A Time Delay Neural Network Based Technique for Nonlinear Microwave Device Modeling.

Wenyuan Liu,Feng Feng,Qi-Jun Zhang,Lin Zhu,Qian Lin,Wei Zhang,Gaohua Liu

doi:10.3390/mi11090831

Abstract

This paper presents a nonlinear microwave device modeling technique that is based on time delay neural network (TDNN). The proposed technique can accurately model the nonlinear microwave devices when compared to static neural network modeling method. A new formulation is developed to allow for the proposed TDNN model to be trained with DC, small-signal, and large signal data, which can enhance the generalization of the device model. An algorithm is formulated to train the proposed TDNN model efficiently. This proposed technique is verified by GaAs metal-semiconductor-field-effect transistor (MESFET), and GaAs high-electron mobility transistor (HEMT) examples. These two examples demonstrate that the proposed TDNN is an efficient and valid approach for modeling various types of nonlinear microwave devices.

Highlights

Artificial neural network (ANN) is a recognized tool for modeling and design optimization in RF and microwave computer-aided design (CAD) [1,2,3,4,5,6,7,8,9]. This technique has been successfully used in parametric modeling of microwave components [10,11,12], electromagnetic (EM) optimization [13,14], parasitic modeling [15], nonlinear device modeling [16,17,18], nonlinear microwave circuit optimization [19,20,21,22], power amplifier modeling [23,24,25], and more
Nonlinear device modeling is an important area of CAD and a variety of device models have been built
We propose a time delay neural network (TDNN) technique for nonlinear microwave device modeling using DC, small-signal, and large-signal information for the first time

Summary

Introduction

Artificial neural network (ANN) is a recognized tool for modeling and design optimization in RF and microwave computer-aided design (CAD) [1,2,3,4,5,6,7,8,9]. The parameters in the equivalent circuit need repetitively changes and sometimes the parameters are mutually contradictory When it comes to a new device, it is time consuming to build a nonlinear model that is based on equivalent modeling technique. We propose a time delay neural network (TDNN) technique for nonlinear microwave device modeling using DC, small-signal, and large-signal information for the first time. We propose an analytical formulation of TDNN for nonlinear device modeling using DC, bias-dependent S-parameter data, and large-signal harmonic balance (HB) data. In Equation (3), the derivative of fANN can be obtained using the adjoint neural network method [27], and k represents the index of delay buffers. The proposed technique can accurately model the nonlinear behavior of the device by training the TDNN model with DC, S-parameter, and HB data. Because of the neural network universal approximation capability [1], such TDNN model can achieve satisfied accuracy

An Algorithm for Training the Proposed TDNN Model

30 Hidden Neurons