, Asadizanjani N, Shahbazmohamadi S, et al. Terahertz characterization of electronic components and comparison of terahertz imaging with X-ray imaging techniques. In: Proceedings Volume 9483, Terahertz Physics, Devices, and Systems IX: Advanced Applications in Industry and Defense. Baltimore, 2015

    Google Scholar 

  7. He Z, Wei L, Shao M, et al. Detection of micro solder balls using active thermography and probabilistic neural network. Infrared Phys Tech, 2017, 81: 236–241

    Article  Google Scholar 

  8. Wei W, Wei L, Nie L, et al. Using active thermography and modified SVM for intelligent diagnosis of solder bumps. Infrared Phys Tech, 2015, 72: 163–169

    Article  Google Scholar 

  9. Lu X, Shi T, Han J, et al. Defects inspection of the solder bumps using self reference technology in active thermography. Infrared Phys Tech, 2014, 63: 97–102

    Article  Google Scholar 

  10. Su L, Shi T, Liu Z, et al. Nondestructive diagnosis of flip chips based on vibration analysis using PCA-RBF. Mech Syst Signal Process, 2017, 85: 849–856

    Article  Google Scholar 

  11. Liao G, Du L, Su L, et al. Using RBF networks for detection and prediction of flip chip with missing bumps. Microelectron Reliab, 2015, 55: 2817–2825

    Article  Google Scholar 

  12. Su L, Shi T, Du L, et al. Genetic algorithms for defect detection of flip chips. Microelectron Reliab, 2015, 55: 213–220

    Article  Google Scholar 

  13. Brand S, Czurratis P, Hoffrogge P, et al. Automated inspection and classification of flip-chip-contacts using scanning acoustic microscopy. Microelectron Reliab, 2010, 50: 1469–1473

    Article  Google Scholar 

  14. Su L, Zha Z, Lu X, et al. Using BP network for ultrasonic inspection of flip chip solder joints. Mech Syst Signal Process, 2013, 34: 183–190

    Article  Google Scholar 

  15. Yang R S H, Braden D R, Zhang G M, et al. An automated ultrasonic inspection approach for flip chip solder joint assessment. Microelectron Reliab, 2012, 52: 2995–3001

    Article  Google Scholar 

  16. Fan M, Wei L, He Z, et al. Defect inspection of solder bumps using the scanning acoustic microscopy and fuzzy SVM algorithm. Microelectron Reliab, 2016, 65: 192–197

    Article  Google Scholar 

  17. Liu F, Su L, Fan M, et al. Using scanning acoustic microscopy and LM-BP algorithm for defect inspection of micro solder bumps. Microelectron Reliab, 2017, 79: 166–174

    Article  Google Scholar 

  18. Lu X N, Shi T L, Wang S Y, et al. Intelligent diagnosis of the solder bumps defects using fuzzy C-means algorithm with the weighted coefficients. Sci China Tech Sci, 2015, 58: 1689–1695

    Article  Google Scholar 

  19. Izakian H, Abraham A. Fuzzy C-means and fuzzy swarm for fuzzy clustering problem. Expert Syst Appl, 2011, 38: 1835–1838

    Article  Google Scholar 

  20. Gong M, Liang Y, Shi J, et al. Fuzzy C-means clustering with local information and kernel metric for image segmentation. IEEE Trans Image Process, 2013, 22: 573–584

    Article  MathSciNet  MATH  Google Scholar 

  21. Chuang K S, Tzeng H L, Chen S, et al. Fuzzy C-means clustering with spatial information for image segmentation. Compized Med Imag Graphics, 2006, 30: 9–15

    Article  Google Scholar 

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Authors and Affiliations

  1. School of Mechanical & Electrical Engineering, Jiangsu Normal University, Xuzhou, 221116, China

    XiangNing Lu, Fan Liu, ZhenZhi He & NingNing Hu

  2. School of Materials Science and Engineering, Georgia Institute of Technology, Atlanta, 30332, USA

    XiangNing Lu & LiYi Li

  3. School of Mechanical Engineering, Jiangnan University, Wuxi, 214122, China

    Lei Su

Authors
  1. XiangNing Lu
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  2. Fan Liu
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  3. ZhenZhi He
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  4. LiYi Li
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  5. NingNing Hu
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  6. Lei Su
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Correspondence to Lei Su.

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Lu, X., Liu, F., He, Z. et al. Defect inspection of flip chip package using SAM technology and fuzzy C-means algorithm. Sci. China Technol. Sci. 61, 1426–1430 (2018). https://doi.org/10.1007/s11431-017-9185-6

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  • DOI: https://doi.org/10.1007/s11431-017-9185-6

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