Efficient Modeling of Ku-Band High Power Dielectric Resonator Filter With Applications of Neural Networks

Li, ShuQi; Wang, Ying; Yu, Ming; Panariello, A.

doi:10.1109/tmtt.2019.2921359

Cited by 13 publications

(5 citation statements)

References 20 publications

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“…A novel deep-q-network (DQN)-based fine-tuning approach is developed to reduce the computation time even with different initial values [143]. Specific KBNN-based approaches are developed for different filter applications, for example, high-power dielectric resonator filters [199], narrow-bandwidth four-pole Ku-band bandpass filters [200], and even multiplexers [201].…”

Section: Filter Modeling and Designmentioning

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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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: Filter Modeling and Designmentioning

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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“…However, it is difficult to train the neural network for a high-order filter because it corresponding to a high-dimensional space mapping relationship and a large amount of training data will be used. To solve this problem, the segmentation finite element (SFE) method 22 can be used to decompose the whole filter into substructures, which will reduce the dimension of the input-output relationship, and several examples of this method to decompose the filter are shown in References [2,4,16]. We assume that the input of a filter to be an m-dimensional vector as follows:…”

Section: Filter Decompositionmentioning

confidence: 99%

“…1 Once a neural network is trained well, not only can it be used repeatedly, but also its response speed is beyond the reach of EM simulation. For these reasons, neural networks have been used for various filter modeling and design applications, including dielectric resonator filter, 2 rectangular waveguide Hplane iris bandpass filter, 3,4 microstrip bandpass filters, 5 and so on.…”

Section: Introductionmentioning

confidence: 99%

Neural network of calibrated coarse model and application to substrate integrated waveguide filter design

Jin

2020

Int J RF Microw Comput Aided Eng

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In this article, we propose a novel neural network of calibrated coarse model, which can obtain the optimal filter response with as little training data as possible to synthesize the entire substrate integrated waveguide (SIW) filter. By incorporating the knowledge of filter decomposition with the inverse neural network, we build a coarse model that can synthesize the dimensions of a SIW filter. However, the SIW structures are subject to a potential leakage problem due to the periodic gaps, the results of the coarse model are very different from the ideal response. We propose a novel calibrated neural network from the perspective of the coupling matrix to correct the errors generated in the coarse model. In addition, this article also proposes an equivalent de‐embedding technique, which is simpler than the thru‐reflect‐line calibration technique to accurately extract the scattering parameters of the SIW discontinuities. An H‐plane fifth order SIW filter is synthesized by the proposed model. The result shows that the SIW filter that is very close to the ideal response can be synthesized with only a few hundred training data.

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“…Several ANN‐based RF modeling works have been reported in the literature for different applications, such as coplanar waveguide (CPW) components, 21 spiral inductors, 22 HEMTs, 23–28 amplifiers, 29–31 antennas, 32 and filters 33–35 . However, some issues have yet to be solved to enhance its usefulness in the real applications of high‐frequency circuit design.…”

Section: Introductionmentioning

confidence: 99%

Optimization of ANN ‐based models and its EM co‐simulation for printed RF devices

Yang

Khusro

et al. 2021

Int J RF Mic Comp-Aid Eng

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Printed VO2 RF switch founds immense potential in RF reconfigurable applications. However, their generic electrical equivalent model is still intangible that can be further integrated in CAD tools and utilize for simulation, analysis and design of RF/microwave circuits and systems. The artificial neural network (ANN) has been gaining popularity in modeling various types of RF components. However, most of these works merely demonstrate the establishment of the ANN‐based RF model in the MATLAB environment without involving significant optimization. Furthermore, the integration of such ANN‐based RF models in the EM and circuit simulator as well as the co‐simulation between the ANN‐based model and conventional models have not been demonstrated or validated. Therefore, the earlier reported models are still one step removed from its real RF applications. In this work, by using the fully printed vanadium dioxide (VO2) RF switch as the modeling example, a systematic hyperparameter optimization process has been conducted. Compared to the non‐optimized ANN model, a dramatic improvement in the model's accuracy has been observed for the ANN model with fully optimized hyperparameters. A correlation coefficient of more than 99.2% for broad frequency range demonstrates the accuracy of the modeling technique. In addition, we have also integrated the Python‐backed ANN‐based model into Advanced Design System (ADS), where a reconfigurable T‐resonator band stop filter is used as an example to demonstrate the co‐simulation between the ANN‐based model and the conventional lumped‐based model.

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Efficient Modeling of Ku-Band High Power Dielectric Resonator Filter With Applications of Neural Networks

Cited by 13 publications

References 20 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

Neural network of calibrated coarse model and application to substrate integrated waveguide filter design

Optimization of ANN ‐based models and its EM co‐simulation for printed RF devices

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