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Proceedings Paper

Chronological equipment diagnosis using evidence integration
Author(s): Norman H. Chang; Costas J. Spanos
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Paper Abstract

We have developed a diagnostic system that employs the Dempster-Shafer (D-S) evidential reasoning technique to conduct malfunction diagnosis on semiconductor manufacturing equipment. This is accomplished by combining evidence originating from equipment maintenance records, from real-time equipment data, and from measurements on the finished product. Using this information, equipment malfunctions are analyzed and their causes are inferred through the resolution of qualitative and quantitative constraints. The qualitative constraints describe the "normal" operation of the equipment. The quantitative constraints are numerical models that apply to the manufacturing step in question. These models are specifically created and characterized through experimentation and statistical analysis. The violation of these constraints is linked to the evaluation of continuous "belief functions" for the calculation of the "belief' associated with the various types of failure. The belief functions encapsulate the experience of many equipment maintenance specialists. Once created, the belief functions can be fine-tuned automatically, drawing from historical maintenance records. These records are stored in symbolic form to facilitate this task. A prototype of this diagnostic system was implemented in an object-oriented programming environment. This implementation enables knowledge and functionalities to be shared by different pieces of manufacturing equipment. The D-S diagnostic method was first applied to a reactor used for Low Pressure Chemical Vapor Deposition (LPCVD) of undoped polysilicon films. Early results indicate that this diagnostic system is sensitive, stable, and accurate.

Paper Details

Date Published: 1 January 1990
PDF: 12 pages
Proc. SPIE 1293, Applications of Artificial Intelligence VIII, (1 January 1990); doi: 10.1117/12.21146
Show Author Affiliations
Norman H. Chang, Univ. of California/Berkeley (United States)
Costas J. Spanos, Univ. of California/Berkeley (United States)


Published in SPIE Proceedings Vol. 1293:
Applications of Artificial Intelligence VIII
Mohan M. Trivedi, Editor(s)

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