Abstract

Suitable illumination is a crucial aspect in the successful solution of machine vision problems. In this research we used objective image evaluation techniques and found that fluorescence imaging is superior to conventional illumination for acquiring images of integrated circuit lead bonds. This is an interesting and surprising finding, since there was no a priori reason to expect that any part of the bond would contain fluorescent components. Consequently, fluorescence imaging should be considered as an option in designing machine vision systems, especially if conventional illumination systems do not produce images of adequate quality. In this research we discovered a novel and effective method for threshold selection.

© 1992 Optical Society of America

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References

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  1. R. T. Chin, C. A. Harlow, “Automatic visual inspection: a survey.” IEEE Trans. Pattern Anal. Machine Intell. PAMI-4, 557–573 (1982).
    [CrossRef]
  2. D. Domres, J. MacFarlane, “Automatic optical inspection techniques for PWB’s—image acquisition and analysis remain areas of challenge,” Test Meas. World 3, 235–239 (1983).
  3. S. Mersch, “Polarized lighting for machine vision applications” in Proceedings of RI/SME Third Annual Conference on Applied Machine Vision (Robotics International, Society of Manufacturing Engineers, Schaumburg, Ill., 1984), pp. 687–691.
  4. A. Pugh, “Robot sensors—a personal view,” in Robot Sensors, A. Pugh ed. (Springer-Verlag, New York, 1986), Vol. 1, pp. 3–14.
  5. M. Born, E. Wolf, Principles of Optics, 2nd ed. (Macmillan, New York, 1964), pp. 264–265.
  6. D. C. Pritchard, Lighting, 2nd ed. (Longmans, London, 1978), pp. 27–30.
  7. R. S. Longhurst, Geometrical and Physical Optics, 2nd ed. (Wiley, New York, 1967), Chap. 20, p. 450.
  8. J. P. Frier, M. E. Frier, Industrial Lighting Systems (McGraw-Hall, New York, 1980), pp. 149–155.
  9. M. I. Sobel, Light (U. Chicago Press, Chicago, Ill., 1987), pp. 229–230.
  10. D. D. Zimmerman, J. R. Dinitto, Hybrid Microcircuit Design Guide (Noyes, Park Ridge, N.J., 1982).
  11. P. Burggraaf, “Inspection trends in IC assembly,” Semicond. Int. 8, 76–81 (1985).
  12. R. Kingslake, Optical Instruments, Vol. 4 of Applied Optics and Optical Engineering, R. Kingslake, ed. (Academic, New York, 1967), pp. 84–88.
  13. R. Kohler, “A segmentation system based on thresholding,” Comput. Graph. Image Processing 15, 319–338 (1981).
    [CrossRef]
  14. J. Chen, “Knowledge-directed lead bond inspection,” Ph.D. dissertation (Drexel University, Philadelphia, Pa., 1989).
  15. O. Tretiak, G. Y. Yu, “Curve-fitting method for measurement of the resolution of digital image input devices,” Opt. Eng. 25, 1312–1315 (1986).

1986 (1)

O. Tretiak, G. Y. Yu, “Curve-fitting method for measurement of the resolution of digital image input devices,” Opt. Eng. 25, 1312–1315 (1986).

1985 (1)

P. Burggraaf, “Inspection trends in IC assembly,” Semicond. Int. 8, 76–81 (1985).

1983 (1)

D. Domres, J. MacFarlane, “Automatic optical inspection techniques for PWB’s—image acquisition and analysis remain areas of challenge,” Test Meas. World 3, 235–239 (1983).

1982 (1)

R. T. Chin, C. A. Harlow, “Automatic visual inspection: a survey.” IEEE Trans. Pattern Anal. Machine Intell. PAMI-4, 557–573 (1982).
[CrossRef]

1981 (1)

R. Kohler, “A segmentation system based on thresholding,” Comput. Graph. Image Processing 15, 319–338 (1981).
[CrossRef]

Born, M.

M. Born, E. Wolf, Principles of Optics, 2nd ed. (Macmillan, New York, 1964), pp. 264–265.

Burggraaf, P.

