Verzunov S.N. Method for Optimizing Design Parameters of Microstrip Antenna Cell-resonators on the Basis of Intelligent Data Analysis

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Abstract

The designs of microstrip antennas are widely described in the literature and give good results in practice. However, since they are inherently low-efficient and limited in bandwidth, it is often necessary to optimize the design by changing its parameters and improving performance. One way to achieve this is to use metamaterials. However, the features of the metamaterial cells and their array make the analytical development of the electromagnetic field equations very complicated and impractical. Typically, this problem is solved by designing the antenna with many computationally expensive and time-consuming electromagnetic simulations. In this paper, the authors propose a new method for searching the optimal design of resonator cells for a microstrip antenna, which makes it possible to purposefully optimize the parameters of resonator cells, which significantly improve its electrical characteristics. The proposed method combines a regression model using a deep network based on fully connected neural layers with a search based on the COBYLA conditional optimization algorithm to find the optimal design parameters of resonator cells. First, a tensor that defines the parameters of the resonator cells is fed to the input of the neural network, and the network is trained to reduce the difference between the output predicted by the deep neural network and the corresponding electrical parameters of the antenna obtained as a result of a full numerical simulation in the CST MWS program. Next, new parameters of the resonator cells are generated through iterative optimization. This step combines the neural network trained earlier with the optimization algorithm and uses some search strategy to find optimal CSRR cell parameters. This approach fully automates the process of creating microstrip antennas with resonator cells based on metamaterials and allows you to find the parameters of the cells that provide the optimal operating mode in general and gives the maximum level of antenna radiation while maintaining the bandwidth. In this case, the current is redistributed between the substrate and the antenna patch towards the patch, which explains the obtained improvements in the antenna characteristics.

Keywords

metamaterials, microstrip cell antennas, CSSR cells, deep learning, antenna modeling, optimization, COBYLA, Matlab Antenna Toolbox, CST Microwave Studio

Sergey N. Verzunov Ph.D. (Engineering), Leading Researcher, Laboratory of Information and Measurement Systems, Machinery researching and Automatics Institute of Kyrgyz Republic National Academy of Sciences, This email address is being protected from spambots. You need JavaScript enabled to view it., https://orcid.org/0000-0003-3130-2776

