scholarly journals Green’s Functions of Multi-Layered Plane Media with Arbitrary Boundary Conditions and Its Application on the Analysis of the Meander Line Slow-Wave Structure

Electronics ◽  
2021 ◽  
Vol 10 (21) ◽  
pp. 2716
Author(s):  
Zheng Wen ◽  
Jirun Luo ◽  
Wenqi Li

A method was proposed for solving the dyadic Green’s functions (DGF) and scalar Green’s functions (SGF) of multi-layered plane media in this paper. The DGF and SGF were expressed in matrix form, where the variables of the boundary conditions (BCs) can be separated in matrix form. The obtained DGF and SGF are in explicit form and suitable for arbitrary boundary conditions, owing to the matrix form expression and the separable variables of the BCs. The Green’s functions with typical BCs were obtained, and the dispersion characteristic of the meander line slow-wave structure (ML-SWS) is analyzed based on the proposed DGF. The relative error between the theoretical results and the simulated ones with different relative permittivity is under 3%, which demonstrates that the proposed DGF is suitable for electromagnetic analysis to complicated structure including the ML-SWS.

Author(s):  
Hexin Wang ◽  
Shaomeng Wang ◽  
Zhanliang Wang ◽  
Xinyi Li ◽  
Duo Xu ◽  
...  

2020 ◽  
Author(s):  
Zheng Wen ◽  
Jirun Luo ◽  
Yu Fan ◽  
Chen Yang ◽  
Fang Zhu ◽  
...  

2012 ◽  
Vol 59 (5) ◽  
pp. 1551-1557 ◽  
Author(s):  
Fei Shen ◽  
Yanyu Wei ◽  
Xiong Xu ◽  
Yang Liu ◽  
Minzhi Huang ◽  
...  

Electronics ◽  
2021 ◽  
Vol 10 (19) ◽  
pp. 2430
Author(s):  
Yijun Zhu ◽  
Yang Xie ◽  
Ningfeng Bai ◽  
Xiaohan Sun

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 0.005c, 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.


2019 ◽  
Vol 47 (10) ◽  
pp. 4650-4657 ◽  
Author(s):  
Shaomeng Wang ◽  
Sheel Aditya ◽  
Xin Xia ◽  
Zishan Ali ◽  
Jianmin Miao ◽  
...  

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