Applying a robust heteroscedastic probabilistic neural network to analog fault detection and classification

IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems

Volumn 19, Issue 1, 2000, Pages 142-151

  Yang, Zheng Rong ^a   Zwolinski, Mark ^b   Chalk, Chris D ^c   Williams, Alan Christopher ^b  

^a HERIOT WATT UNIVERSITY   (United Kingdom)

^b UNIVERSITY OF SOUTHAMPTON   (United Kingdom)

^c NXP SEMICONDUCTORS   (Netherlands)

Author keywords

[No Author keywords available]

Indexed keywords

ELECTRIC FAULT CURRENTS; ELECTRIC FAULT LOCATION; LINEAR INTEGRATED CIRCUITS; STATISTICAL METHODS;

ANALOG INTEGRATED CIRCUITS; AUTOMATIC TEST PATTERN GENERATION; FAULT DIAGNOSIS; QUIESCENT SUPPLY CURRENT;

INTEGRATED CIRCUIT TESTING;

EID: 0033908161     PISSN: 02780070     EISSN: None     Source Type: Journal    
DOI: 10.1109/43.822626     Document Type: Article

References (29)

1. Y. K. Malaiya and A. P. Jayasumana, "Enhancement of resolution in supply current based testing for large ICs," in Proc. IEEE VLSI Test Symp.. 1991, pp. 291-296.
2. S. J. Spinks, C. D. Chalk, M. Zwolinski, and I. M. Bell, "Generation and verification of tests for analogue circuits subject to process parameter deviations," in Proc. 1997 IEEE Int. Symp. Defect and Fault Tolerance in VLSI Systems, Paris, Oct. 1997, pp. 100-108.
3. B. R. Epstein, M. Czigler, and S. R. Miller, "Fault detection and classification in linear integrated circuits: An application of discrimination analysis and hypothesis testing," IEEE Trans. Computer-Aided Design, vol. 12, pp. 102-113, Jan. 1993.
4. R. A. Fisher, "The use of multiple measurements in taxonomic problems,"/!««. Eugenics, vol. 7, pp. 178-188, 1936.
5. Z. R. Yang and G. Musgrave, "Applying artificial neural network to fault diagnosis of analogue circuit," in Proc. 3rd Int. Conf. VLSI and CAD, Korea, Nov. 1993.
6. S. Yu, B. W. Jervis, K. R. Eckersall, I. M. Bell, A. G. Hall, and G. E. Taylor, "Neural network approach to fault diagnosis in CMOS opamps with gate oxide short faults," Electron. Lett., vol. 30, pp. 695-696, 1994.
7. Y Maidon, B. W. Jervis, and S. Lesage, "Diagnosis of multifaults in analogue circuits using multilayer perceptrons," Proc. Inst. Elect. Eng. Circuits Devices Syst., vol. 144, pp. 149-154, 1997.
8. S.S. Somayajula, E. Sanchez-Sinencio, and J. P. de Gyvez, "Analog fault diagnosis based on ramping power supply current signature clusters," IEEE Trans. Circuits Syst.-11, vol. 43, pp. 703-712, Oct. 1996.
9. N. B. Hamida, K. Saab, D. Marche, B. Kaminska, and G. Quesnel, "LIMSoft: Automated tool for design and test integration of analog circuits," in Proc. Int. Test Conf., 1993, pp. 571-580.
10. M. Slamani and B. Kaminska, "Analog circuit fault diagnosis based on sensitivity computation and functional testing," IEEE Design Test Comput., vol. 9, pp. 30-39, Jan. 1992.
11. S. W. Director and R. A. Rohrer, "The generalized adjoint network and network sensitivities," IEEE Trans. Circuit Theory, vol. CT-16, pp. 318-323, Mar. 1969.
12. Y Chou, Statistical Analysis. New York: Holt Rinehart and Winston, 1970.
13. T. Kohonen, Self-Organization and Associative Memory, 3rd ed.. Berlin, Germany: Springer-Verlag, 1989.
14. R. L. Wilson and S. Ramesh, "Bankruptcy prediction using neural networks," Decision Support Syst., vol. 11, pp. 545-557, 1994.
15. Z. R. Yang, "UK construction company failure prediction: A robust heteroscedastic parzen window classifier," Ph.D., Univ. Portsmouth, Portsmouth, U.K., 1998.
16. H. Ogawa and K. Yamasaki, "A theory of over-learning," Artif. Neural Networks, vol. 2, pp. 215-218, 1992.
17. C. Bishop, Neural Networks for Pattern Recognition. Oxford, U.K.: Clarendon, 1995.
18. S. Chen, C. F. N. Cowan, and P. M. Grant, "Orthogonal least squares learning algorithm for radial basis function networks," IEEE Trans. Neural Networks, vol. 2, pp. 302-309, Feb..
19. D. Specht, "Probabilistic neural networks for classification, mapping, or associative memory," in Proc. Int. Conf. Neural Networks, vol. 1, 1988, pp. 525-530.
20. _, Probabilistic neural networks," Neural Networks, vol. 3, pp. 109-118, 1990.
21. E. Parzen, "On estimation of a probability density function and mode," Ann. Math. Statistics, vol. 3, pp. 1065-1076, 1962.
22. A. P. Dempster, N. M. Laird, and D. B. Rubin, "Maximum likelihood from incomplete data via the EM algorithm," J. Roy. Statistic Soc. (B), pp. 1-38, 1977.
23. R. L. Streit and T. E. Luginbuhl, "Maximum likelihood training of probabilistic neural networks," IEEE Trans. Neural Networks, vol. 5, pp. 764-783, Sept. 1994.
24. Z. R. Yang and S. Chen, "Robust maximum likelihood training of probabilistic neural networks," Neural Networks, vol. 11, no. 4, pp. 739-747, 1998.
25. R. G. Miller, "The jackknife-A review," Biometrika, vol. 61, pp. 1-15, 1974.
26. D. E. Rumelhart and J. L. McClelland, Parallel Distributed Processing: Exploration in the Cognition. Cambridge, MA: MIT Press, 1986.
27. W. Sammon, "A nonlinear mapping for data structure analysis," IEEE Trans. Computer, vol. C-18, pp. 401109, May 1969.
28. J. A. Leonard and M. A. Kramer, "Radial basis function networks for classifying process faults," IEEE Contr. Syst. Technol., pp. 31-38, Apr. 1991.
29. J. A. Leonard, M. A. Kramer, and L. H. Ungar, "Using radial basis functions to approximate a function and its error bounds," IEEE Trans. Neural Networks, vol. 5, pp. 624-627, Apr. 1992.