P. Burggraaf, “Inspection trends in IC assembly,” Semicond. Int. 8, 76–81 (1985).

Chen, J.

J. Chen, “Knowledge-directed lead bond inspection,” Ph.D. dissertation (Drexel University, Philadelphia, Pa., 1989).

Chin, R. T.

R. T. Chin, C. A. Harlow, “Automatic visual inspection: a survey.” IEEE Trans. Pattern Anal. Machine Intell. PAMI-4, 557–573 (1982).
[CrossRef]

Dinitto, J. R.

D. D. Zimmerman, J. R. Dinitto, Hybrid Microcircuit Design Guide (Noyes, Park Ridge, N.J., 1982).

Domres, D.

D. Domres, J. MacFarlane, “Automatic optical inspection techniques for PWB’s—image acquisition and analysis remain areas of challenge,” Test Meas. World 3, 235–239 (1983).

Frier, J. P.

J. P. Frier, M. E. Frier, Industrial Lighting Systems (McGraw-Hall, New York, 1980), pp. 149–155.

Frier, M. E.

J. P. Frier, M. E. Frier, Industrial Lighting Systems (McGraw-Hall, New York, 1980), pp. 149–155.

Harlow, C. A.

R. T. Chin, C. A. Harlow, “Automatic visual inspection: a survey.” IEEE Trans. Pattern Anal. Machine Intell. PAMI-4, 557–573 (1982).
[CrossRef]

Kingslake, R.

R. Kingslake, Optical Instruments, Vol. 4 of Applied Optics and Optical Engineering, R. Kingslake, ed. (Academic, New York, 1967), pp. 84–88.

Kohler, R.

R. Kohler, “A segmentation system based on thresholding,” Comput. Graph. Image Processing 15, 319–338 (1981).
[CrossRef]

Longhurst, R. S.

R. S. Longhurst, Geometrical and Physical Optics, 2nd ed. (Wiley, New York, 1967), Chap. 20, p. 450.

MacFarlane, J.

D. Domres, J. MacFarlane, “Automatic optical inspection techniques for PWB’s—image acquisition and analysis remain areas of challenge,” Test Meas. World 3, 235–239 (1983).

Mersch, S.

S. Mersch, “Polarized lighting for machine vision applications” in Proceedings of RI/SME Third Annual Conference on Applied Machine Vision (Robotics International, Society of Manufacturing Engineers, Schaumburg, Ill., 1984), pp. 687–691.

Pritchard, D. C.

D. C. Pritchard, Lighting, 2nd ed. (Longmans, London, 1978), pp. 27–30.

Pugh, A.

A. Pugh, “Robot sensors—a personal view,” in Robot Sensors, A. Pugh ed. (Springer-Verlag, New York, 1986), Vol. 1, pp. 3–14.

Sobel, M. I.

M. I. Sobel, Light (U. Chicago Press, Chicago, Ill., 1987), pp. 229–230.

Tretiak, O.

O. Tretiak, G. Y. Yu, “Curve-fitting method for measurement of the resolution of digital image input devices,” Opt. Eng. 25, 1312–1315 (1986).

Wolf, E.

M. Born, E. Wolf, Principles of Optics, 2nd ed. (Macmillan, New York, 1964), pp. 264–265.

Yu, G. Y.

O. Tretiak, G. Y. Yu, “Curve-fitting method for measurement of the resolution of digital image input devices,” Opt. Eng. 25, 1312–1315 (1986).

Zimmerman, D. D.

D. D. Zimmerman, J. R. Dinitto, Hybrid Microcircuit Design Guide (Noyes, Park Ridge, N.J., 1982).

Comput. Graph. Image Processing (1)

R. Kohler, “A segmentation system based on thresholding,” Comput. Graph. Image Processing 15, 319–338 (1981).
[CrossRef]

IEEE Trans. Pattern Anal. Machine Intell. (1)

R. T. Chin, C. A. Harlow, “Automatic visual inspection: a survey.” IEEE Trans. Pattern Anal. Machine Intell. PAMI-4, 557–573 (1982).
[CrossRef]

Opt. Eng. (1)

O. Tretiak, G. Y. Yu, “Curve-fitting method for measurement of the resolution of digital image input devices,” Opt. Eng. 25, 1312–1315 (1986).

