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In semiconductor manufacturing, there are many opportunities to improve productivity or reduce equipment downtime by implementing an expert system to perform diagnostic reasoning. Expert systems can be used to diagnose problems with the equipment used in manufacturing and testing as well as diagnosing process problems when production quality declines. Expert systems have been used in hundreds of applications for fault diagnosis. Many of the early systems developed employ a "rule-based” approach, where, typically rules are used to describe a set of symptoms that must exist in order to conclude that a certain problem exists. This type of knowledge is often called "heuristic" knowledge because it contains the expert’s own specialized rules for problem solving that have been developed from years of experience. But, before an expert develops a set of heuristics, problems are solved by understanding a "model" of how a system operates. This "model-based" approach to fault diagnosis has been an area of recent research and development. Both of these approaches to fault diagnosis have been used successfully in many applications, but there are weaknesses in both approaches. Rule-based approaches tend to be brittle (i.e. unable to provide a solution - or worse, provide a wrong solution) if there is knowledge missing from the system, and model- based approaches may take a long time to reach a solution for a large model and must always use that model (i.e. no shortcuts, even when evidence points to a specific problem). Additionally, there are problems which cannot be modelled well enough to be put into a model-based approach. This paper will describe HINTS (Harris Internal Troubleshooting System), a classification approach to fault diagnosis that uses concepts from both model-based and rule-based approaches but overcomes their limitations.
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