Neuro-fuzzy network approach for modeling submicron MOSFETs: application to MOSFET subcircuit simulation

A neuro-fuzzy network approach is developed to model the nonlinear behavior of submicron metal-oxide semiconductor field-effect transistors (MOSFETs). The proposed model is trained and implemented as a MOSFET in a software environment. The training data are obtained through various simulations of a MOSFET Berkeley short channel insulated-gate field-effect transistor model 3 (BSIM3) in HSPICE, and the trained model is utilized to simulate the MOSFET device. The obtained result shows good and noticeable agreement between the numerical result of the original model in HSPICE and the neuro-fuzzy approach in the device and subcircuit modeling.