Application Of Knowledge-based Artificial Neural Network To Electromagnetic Engineering

For today's high performance and fast electromagnetic engineering computer-aided design, it is increasingly necessary to model elements and devices with high accuracy, reliability and efficiency. Artificial neural network (ANN) recently has received extensive attention as a fast and accuate modeling tool in electromagnetic engineering.In this paper, knowledge-based neural network (KBN7N) is introduced to model sophisticated electromagnetic objects by incorporating prior knowledge into artificial neural network structures. Two novel knowledge-based neural network structures are presented. One is the robust knowledge-based neural network (RKBNN) with principle component analysis (PCA) as data pre-processor for network training. The other is the neural network with knowledge-based neurons (NNKBN) where extended prior knowledge analytic formulas work as activation functions of the neurons.Firstly, the RKBNN is used for modeling the frequency-dependent resistance and inductance extraction of coplanar interconnect in high speed digital integrated circuits (HSDIC) . Results show that the network training procedure becomes efficient and stable with PCA as data pre-processor and the developed RKBNN models are robust for generalization.Secondly, the NNKBN trained with insufficient training data is applied to model the discontinuity of nonsymmetrical stripline gap widely used in multi-chip package module (MCM) . Results show that the N7NKBN models are good for extrapolation with high accuracy.All of the proposed models not only preserve the accuracy of the EM simulations, but also simplify their CPU and memory requirements, and at the same time keep good extrapolation capability. Hence, the artificial neural networks incorporated with prior knowledge information of the problems to be modeled have potential power for the high performance and fast CAD in electromagnetic engineering.