Cascading metamodels from different sources for performance analysis of a power module

Abstract

During the development process of a semiconductorbased product several types of results are generated, often in large volumes, e.g. simulation or test measurements. These have to be processed and can then be used as a reusable knowledge base for further experiments/developments. Hence, to manage such knowledge from various data sources, it is not sufficient to use classical data analysis methods. A compressed representation of this information, showing only what is important with respect to the systems performance, is desirable. We develop a method to support the combination of information from different sources and to represent it. The concept is based on cascading metamodels: The outputs of metamodels become inputs to subsequent metamodels, and mathematical composition operators can be generated for this concatenating procedure. This method is applied to a power module in order to perform sensitivity analysis on the combined metamodel.