Modelling of LDMOS transistor using artificial neural networks: DC and RF performances

Abstract

This article presents an accurate method based on artificial neural networks (ANNs) for DC and RF modelling of laterally diffused metal oxide semiconductor (LDMOS) transistors, under various temperature conditions. In LDMOS transistors, temperature is an effective factor, so the proposed models include this parameter. Two neural networks‐based procedures have been proposed for LDMOS transistor modelling, first for DC and second for RF modelling. In each case, two kinds of neural networks have been used, multilayer perceptron and radial basis function neural networks. Two models are compared to each other in terms of accuracy, and for both of them, an excellent agreement between modelled and measured data is obtained. The ANN model is developed and trained with the help of data obtained by simulation of a Si‐LDMOS transistor using ADS software.

Keywords:

• RF and DC modelling
• LDMOS
• RF power transistor
• MLP
• RBF