Accurate and efficient modeling of SOI MOSFET with technology independent neural networks

This paper presents neural network (NN) approaches for modeling the I-V characteristics of silicon-on-insulator MOSFETs. The modeling approach is technology independent, fast, and accurate, which makes it suitable for circuit simulators. In the model, two different NN architectures, namely, multilayer perceptron and generalized radial basis function, are used and compared. To increase the training efficiency of the NN, both modular and region partitioning methods have been proposed and utilized. In addition, two approaches for obtaining the transconductance and output conductance of the device are discussed. The first approach makes use of an NN for the conductances, while the second uses the numerical differentiation of the I-V results. To confirm the accuracy of the model, the drain-current characteristics as well as conductances obtained by the model are compared to the simulation data for the points where the NNs are not trained. The comparison shows excellent agreements with relative errors of around 1% over a wide range of drain and gate voltages as well as channel lengths and widths.