Semicond. Int. (1)

P. Burggraaf, “Inspection trends in IC assembly,” Semicond. Int. 8, 76–81 (1985).

Test Meas. World (1)

D. Domres, J. MacFarlane, “Automatic optical inspection techniques for PWB’s—image acquisition and analysis remain areas of challenge,” Test Meas. World 3, 235–239 (1983).

Other (10)

S. Mersch, “Polarized lighting for machine vision applications” in Proceedings of RI/SME Third Annual Conference on Applied Machine Vision (Robotics International, Society of Manufacturing Engineers, Schaumburg, Ill., 1984), pp. 687–691.

A. Pugh, “Robot sensors—a personal view,” in Robot Sensors, A. Pugh ed. (Springer-Verlag, New York, 1986), Vol. 1, pp. 3–14.

M. Born, E. Wolf, Principles of Optics, 2nd ed. (Macmillan, New York, 1964), pp. 264–265.

D. C. Pritchard, Lighting, 2nd ed. (Longmans, London, 1978), pp. 27–30.

R. S. Longhurst, Geometrical and Physical Optics, 2nd ed. (Wiley, New York, 1967), Chap. 20, p. 450.

J. P. Frier, M. E. Frier, Industrial Lighting Systems (McGraw-Hall, New York, 1980), pp. 149–155.

M. I. Sobel, Light (U. Chicago Press, Chicago, Ill., 1987), pp. 229–230.

D. D. Zimmerman, J. R. Dinitto, Hybrid Microcircuit Design Guide (Noyes, Park Ridge, N.J., 1982).

R. Kingslake, Optical Instruments, Vol. 4 of Applied Optics and Optical Engineering, R. Kingslake, ed. (Academic, New York, 1967), pp. 84–88.

J. Chen, “Knowledge-directed lead bond inspection,” Ph.D. dissertation (Drexel University, Philadelphia, Pa., 1989).

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Figures (6)

Fig. 1
Fig. 1

Schematic of the illumination system for machine vision: (a) conventional, (b) fluorescence.

Fig. 2
Fig. 2

Side and top views of an integrated circuit ball bond.

Fig. 3
Fig. 3

Microscope equipped for conventional (incidence) and fluorescence imaging. R indicates the components used for conventional illumination, and F is the fluorescence.

Fig. 4
Fig. 4

Images of a lead bond obtained with (a) conventional and (b) ultraviolet fluorescence systems. We see that the fluorescence image is superior. It has better contrast and clearer edges and regions. (c), (d) Images of (a) and (b), respectively, after thresholding to separate pad from background; (e) and (f) are images that show thresholding for separating bond from pad. We see that a simple threshold can separate bond from pad for the image obtained by using fluorescence. The threshold can be set automatically by using Gaussian curve fitting. Some information is lost when thresholding is used to separate the bond from the pad in the conventional image.

Fig. 5
Fig. 5

Images of the same lead bond obtained with different fluorescent excitation and computer processing: (a) green, (b) blue, (c) ultraviolet. We see that the image obtained with ultraviolet is best. It has better contrast and clearer edges and regions. Sobel edge detection is shown in (d), (e), and (f). Thresholding for separation of pad from background is shown in (g), (h), and (i). Thresholding for separation of bond from pad is shown in (j), (k), and (l).

Fig. 6
Fig. 6

Histograms and Gaussian fit to the peak of images in Fig. 5(c). T1 separates the pad from the background, and T2 separates the bond from the pad.

Tables (3)

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Table I Parameters for Filter Sets of Three Light Sources (nanometers)

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Table II Results of Comparison of Regular and Fluorescent Light

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Table III Comparison of Fluorescent Excitation Wavelengthsa

Equations (2)

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f ( i , A , σ , μ ) = A 2 π σ - exp [ - ( i - μ ) 2 / 2 σ 2 ] ,
d ( A , σ , μ ) = i [ H ( i ) - F ( i , A , σ , μ ) ] 2 ,

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