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Automatic localization of signal sources in photon emission images for integrated circuit analysis

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Abstract

Defects localization is a key step in failure analysis of highly scaled complementary oxide semiconductor integrated circuits (ICs). It gives prior information on VLSI circuits and allows the designers to improve their diagnostic. Light emission techniques are efficient to localize defects in modern ICs. The identification of the emission spots is an essential step of the process because it shows where is located the electrical activity in the chip. Due to scaling, more and more emission nodes are located within the acquisition area so that large variations of emission intensity can exist. Thresholding techniques have been implemented, but they fail to provide an exhaustive localization. To overcome this problem, we introduce in this paper an automatic unsupervised process. It is based on a combination of median filtering, mathematical morphology and local maxima research. This new approach is evaluated and tested on 20 photon emission images (real and simulated). The final result is compared to an expert evaluation, and the detection quality is quantified.

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References

  1. Zontak, M., Cohen, I.: Defect detection in patterned wafers using anisotropic kernels. Mach. Vis. Appl. 21(2), 129–141 (2010)

    Article  MATH  Google Scholar 

  2. Anderson, R.E., Soden, J.M., Henderson, Christopher.L.: Future technology challenges for failure analysis. Technical report, Sandia National Labs., Albuquerque, NM (1995)

  3. Heinrich, H.K., Bloom, D.M., Hemenway, B.R.: Noninvasive sheet charge density probe for integrated silicon devices. Appl. Phys. Lett. 48(16), 1066–1068 (1986)

    Article  Google Scholar 

  4. Yee, W.M., Paniccia, M., Eiles, T., Rao, V.: Laser voltage probe (lVP): a novel optical probing technology for flip-chip packaged microprocessors. In: Proceedings of the 1999 7th International Symposium on the Physical and Failure Analysis of Integrated Circuits, pp.15–20. IEEE (1999)

  5. Marriott, G., Clegg, R.M., Donna, J.A.-J., Jovin, T.M.: Time resolved imaging microscopy. Phosphorescence and delayed fluorescence imaging. Biophys. J. 60(6), 1374 (1991)

    Article  Google Scholar 

  6. Bascoul, G., Perdu P., Celi, G., Dudit, S., Lewis, D.: Time resolved imaging at low power supply on 45 nm technology. In: 2011 18th IEEE International Symposium on the Physical and Failure Analysis of Integrated Circuits (IPFA), pp. 1–4. IEEE (2011)

  7. Uchikado, A., Kawanab, S., Okubo, T., Shimase, A., Majima, T., Hirai, N., Ito, Y., Nakamura, T.: Case studies on application of time resolved imaging emission microscopy for backside timing analysis. In: 2012 19th IEEE International Symposium on the Physical and Failure Analysis of Integrated Circuits (IPFA), pp. 1–4, July (2012)

  8. Song, P., Stellari, F., Weger, A.: Counterfeit IC detection using light emission. In: 2014 IEEE International Test Conference (ITC), pp. 1–8 (2014)

  9. Skorobogatov, S.: Using optical emission analysis for estimating contribution to power analysis. In: 2009 Workshop on Fault Diagnosis and Tolerance in Cryptography (FDTC), pp. 111–119 (2009)

  10. Tajik, S., Nedospasov, D., Seifert, J.P., Helfmeier, C., Boit, C.: Emission analysis of hardware implementations. In: 17th Euromicro Conference on Digital System Design, pp. 528–534. IEEE (2014)

  11. Perdu, P., Bascoul, G., Chef, S., Celi, G., Sanchez, K.: Optical probing (EOFM/TRI): a large set of complementary applications for ultimate VLSI. In: 2013 20th IEEE International Symposium on the Physical and Failure Analysis of Integrated Circuits (IPFA), pp. 119–126. IEEE (2013)

  12. Khurana, N., Chiang, C.L.: Analysis of product hot electron problems by gated emission microscopy. In: Reliability Physics Symposium, 1986. 24th Annual, pp. 189–194. IEEE (1986)

  13. Khurana, N.: Image emission microscope with improved image processing capability, March (1989)

  14. Barton, D.L., Tangyunyong, P., Soden, J.M., Liang, A.Y., Zaplatin, A.N., Shivanandan, K., Donohoe, G.: Infrared light emission from semiconductor devices. In: Conference Proceedings of the 22nd Symposium for Test and Failure Analysis (ISTFA), pp. 9–17 (1996)

  15. Desplats, R., Eral, A., Beaudoin, F., Perdu, P., Chion, A., Shah, K., Lundquist, T.R.: IC diagnostic with time resolved photon emission and cad auto-channeling. In: International Symposium of Testing and Failure Analysis, pp. 45–55. ASM International; 1998 (2003)

