Report Number: CS-TR-94-1526
Institution: Stanford University, Department of Computer Science
Title: Combining Experiential and Theoretical Knowledge in the Domain of Semiconductor Manufacturing
Author: Mohammed, John Llewelyn
Date: September 1994
Abstract: Semiconductor Manufacturing is characterized by complexity and continual, rapid change. These characteristics reduce the effectiveness of traditional diagnostic expert systems: the knowledge represented cannot adapt to changes in the manufacturing plan because the dependence of the knowledge on the plan is not explicitly represented. It is impractical to manually encode all the dependencies in a complex plan. We address this problem in two ways. First, we employ model-based techniques to encode theoretical knowledge, so that symbolic simulation of a new manufacturing plan can automatically glean diagnostic information. Our representation is sufficiently detailed to capture the plan's inherent causal dependencies, yet sufficiently abstract to make symbolic simulation practical. This theoretical knowledge can adapt to changes in the manufacturing plan. However, the expressiveness and tractability of our representational machinery limit the range of phenomena that we can represent. Second, we describe Generic Rules, which combine the expressiveness of heuristic rules with the robustness of theoretical models. Generic Rules are general patterns for heuristic rules, associated with model-based restrictions on the situations in which the patterns can be instantiated to form rules for new contexts. In this way, theoretical knowledge is employed to encode the dependence of heuristic knowledge on the manufacturing plan.