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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MDPI AG
2021-07-01
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Series: | Electronics |
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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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issn | 2079-9292 |
language | English |
last_indexed | 2024-03-10T09:41:08Z |
publishDate | 2021-07-01 |
publisher | MDPI AG |
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series | Electronics |
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 |