Artificial-Neural-Network Prediction of Device Behaviors in Submicron MOS Transistors

Abstract

A new technique is presented for modeling submicron MOSFET devices and predicting the MOSFET device behaviors by using fuzzy theory and artificial neural network (ANN). The power of ANNs used as a realization of I-V characterizations is demonstrated on the submicron MOS transistors. The prediction results are compared with experimental data of the actual devices and obtained a good agreement under different bias situations.