Abstract
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%.
Highlights
Electromagnetic (EM) simulation approaches, such as method of moments (MoM), which is a numerical computational method that transforms Maxwell’s equations into integral matrix equations, obtain the EM performance by solving the dense matrix equation.The finite-difference time-domain (FDTD) method solved Maxwell’s equations in an explicit way
The metallic geometry of the structure, and the permittivity and thickness of the dielectric layers are revised during the sweeping process
The geometrical parameters of the RF device are used as the input data of the neural network, while the S parameters of the RF device is the output of the neural network
Summary
Electromagnetic (EM) simulation approaches, such as method of moments (MoM), which is a numerical computational method that transforms Maxwell’s equations into integral matrix equations, obtain the EM performance by solving the dense matrix equation. Optimization algorithms, such as stochastic gradient descent (SGD), Newton’s method, or Levenberg–Marquardt (LM) algorithm, have been used for the training process These optimization methods are still unsuitable for deep learning training, in which there are a large amount of training data and complex neural network structures. There are many classic optimization algorithms to training the neural network, such as stochastic gradient descent (SGD) [6], Newton’s method [7] or Levenberg–Marquardt (LM) algorithm [8]. They all have some drawbacks, which cause them not suitable for training DNN in various ways. An example of two coupled transmission lines is used to validate the accuracy of this method in a very wide frequency band range
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