Efficient design using fuzzy logic based regression models

With ever decreasing design cycles, it is important for designers to have techniques they can use to quickly and efficiently model new designs. From these models, package performance can be estimated and electrical, thermal, and mechanical considerations can be balanced. In this paper, we present a method of quickly investigating new design concepts based on knowledge of previously studied designs and knowledge of the differences between the new and old designs. This approach is useful when the difference between designs is simple and can be accurately modeled with fewer data. The use of less data equates to a savings of time and money. In the case studies presented, we establish two "base" models using 40 data each, then we establish two additional models of similar processes using only five and seven data each. Here, the initial (base) design and the design differences are modeled with fuzzy logic based regression models. Such fuzzy logic regression models can be based on numerically or empirically obtained data or physical knowledge of the system to be modeled. Once established, these models have the advantage of offering very fast response times uncharacteristic of experimentation, prototyping, and numerical methods such as finite element, finite difference, or boundary element modeling.