Abstract

Microscopic deformation analysis has been performed using digital image correlation and artificial neural networks (ANNs). Cross-correlations of small image regions before and after deformation contain a peak, the position of which indicates the displacement to pixel accuracy. Subpixel resolution has been achieved here by nonintegral pixel shifting and by training ANNs to estimate the fractional part of the displacement. Results from displaced and thermally stressed microelectronic devices indicate these techniques can achieve comparable accuracies to other subpixel techniques and that the use of ANNs can facilitate very fast analysis without knowledge of the analytical form of the image correlation function.

© Optical Society of America

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References

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  1. D. Vogel, A. Schubert, W. Faust, R. Dudek and B. Michel, “MicroDAC - a novel approach to measure in situ deformation fields of microscopic scale,” Microelectron. Reliab. 36, 1939-1942 (1996).
    [CrossRef]
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    [CrossRef]
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    [CrossRef] [PubMed]
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    [CrossRef] [PubMed]
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    [CrossRef] [PubMed]
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    [CrossRef] [PubMed]
  7. T. C. Chu, W. F. Ranson, M. A. Sutton and W. H. Peters, “Applications of digital-image-correlation techniques to experimental mechanics,” Exp. Mech. 25, 232-244 (1985).
    [CrossRef]
  8. B. Han, “Recent advances of moiré and microscopic moiré interferometry for thermal deformation analyses of microelectronic devices,” Exp. Mech. 38, 278-288 (1998).
  9. J. W. Goodman, Statistical optics (Wiley, New York, 1985).
  10. J. Millman and C. C. Halkias, Integrated electronics (McGraw-Hill, Tokyo 1971), Sect. 17-20.
  11. K. Swingler, Applying neural networks - a practical guide (Academic Press, London 1996).
  12. J. Rogers, Object-oriented neural networks in C++ (Academic Press, San Diego 1997), Chap. 5.
  13. R. C. Eberhart and R. W. Dobbins, Neural network PC tools - a practical guide (Academic Press, San Diego 1990), Chaps. 10-14.

Other (13)

D. Vogel, A. Schubert, W. Faust, R. Dudek and B. Michel, “MicroDAC - a novel approach to measure in situ deformation fields of microscopic scale,” Microelectron. Reliab. 36, 1939-1942 (1996).
[CrossRef]

G. Vendroux, N. Schmidt and W. G. Knauss, “Submicron deformation field measurements: Part 3. demonstration of deformation determinations,” Exp. Mech. 38, 154-160 (1998).
[CrossRef]

J. M. Schmitt, “OCT elastography: imaging microscopic deformation and strain of tissue,” Opt. Express 3, 199-211 (1998), http://www.opticsexpress.org/oearchive/source/5793.htm.
[CrossRef] [PubMed]

Y. Kobayashi, T. Takemori, N. Mukohzaka, N. Yoshida and S. Fukishima, “Real-time velocity measurement by the use of a speckle-pattern correlation system that incorporates a ferroelectric liquid-crystal spatial light modulator,” Appl. Opt. 33, 2785-2794 (1994).
[CrossRef] [PubMed]

M. Sjödahl, “Electronic speckle photography: increased accuracy by nonintegral pixel shifting,” Appl. Opt. 33, 6667-6673 (1994).
[CrossRef] [PubMed]

M. Sjödahl, “Accuracy in electronic speckle photography,” Appl. Opt. 36, 2875-2885 (1997).
[CrossRef] [PubMed]

T. C. Chu, W. F. Ranson, M. A. Sutton and W. H. Peters, “Applications of digital-image-correlation techniques to experimental mechanics,” Exp. Mech. 25, 232-244 (1985).
[CrossRef]

B. Han, “Recent advances of moiré and microscopic moiré interferometry for thermal deformation analyses of microelectronic devices,” Exp. Mech. 38, 278-288 (1998).

J. W. Goodman, Statistical optics (Wiley, New York, 1985).

J. Millman and C. C. Halkias, Integrated electronics (McGraw-Hill, Tokyo 1971), Sect. 17-20.

K. Swingler, Applying neural networks - a practical guide (Academic Press, London 1996).

J. Rogers, Object-oriented neural networks in C++ (Academic Press, San Diego 1997), Chap. 5.

R. C. Eberhart and R. W. Dobbins, Neural network PC tools - a practical guide (Academic Press, San Diego 1990), Chaps. 10-14.

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Figures (4)

Fig. 1.
Fig. 1.

Micrographs (320×320µm) of cross-sections through an PQFP microelectronics device. Sample 1 is packaging material and possesses a fine homogenous structure. Sample 2 contains regions of copper (lower area), silicon (upper right), packaging (upper left) and filler (center).

Fig. 2.
Fig. 2.

Test performance of several MLP architectures on sample 1 (lower plots) and sample 2.

Fig. 3.
Fig. 3.

Unfiltered in-plane displacement map calculated using NPS with a 24×24 subimage for a temperature rise from 20 °C to 50 °C. The length and direction of the arrows indicate the magnitude and direction of the displacement for each subimage.

Fig. 4.
Fig. 4.

Unfiltered in-plane displacement map calculated using MLP-2 for a temperature rise from 20 °C to 50 °C. The displacement fields of Figs. 3 and 4 may be smoothed and differentiated to produce strain maps.

Tables (2)

Tables Icon

Table 1. Performance of successive approximation NPS MDAC for pure translation.

Tables Icon

Table 2. Performance of ANN MDAC for pure translation.

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