Double Gate Nanoscale MOSFET Modeling by a Neural Network Approach

摘要

The use of independently-driven nano-scale double gate (DG) MOSFETs for low-power analog circuits is emphasized and illustrated. In independent drive configuration, the top gate response of DG-MOSFETs can be altered by application of a control voltage on the bottom gate. This paper presents modeling of nanometer Double Gate (DG) MOSFET by a neural network approach. The principle of this approach is firstly introduced and its application in modeling DC and conductance characteristics of nano-DG MOSFET is demonstrated in details. It is shown that this approach does not need parameter extraction routine while its prediction of the transistor performance has a small relative error within 1% compared with measure data, thus its result is as accurate as that BSIM model.