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.