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Quantification and Prediction of Damage in SAM Images of Semiconductor Devices

  • Dženana Alagić
  • Olivia Bluder
  • Jürgen Pilz
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10882)

Abstract

The importance of statistical lifetime models for the reliability assessment of semiconductors is increasing steadily, because resources and time are limited. The devices are tested under accelerated electrical and thermal conditions which causes degradation in metal layers. To visualize the damage, Scanning Acoustic Microscopy (SAM) is used. In this work, an approach combining image processing and statistical modeling is presented in order to quantify and predict the damage intensity in SAM images. The image processing algorithm automatically locates and quantifies the maximum damaged areas in SAM images. The damage intensity is coded as an ordered categorical variable and a cumulative link model for damage prediction is defined. Both the algorithm and the proposed statistical model show good results.

Keywords

SAM Image processing Cumulative link model Semiconductor reliability Lifetime model 

Notes

Acknowledgments

This work was jointly funded by the Austrian Research Promotion Agency (FFG, Project No. 854247) and the Carinthian Economic Promotion Fund (KWF, contract KWF-1521/28101/40388).

References

  1. 1.
    Rasband, W.S.: ImageJ. U.S. National Institutes of Health, Bethesda, Maryland, USA (1997–2016). http://imagej.nih.gov/ij/
  2. 2.
    Alagić, D.: A statistical measure for fatigue induced degradation in metal layers. Master thesis, Alpen-Adria University of Klagenfurt (2017)Google Scholar
  3. 3.
    Thévenaz, P., Ruttimann, U.E., Unser, M.: A pyramid approach to subpixel registration based on intensity. IEEE Trans. Image Process. 7(1), 27–41 (1998). http://bigwww.epfl.ch/publications/thevenaz9801.htmlCrossRefGoogle Scholar
  4. 4.
    Glavanovics, M., Köck, H., Kosel, V., Smorodin, T.: A new cycle test system emulating inductive switching waveforms. In: Proceedings of the 12th European Conference on Power Electronics and Applications, pp. 1–9 (2007)Google Scholar
  5. 5.
    Agresti, A.: An Introduction to Categorical Data Analysis. Wiley, Hoboken (2007)Google Scholar
  6. 6.
    Christensen, R.H.B.: Analysis of ordinal data with cumulative link models estimation with the R-package ordinal (2015). https://cran.r-project.org/web/packages/ordinal/vignettes/clm_intro.pdf

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Dženana Alagić
    • 1
    • 2
  • Olivia Bluder
    • 1
  • Jürgen Pilz
    • 2
  1. 1.KAI - Kompetenzzentrum Automobil- und Industrieelektronik GmbHVillachAustria
  2. 2.Institut für StatistikAlpen-Adria-UniversitätKlagenfurtAustria

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