Hostname: page-component-cd9895bd7-dk4vv Total loading time: 0 Render date: 2024-12-26T04:47:31.406Z Has data issue: false hasContentIssue false

Application of a Bayesian network to integrated circuit tester diagnosis

Published online by Cambridge University Press:  27 February 2009

Daniel Mittelstadt
Affiliation:
Department of Mechanical Engineering, Oregon State University, Corvallis, OR 97331-4602
Robert Paasch
Affiliation:
Department of Mechanical Engineering, Oregon State University, Corvallis, OR 97331-4602
Bruce D’Ambrosio
Affiliation:
Department of Computer Science, Oregon State University, Corvallis, OR 97331-4602

Abstract

Research efforts to implement a Bayesian belief-network-based expert system to solve a real-world diagnostic problem – the diagnosis of integrated circuit (IC) testing machines – are described. The development of several models of the IC tester diagnostic problem in belief networks also is described, the implementation of one of these models using symbolic probabilistic inference (SPI) is outlined, and the difficulties and advantages encountered are discussed. It was observed that modeling with interdependencies in belief networks simplifies the knowledge engineering task for the IC tester diagnosis problem, by avoiding procedural knowledge and focusing on the diagnostic component’s interdependencies. Several general model frameworks evolved through knowledge engineering to capture diagnostic expertise that facilitated expanding and modifying the networks. However, model implementation was restricted to a small portion of the modeling, that of contact resistance failures, which were due to time limitations and inefficiencies in the prototype inference software we used. Further research is recommended to refine existing methods, in order to speed evaluation of the models created in this research. With this accomplished, a more complete diagnosis can be achieved.

Type
Articles
Copyright
Copyright © Cambridge University Press 1995

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

Dagum, P., Galper, A., & Horvitz, E. (1992). Dynamic network models for forecasting. Uncert. Artif. Intell. 8, 4148.Google Scholar
D’Ambrosio, B. (1992). Real-time value-driven diagnosis and repair. In Proc. Third Int. Workshop on the Principles of Diagnosis, 8696.Google Scholar
D’Ambrosio, B. (1993). Incremental probabilistic inference. In Ninth Ann. Conf. on Uncertainty on AI (Heckerman, D.,& Mamdani, A., Eds.), pp. 301308. Morgan Kaufmann, Palo Alto, CA.Google Scholar
De Kleer, J. (1987). Diagnosing multiple faults. Artif. Intell. 32, 97130.CrossRefGoogle Scholar
Friedrich, G., & Nejdl, W. (1992). Choosing Observations and Actions in Model-Based Diagnosis/Repair Systems. In Proc. Third Int. Conf. on Knowledge Representation and Reasoning, Cambridge, MA.Google Scholar
Heckerman, D. (1991). Probabilistic Similarity Networks. MIT Press, Cambridge, MA.Google Scholar
Henrion, M., Breese, J., & Horvitz, E. (1991). Decision analysis and expert systems. AI Magazine 12(4), 6491.Google Scholar
Li, Z., & D’Ambrosio, B. (1994). Efficient inference in Bayes nets as a combinatorial optimization problem. Int. J. Approx. Reasoning 10 (5).Google Scholar
Nicholson, A., & Brady, J. (1992). Sensor validation using dynamic belief networks. Uncert. Artif. Intell. 8, 207214.Google Scholar
Pearl, J. (1988). Probabilistic Reasoning in Intelligent Systems. Morgan Kaufmann, Palo Alto, CA.Google Scholar
Provan, G. (1992). Modelling the dynamics of diagnosis and treatment using temporal influence diagrams. In Third Int. Workshop on the Principles of Diagnosis, 97106.Google Scholar