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Multi-scale Coefficients Fusion Strategy for Enhancement of SAM Image in Solder Joints Detection

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Abstract

Defect inspection of IC devices is getting more challenging with the increase of package density. Scanning acoustic microscopy (SAM) is widely used in electronic industry. The detection resolution is, however, limited by the penetration depth of ultrasound. It is necessary to find a way to improve the resolution and accuracy. A new strategy of multi-scale decomposition and fusion based on the wavelet transform was proposed to enhance the image resolution in SAM detection. The original SAM image was subjected to wavelet decomposition at different scales. Two recombined images A and B were decomposed into low frequency band (cAd1 and cAd2) and high frequency bands (cHd1, cVd1, cDd1, and cHd2, cVd2, cDd2), which were then merged respectively based on the local area energy. A high resolution SAM image was reconstructed by using the new coefficients. Back propagation network modified with genetic algorithm (GA-BP) was utilized to classify the solder joints. The proposed scheme achieved highest recognition accuracy (97.16%) compared with other methods. The new strategy provides an effective way to enhance the image quality and recognition accuracy in SAM detection of micro defect.

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Data Availability

Most of the data have been included in this paper. All the data and materials are available at request.

References

  1. Lau, J.: Recent advances and new trends in flip chip technology. J. Electron. Packag. 138(3), 030802 (2016)

    Article  Google Scholar 

  2. Du, L., Shi, T., Chen, P., et al.: Optimization of through silicon via for three-dimensional integration. Microelectron. Eng. 139, 31–38 (2015)

    Article  CAS  Google Scholar 

  3. Sha, Y.H., He, Z.Z., Du, J.W., et al.: Intelligent detection technology of flip chip based on H-SVM algorithm. Eng. Fail. Anal. 134, 106032 (2022)

    Article  Google Scholar 

  4. Tian, Y., Ren, N., Jian, X., et al.: Interfacial compounds characteristic and its reliability effects on SAC305 microjoints in flip chip assemblies. J. Electron. Packag. 140(3), 031007 (2018)

    Article  Google Scholar 

  5. Li, J., Tian, W., Liao, H., et al.: The mathematical model and novel final test system for wafer-level packaging. IEEE Trans. Ind. Inform. 13(4), 1817–1824 (2016)

    Article  Google Scholar 

  6. Lu, X., Liu, F., He, Z., et al.: Defect inspection of flip chip package using SAM technology and fuzzy C-means algorithm. Sci. China Techol. Sci. 61(9), 1426–1430 (2018)

    Article  Google Scholar 

  7. Wang, W., Lu, X.N., He, Z.Z., et al.: Using convolutional neural network for intelligent SAM inspection of flip chips. Measure. Sci Technol. 32(11), 115022 (2021)

    Article  ADS  CAS  Google Scholar 

  8. Wang, Z., Liu, X., He, Z., et al.: Intelligent detection of flip chip with the scanning acoustic microscopy and the general regression neural network. Microelectron Eng. 217, 111127 (2019)

    Article  CAS  Google Scholar 

  9. Zhao, L., He, Z., Wang, Z., et al.: Simulation and experimental investigation on active thermography test of the solder balls. IEEE Trans. Ind. Inform. 16(3), 1617–1624 (2020)

    Article  CAS  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 Pr. 85, 849–856 (2017)

    Article  Google Scholar 

  11. Sakai, K., Kikuchi, O., Kitami, K., et al.: Defect detection method using statistical image processing of scanning acoustic tomography, International symposium on the physical and failure analysis of integrated circuits (IPFA). IEEE, pp. 293–296 (2016)

  12. Lu, X., He, Z., Su, L., et al.: Detection of Micro Solder balls using active thermography technology and K-means algorithm. IEEE Trans. Ind. Inform. 14(12), 5620–5628 (2018)

    Article  ADS  Google Scholar 

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

    Article  ADS  CAS  Google Scholar 

  14. Dziurdzia, B., Mikolajek, J.: X-ray inspection and six-sigma in analysis of LED thermal pad coverage. Solder Surf. Mt. Tech. 29(1), 28–33 (2017)

    Article  Google Scholar 

  15. Su, L., Wang, L., Li, K., et al.: Automated X-ray recognition of solder bump defects based on ensemble-ELM. Sci. China Techol. Sci. 62(9), 1512–1519 (2019)

