Modeling Radio-Frequency Devices Based on Deep Learning Technique

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 thick...

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Main Authors: Zhimin Guan, Peng Zhao, Xianbing Wang, Gaofeng Wang
Format: Article
Language:English
Published: MDPI AG 2021-07-01
Series:Electronics
Subjects:
Online Access:https://www.mdpi.com/2079-9292/10/14/1710
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author Zhimin Guan
Peng Zhao
Xianbing Wang
Gaofeng Wang
author_facet Zhimin Guan
Peng Zhao
Xianbing Wang
Gaofeng Wang
author_sort Zhimin Guan
collection DOAJ
description 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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spelling doaj.art-702b7ee354874fa599becfa3c70dabd22023-11-22T03:38:54ZengMDPI AGElectronics2079-92922021-07-011014171010.3390/electronics10141710Modeling Radio-Frequency Devices Based on Deep Learning TechniqueZhimin Guan0Peng Zhao1Xianbing Wang2Gaofeng Wang3Key Lab of RF Circuits and Systems of Ministry of Education, School of Electronics and Information, Hangzhou Dianzi University, Hangzhou 310018, ChinaKey Lab of RF Circuits and Systems of Ministry of Education, School of Electronics and Information, Hangzhou Dianzi University, Hangzhou 310018, ChinaKey Lab of RF Circuits and Systems of Ministry of Education, School of Electronics and Information, Hangzhou Dianzi University, Hangzhou 310018, ChinaKey Lab of RF Circuits and Systems of Ministry of Education, School of Electronics and Information, Hangzhou Dianzi University, Hangzhou 310018, ChinaAn 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%.https://www.mdpi.com/2079-9292/10/14/1710RF device modelingparameterized geometryS parametersuniform/non-uniform samplingdeep learningdeep neural network
spellingShingle Zhimin Guan
Peng Zhao
Xianbing Wang
Gaofeng Wang
Modeling Radio-Frequency Devices Based on Deep Learning Technique
Electronics
RF device modeling
parameterized geometry
S parameters
uniform/non-uniform sampling
deep learning
deep neural network
title Modeling Radio-Frequency Devices Based on Deep Learning Technique
title_full Modeling Radio-Frequency Devices Based on Deep Learning Technique
title_fullStr Modeling Radio-Frequency Devices Based on Deep Learning Technique
title_full_unstemmed Modeling Radio-Frequency Devices Based on Deep Learning Technique
title_short Modeling Radio-Frequency Devices Based on Deep Learning Technique
title_sort modeling radio frequency devices based on deep learning technique
topic RF device modeling
parameterized geometry
S parameters
uniform/non-uniform sampling
deep learning
deep neural network
url https://www.mdpi.com/2079-9292/10/14/1710
work_keys_str_mv AT zhiminguan modelingradiofrequencydevicesbasedondeeplearningtechnique
AT pengzhao modelingradiofrequencydevicesbasedondeeplearningtechnique
AT xianbingwang modelingradiofrequencydevicesbasedondeeplearningtechnique
AT gaofengwang modelingradiofrequencydevicesbasedondeeplearningtechnique