  1. Bryakin, I. V., Bochkarev, I. V. Hybrid method of induction sounding for detection of underground cable lines and pipelines. Elektrotekhnicheskie sistemy i kompleksy [Electrotechnical systems and complexes], 2019, no. 2(43), pp. 70-78. (In Russian) doi: 10.18503/2311-8318-2019-2(43)-70-78
  2. Gianfagn A., Swaminathan M., Raj P.M., Tummala R., An-tonini G. Enabling Antenna Design with Nano-Magnetic Materials using Machine Learning. IEEE Nanotechnology Materials and Devices Conference. IEEE, 2015. P. 5. doi: 10.1109/NMDC.2015.7439256
  3. Suganthi, T. Kavitha, V. Ravindra Survey on metamaterial antennas. IOP Conference Series: Materials Science and Engineering. IOP, 2021, vol. 1070, no. 1, p. 12086. doi: 10.1088/1757-899X/1070/1/012086
  4. On H.-I., Jeong L., Jung M., Kang D.-J., Park J.-H., Lee H.-J. Optimal design of microwave absorber using novel variational autoencoder from a latent space search strategy. Materials & Design, 2021, vol. 212, p. 110266. doi: 10.1016/j.matdes.2021.110266
  5. Sun K., Fan R., Zhang X., Zhang Z., Shi Z. An overview of metamaterials and their achievements in wireless power transfer. Journal of Materials Chemistry, 2018, vol. 6, no. 12, pp. 2925–2943. doi: 10.1039/C7TC03384B
  6. Landy N.I., Sajuyigbe S., Mock J.J., Smith D.R., Padilla W.J. Perfect metamaterial absorber. Phys. Rev. Lett. 2008, vol. 100, pp. 1–4. doi: 10.1103/PhysRevLett.100.207402
  7. Mei H., Yang W., Zhao X., Yao L., Yao Y., Chen C., Cheng L. Insitu growth of SiC nanowires and carbon nanotubes on 3D printed metamaterial structures to enhance electromagnetic wave absorption. Materials & Design. 2021, vol. 197, p. 109271. doi: 10.1016/j.matdes.2020.109271
  8. Al-Nuaimi M.K., Whittow W.G. Compact microstrip band stop filter using CSRR and CCSRR: Design, simulation and results. Proceedings of the Fourth European Conference on Antennas and Propagation. IEEE, 2010, pp. 1-5.
  9. So S., Rho J., Designing nanophotonic structures using conditional deep convolutional generative adversarial networks. Nanophotonics. 2019, vol. 8, pp. 1255–1261. doi: 10.1515/nanoph-2019-0117
  10. Liu Z., Zhu D., Rodrigues S.P., Lee K.T., Cai W. Generative Model for the Inverse Design of Metasurfaces. Nano Lett. 2018, vol. 18, pp. 6570–6576. doi: 10.1021/acs.nanolett.8b03171
  11. Kollmann H.T., Abueidda D.W., Koric S., Guleryuz E., Sobh N.A. Deep learning for topology optimization of 2D metamaterials. Materials & Design, 2020, vol. 196, p. 109098. doi: 10.1016/j.matdes.2020.109098
  12. Balanis C.A. Antenna Theory: Analysis and Design. John Wiley & Sons Inc., 2005.
  13. Tipikin A. Modelirovanie antennykh ustroistv v Matlab s ispolzovaniem paketa rasshireniya AntennaToolbox [Modeling antenna devices in Matlab using the Antenna Toolbox extension package]. Moscow, Solon-Press Publ., 2016, 116 p. (in Russian)
  14. Kasimova A. U., Verzunov S. N. Review and analysis of modern methods for measuring the dielectric permittivity of rocks, Problemy avtomatiki i upravleniya. [Automation and control issues], 2022, no. 1, vol. 43, pp. 33-49. (in Russian)
  15. http://cbd.minjust.gov.kg/act/view/ru-ru/11510 (accessed 28 April 2022) (In Russian)
  16. Vaz R. Optimization of microstrip antenna designs: simulation of CSRR metamaterial cells with machine learning. Monograph (Graduate) – Electrical Engineering, Federal University of Tocantins, Palmas, 2019.
  17. Simuleon B.V. SIMULIA CST Studio Suite. Pettelaarpark 84 5216 PP‘s-Hertogenbosch, Netherlands: Electromagnetic Field Software. 2021. URL: https://www.simuleon.com/simulia-cst-studio-suite/ (accessed 2021 September 24)
  18. Sarkar S. B. Design and Analysis of 5.2 GHz Rectangular Microstrip Patch Array Antenna using Split Ring Resonator. 4th International Conference on Signal Processing, Commu-nications and Networking. IEEE, 2017, pp. 1-5. doi: 10.1109/ICSCN.2017.8085654
  19. Powell M.J.D. A direct search optimization method that models the objective and constraint functions by linear inter-polation. Advances in Optimization and Numerical Analysis. 1994, pp. 51-67. doi: 10.1007/978-94-015-8330-5_4
  20. Slyusar V. Metamaterials in antenna technology: basic prin-ciples and results, Pervaya Milya. [Last Mile]. (Supplement to the journal "Electronics: Science, Technology, Business"), 2010, no. 3-4, pp. 44-60. (in Russian)

Verzunov S.N. Method for Optimizing Design Parameters of Microstrip Antenna Cellresonators on the Basis of Intelligent Data Analysis. Elektrotekhnicheskie sistemy i kompleksy [Electrotechnical Systems and Complexes], 2022, no. 3(56), pp. 54-64. (In Russian). https://doi.org/10.18503/2311-8318-2022-3(56)-54-64

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