  16. Chef, S., Jacquir, S., Sanchez, K., Perdu, P., Binczak, S.: Unsupervised image processing scheme for transistor photon emission analysis in order to identify defect location. J. Electron. Imaging 24(1), 013019–013019 (2015)

    Article  Google Scholar 

  17. Jalba, A.C., Wilkinson, M.H.F., Roerdink, J.B.T.M., Bayer, M.M., Juggins, S.: Automatic diatom identification using contour analysis by morphological curvature scale spaces. Mach. Vis. Appl. 16(4), 217–228 (2005)

    Article  Google Scholar 

  18. Jain, P., Tyagi, V.: A survey of edge-preserving image denoising methods. Inf. Syst. Front. 18(1), 159–170 (2016)

    Article  Google Scholar 

  19. Kumar, B.K.S.: Image denoising based on non-local means filter and its method noise thresholding. Signal Image Video Process. 7(6), 1211–1227 (2013)

    Article  Google Scholar 

  20. Arbeláez, P., Hariharan, B., Gu, C., Gupta, S., Bourdev, L., Malik, J.: Semantic segmentation using regions and parts. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3378–3385. IEEE (2012)

  21. Chef, S., Jacquir, S., Sanchez, K., Perdu, P., Binczak, S.: Filtering and emission area identification in the time resolved imaging data. In: ISTFA 2012: Conference Proceedings from the 38th International Symposium for Testing and Failure Analysis: November 11–15, 2012, Phoenix Convention Center, Phoenix, Arizona, USA, p. 264. ASM International (2012)

  22. Hadjadj, Z., Cheriet, M., Meziane, A., Cherfa, Y.: A new efficient binarization method: application to degraded historical document images. Signal Image Video Process. 11, 1–8 (2017)

    Article  Google Scholar 

  23. Khiyal, M.S.H, Khan, A., Bibi, A.: Modified watershed algorithm for segmentation of 2D images. Issues Informing Sci. Inf. Technol. 6, 877–886 (2009). https://doi.org/10.28945/1077

  24. Parsi, A., Sorkhi, A.G., Zahedi, M.: Improving the unsupervised LBG clustering algorithm performance in image segmentation using principal component analysis. Signal Image Video Process. 10(2), 301–309 (2016)

    Article  Google Scholar 

  25. Shahbaba, M., Beheshti, S.: Signature test as statistical testing in clustering. Signal Image Video Process. 10(7), 1343–1351 (2016)

    Article  Google Scholar 

  26. Chef, C., Perdu, P., Bascoul, G., Jacquir, S., Sanchez, K., Binczak, S.: New statistical post processing approach for precise fault and defect localization in tri database acquired on complex VLSI. In: 2013 20th IEEE International Symposium on the Physical and Failure Analysis of Integrated Circuits (IPFA), pp. 136–141. IEEE (2013)

  27. Chef, C., Jacquir, S., Perdu, P., Sanchez, K., Binczak, S.: Cluster matching in time resolved imaging for VLSI analysis. In: 2014 IEEE 21st International Symposium on the Physical and Failure Analysis of Integrated Circuits (IPFA), pp. 379–382. IEEE (2014)

  28. Nirmala, S., Nagabhushan, P.: Foreground text segmentation in complex color document images using Gabor filters. Signal Image Video Process. 6(4), 669–678 (2012)

    Article  Google Scholar 

  29. Pitas, I., Venetsanopoulos, A.N.: Nonlinear Digital Filters, vol. 84. Springer, New York (1990)

    Book  MATH  Google Scholar 

  30. Serra, J.: Introduction to mathematical morphology. Comput. Vis. Graph. Image Process. 35(3), 283–305 (1986)

    Article  MATH  Google Scholar 

  31. Soille, P.: Morphological Image Analysis: Principles and Applications. Springer, New York (2003)

    MATH  Google Scholar 

  32. Comer, L., Delp, E.J.: Morphological operations for color image processing. J. Electron. Imaging 8(3), 279–289 (1999)

    Article  Google Scholar 

  33. Dougherty, E.R., Lotufo, R.A.: Hands-on Morphological Image Processing, vol. 59. SPIE Press, Bellingham (2003)

    Book  Google Scholar 

  34. Beyerer, J., León, F.P., Frese, C.: Morphological image processing. In: Machine Vision, pp. 607–647. Springer (2016)

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Boscaro, A., Jacquir, S., Chef, S. et al. Automatic localization of signal sources in photon emission images for integrated circuit analysis. SIViP 12, 775–782 (2018). https://doi.org/10.1007/s11760-017-1219-z

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  • DOI: https://doi.org/10.1007/s11760-017-1219-z

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