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

• Published in: IEEE transactions on microwave theory and techniques Vol. 70; no. 11; pp. 4597 - 4619
• Main Authors: Feng, Feng, Na, Weicong, Jin, Jing, Zhang, Jianan, Zhang, Wei, Zhang, Qi-Jun
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.11.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Artificial neural networks; Artificial neural networks (ANNs); CAD; Circuit design; Computer aided design; Deep learning; deep neural network; Design optimization; Filter design (mathematics); Gallium nitrides; High electron mobility transistors; Integrated circuit modeling; inverse modeling; knowledge-based neural network (KBNN); Large scale integration; Microelectromechanical systems; Microwave circuits; microwave computer-aided design (CAD); Microwave integrated circuits; Microwave theory and techniques; Modelling; Neural networks; neuro-transfer function (neuro-TF); Passive components; Recurrent neural networks; Semiconductor devices; Solid modeling; Substrate integrated waveguides; Transfer functions; Very large scale integration; Wireless power transmission
• DOI: 10.1109/TMTT.2022.3197751
• ISSN: 0018-9480

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