A coupled FDTD-artificial neural network technique for large-signal analysis of microwave circuits

Goasguen, Sebastien; El-Ghazaly, S.M.

doi:10.1002/mmce.10018

Cited by 5 publications

(4 citation statements)

References 14 publications

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“…FFNNs are usually used to solve non-dynamic modeling problems [1]. MLPs are the most popularly used FFNN structures, which are widely used in microwave modeling for both passive component modeling [7], [8], [9], [10], [15], [17], [18], [19], [22], [30], [31], [34], [76], [80], [83], [92], [93], [94], [95], [96], [97], [98], [99], [100], [101], [102], [103], [104], [105] and active device/circuit modeling [12], [13], [23], [24], [25], [29], [32], [33], [106], [107], [108], [109], [110], [111], [112], [113], [114], [115],…”

Section: A Feedforward Neural Networkmentioning

confidence: 99%

“…Increased research interests of ANN in the microwave community in the 1990s led to special issues on applications of ANN in microwave CAD (1999 and 2002 special issues of the Int. J. RF Microwave CAE) covering more topics, such as ANN structures and training [14], [15], [16], EM design acceleration [17], [18], [19], microwave filter design [17], [20], [21], [22], microwave amplifier design [23], [24], [25], [26], intermodulation distortion [27], coplanar waveguide (CPW) [28], microwave device modeling [29], microstrip antennas [30], [31], large-signal analysis [32], [33], multiconductor transmission line [34], and microwave This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/ measurements [35].…”

Section: Introductionmentioning

confidence: 99%

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

Feng

Jin

et al. 2022

IEEE Trans. Microwave Theory Techn.

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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. Knowledgebased 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 largescale 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).

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Section: A Feedforward Neural Networkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Feng

Jin

et al. 2022

IEEE Trans. Microwave Theory Techn.

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“…Although these EM simulation methods are powerful for RF device analyses these approaches suffer from a severe problem, i.e., they are very time consuming. Some studies have used artificial neural network (ANN) for assessment of electromagnetic field radiating by electrostatic discharges [1] or design of microwave circuits [2]. In these studies, shallow neural networks (with only one hidden layer in neural network) have been used.…”

Section: Introductionmentioning

confidence: 99%

Modeling Radio-Frequency Devices Based on Deep Learning Technique

2021

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An advanced method of modeling radio-frequency (RF) devices based on a deep learning technique is proposed for accurate prediction of S parameters. The S parameters of RF devices calculated by full-wave electromagnetic solvers along with the metallic geometry of the structure, permittivity and thickness of the dielectric layers of the RF devices are used partly as the training and partly as testing data for the deep learning structure. To implement the training procedure efficiently, a novel selection method of training data considering critical points is introduced. In order to rapidly and accurately map the geometrical parameters of the RF devices to the S parameters, deep neural networks are used to establish the multiple non-linear transforms. The hidden-layers of the neural networks are adaptively chosen based on the frequency response of the RF devices to guarantee the accuracy of generated model. The Adam optimization algorithm is utilized for the acceleration of training. With the established deep learning model of a parameterized device, the S parameters can efficiently be obtained when the device geometrical parameters change. Comparing with the traditional modeling method that uses shallow neural networks, the proposed method can achieve better accuracy, especially when the training data are non-uniform. Three RF devices, including a rectangular inductor, an interdigital capacitor, and two coupled transmission lines, are used for building and verifying the deep neural network. It is shown that the deep neural network has good robustness and excellent generalization ability. Even for very wide frequency band (0–100 GHz), the maximum relative error of the coupled transmission lines using the proposed method is below 3%.

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“…However, the resources of this straightforward approach seem to be far from exhausted. Examples of incorporation of universal frequency‐domain and time‐domain modeling software with suitable advanced optimization procedures include the finite element method (FEM) package HFSS coupled with a genetic algorithm [7], the TLM simulator MEFiSTo combined with an artificial neural network (ANN) scheme [8], the (2.5‐D) method of moments solver IE3D operating in conjunction with a genetic algorithm optimizer [9], and several algorithms combining in‐house FEM and finite‐difference time‐domain (FDTD) solvers with ANN techniques [10–15]. These works have proven the functionality of the concept and identified MATLAB as an excellent computing framework for controlling simulators and conveniently implementing optimization algorithms.…”

Section: Introductionmentioning

confidence: 99%

Neural network optimization of complex microwave structures with a reduced number of full-wave analyses

Murphy

Yakovlev

2011

Int J RF and Microwave Comp Aid Eng

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An innovative technique of neural network optimization of substantially less computational cost is presented as a procedure suitable for viable computer-aided design of complex microwave systems. The number of necessary full-wave simulations in this optimization procedure is dramatically reduced because of a special objective function (OF) and constrained optimization response surface (CORS) sampling in the dynamic training of the decomposed radial-basis-function network. A high rate of convergence to an optimization objective is shown to be dependent on the combined effect of the OF and CORS technique. Performance of the algorithm is illustrated by its application to a waveguide band-pass filter, a loaded microwave oven, and a dielectric resonator antenna; their optimal designs are found from five-parameter optimizations requiring as little as 167, 177, and 99 analyses, respectively.

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A coupled FDTD-artificial neural network technique for large-signal analysis of microwave circuits

Cited by 5 publications

References 14 publications

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

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

Modeling Radio-Frequency Devices Based on Deep Learning Technique

Neural network optimization of complex microwave structures with a reduced number of full-wave analyses

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