Abstract

We present a new machine learning (ML) deep learning (DL) synthesis algorithm for the design of a microstrip meander line (MML) slow wave structure (SWS). Exact numerical simulation data are used in the training of our network as a form of supervised learning. The learning results show that the training mean squared error is as low as 5.23 × 10−2 when using 900 sets of data. When the desired performance is reached, workable geometry parameters can be obtained by this algorithm. A D-band MML SWS with 20 GHz bandwidth at 160 GHz center frequency is then designed using the auto-design neural network (ADNN). A cold test shows that its phase velocity varies by 0.005 c, and the transmission rate of a 50-period SWS is greater than −5 dB with the reflectivity below −15 dB when the frequency is from 150 to 170 GHz. Particle-in-cell (PIC) simulation also illustrates that a maximum power of 3.2 W is reached at 160 GHz with 34.66 dB gain and output power greater than 1 W from 152 to 168 GHz.

Highlights

  • Introduction with XGBoost and Neural NetworkHigh-frequency millimeter-wave communication is receiving increasing attention due to the development of 6G and next-generation communication networks [1]

  • This paper aims to rapidly design an microstrip meander line (MML) slow wave structure (SWS) according to the target center frequency and bandwidth using our proposed algorithm

  • An MML SWS is designed for particle-in –cell (PIC) simulations using the parameters obtained by this method, and the results show that the obtained parameters work well for the MML SWS

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Summary

Introduction

High-frequency millimeter-wave communication is receiving increasing attention due to the development of 6G and next-generation communication networks [1]. It is difficult to realize high-power millimeter-wave sources over the W-band because of the power limitation of solid-state devices [2]. As a core component of high-power microwave devices, the traveling wave tube (TWT) has a wide range of applications in millimeter-wave fields, and has been applied in terahertz wave transmission systems [3]. Compared with solid-state devices, TWTs have obvious advantages at high frequencies [4]. It is necessary to carry out research on high-frequency TWT technology to promote the development of high-frequency millimeter-wave technology [5]. Among various types of TWTs, spiral and folded waveguides are the most common slow wave structure (SWS)

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