A Comparative Analysis of Neuro-fuzzy and Grammatical Evolution Models for Simulating Field-Effect Transistors

In this paper we have developed fuzzy inference system models for a field-effect transistor. The hope is to see if such techniques can be used for inventing future semiconductor based devices. Three modeling techniques have been used. Neuro fuzzy based on grid partitioning and neuro fuzzy based on cluster partitioning create Sugeno fuzzy inference systems, which are trained with a neural network back propagation method. The third modeling technique is based on grammatical evolution, where a grammar template in the form of rules is evolved using genetic algorithms based evolutionary techniques. This grammar template is based on the Mamdani fuzzy inference system. Experimental results indicate that all models produce acceptable levels of performance, some even have an error rate that is nearly negligible.