    Article  Google Scholar 

  16. Li, J., Zhang, H., Zhou, C., et al.: A multi-parameter numerical modeling and simulation of the dipping process in microelectronics packaging. IEEE Trans. Ind. Inform. 15(7), 3808–3820 (2018)

    Article  Google Scholar 

  17. Li, K., Wang, L., Wu, J., et al.: Using GA-SVM for defect inspection of flip chips based on vibration signals. Microelectron Reliab. 81, 159–166 (2018)

    Article  Google Scholar 

  18. Wang, X., Zhang, G., Ma, H., et al.: Measurement of a 3D ultrasonic wavefield using pulsed laser holographic microscopy for ultrasonic nondestructive evaluation. Sensors 18(2), 573 (2018)

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  19. Su, L., Zha, Z., Lu, X., et al.: Using BP network for ultrasonic inspection of flip chip solder joints. Mech. Syst. Signal Pr. 34(1–2), 183–190 (2013)

    Article  Google Scholar 

  20. 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 79, 166–174 (2017)

    Article  CAS  Google Scholar 

  21. Su, L., Yu, X., Li, K., et al.: Simulation and experimental verification of edge blurring phenomenon in microdefect inspection based on high-frequency ultrasound, IEEE Access, pp. 11515–11525 (2019).

  22. Hainsworth, A., Lee, S., Foot, P., et al.: Super-resolution imaging of subcortical white matter using stochastic optical reconstruction microscopy (STORM) and super‐resolution optical fluctuation imaging (SOFI). Neuropath Appl. Neuro. 44(4), 417–426 (2018)

    Article  CAS  Google Scholar 

  23. Lai, W., Huang, J., Ahuja, N., et al.: Fast and accurate image super-resolution with deep laplacian pyramid networks. IEEE Trans. Pattern Anal. (2018). https://doi.org/10.1109/TPAMI.2018.2865304

    Article  Google Scholar 

  24. Zhang, Z., Komazaki, N., Imamura, T., et al.: Directional selection of two-dimensional complex discrete wavelet transform and its application to image processing. Int. J. Wavelets Multi. 8(4), 659–676 (2010)

    Article  MathSciNet  Google Scholar 

  25. Lei, B., Fan, J.: Image thresholding segmentation method based on minimum square rough entropy. Appl. Soft Comput. 84, 105687 (2019)

    Article  Google Scholar 

  26. Chen, L., Papandreou, G., Kokkinos, I., et al.: Deeplab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs. IEEE Trans. Pattern Anal. 40(4), 834–848 (2017)

    Article  Google Scholar 

  27. Abdollahzadeh, B., Gharehchopogh, F.S.: A multi-objective optimization algorithm for feature selection problems. Eng. Comput. 38(3), 1845–1863 (2022)

    Article  Google Scholar 

  28. Such, F.P., Madhavan, V., Conti, E., et al.: Deep neuroevolution: genetic algorithms are a competitive alternative for training deep neural networks for reinforcement learning, arXiv preprint arXiv: 1712. 06567, vol. 20, (2017)

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Acknowledgements

Thanks for the supports of the National Natural Science Foundation of China (Grant No.52075231), the Qinglan project for middle-aged and young science leaders of Jiangsu province.

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Authors and Affiliations

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

    Xiangning Lu & Zhenzhi He

  2. School of Microelectronics, Tianjin University, Tianjin, China

    Zengxiang Wang

  3. Key Laboratory of Digital Manufacturing Equipment and Technology, Huazhong University of Science and Technology, Wuhan, China

    Xiangning Lu, Guanglan Liao & Tielin Shi

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  1. Xiangning Lu
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  2. Zengxiang Wang
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  3. Zhenzhi He
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  4. Guanglan Liao
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  5. Tielin Shi
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Contributions

XL and ZH wrote the main manuscript text. ZW programmed the algorithm. GL and XL prepared figures. TS and GL provided the idea of SAM enhancement. All the authors consented to publish this paper.

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Correspondence to Zhenzhi He.

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Lu, X., Wang, Z., He, Z. et al. Multi-scale Coefficients Fusion Strategy for Enhancement of SAM Image in Solder Joints Detection. J Nondestruct Eval 43, 6 (2024). https://doi.org/10.1007/s10921-023-01024-x

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