Artificial Neural Networks for Microwave Computer-Aided Design: The State of the Art

This article presents an overview of artificial neural network (ANN) techniques for a microwave computer-aided design (CAD). ANN-based are becoming useful performing forward/inverse modeling active/passive components to enhance circuit design. With measured or simulated data devices, ANNs can be trained learn relevant relationships, which are, otherwise, computationally expensive efficient analytical formulas not available. Fundamental concepts the ANN structure and training, such as feedforward networks (FFNNs), recurrent (RNNs)/dynamic (DNNs)/time-delay (TDNNs), deep networks, training extrapolation, described. Knowledge-based (KBNNs) described improving accuracy reliability optimization. Various advanced techniques, neuro-transfer function (neuro-TF) modeling, inverse discussed. The existing emerging applications in CAD identified, electromagnetic (EM)/multiphysics nonlinear circuits transistors, filter design, very large-scale integration (VLSI) interconnects, oscillator, transmitter receiver gallium nitride (GaN) high electron-mobility transistor (HEMT), wireless power transfer (WPT), microelectromechanical system (MEMS), substrate-integrated waveguide (SIW).