Neural Network Modeling of Antennas on Package for 5G Applications

Date

2023-01-01

Author

Saçin, Ekin Su
Durgun, Ahmet Cemal

Metadata

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The design and optimization of microwave devices require rigorous electromagnetic (EM) analysis requiring excessive computational resources. To mitigate this, neural network based machine learning methods can be used to generate surrogate models for EM performance analysis. In this study, we constructed a spectral transposed convolutional neural network model for a microstrip patch antenna printed on a package. The model takes geometrical parameters and material properties of the antenna as inputs and generates S11 parameter within the band of 23-33 GHz as output. The results showed a very good correlation between simulations and predictions. This verifies that NN based models can be used for EM analysis of antennas, particularly at the earlier stages of a design process.

Subject Keywords

5G, Antenna on Package, Machine Learning, Microstrip Patch Antenna, Neural Network

URI

https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85162193599&origin=inward
https://hdl.handle.net/11511/104658

DOI

https://doi.org/10.23919/eucap57121.2023.10133407

Conference Name

17th European Conference on Antennas and Propagation, EuCAP 2023

Collections

Department of Electrical and Electronics Engineering, Conference / Seminar

Citation Formats

E. S. Saçin and A. C. Durgun, “Neural Network Modeling of Antennas on Package for 5G Applications,” presented at the 17th European Conference on Antennas and Propagation, EuCAP 2023, Florence, İtalya, 2023, Accessed: 00, 2023. [Online]. Available: https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85162193599&origin=inward.