A Beam-Splitting Bianisotropic Metasurface Designed by Optimization and Machine Learning
Electromagnetic metasurfaces have attracted significant interest recently due to their low profile and advantageous applications. Practically, many metasurface designs start with a set of constraints for the radiated far-field, such as main-beam direction(s) and side lobe levels, and end with a non-...
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Open Journal of Antennas and Propagation |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9826784/ |
| _version_ | 1818041663592857600 |
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| author | Stewart Pearson Parinaz Naseri Sean V. Hum |
| author_facet | Stewart Pearson Parinaz Naseri Sean V. Hum |
| author_sort | Stewart Pearson |
| collection | DOAJ |
| description | Electromagnetic metasurfaces have attracted significant interest recently due to their low profile and advantageous applications. Practically, many metasurface designs start with a set of constraints for the radiated far-field, such as main-beam direction(s) and side lobe levels, and end with a non-uniform physical structure for the surface. This problem is quite challenging, since the required tangential field transformations are not completely known when only constraints are placed on the scattered fields. Hence, the required surface properties cannot be solved for analytically. Moreover, the translation of the desired surface properties to the physical unit cells can be time-consuming and difficult, as it is often a one-to-many mapping in a large solution space. Here, we divide the inverse design process into two steps: a macroscopic and microscopic design step. In the former, we use an iterative optimization process to find the surface properties that radiate a far-field pattern that complies with specified constraints. This iterative process exploits non-radiating currents to ensure a passive and lossless design. In the microscopic step, these optimized surface properties are realized with physical unit cells using machine learning surrogate models. The effectiveness of this end-to-end synthesis process is demonstrated through measurement results of a beam-splitting prototype. |
| first_indexed | 2024-12-10T08:34:00Z |
| format | Article |
| id | doaj.art-b6eff0cb74984efa8ec45cf571111288 |
| institution | Directory Open Access Journal |
| issn | 2637-6431 |
| language | English |
| last_indexed | 2024-12-10T08:34:00Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Open Journal of Antennas and Propagation |
| spelling | doaj.art-b6eff0cb74984efa8ec45cf5711112882022-12-22T01:56:01ZengIEEEIEEE Open Journal of Antennas and Propagation2637-64312022-01-01379881110.1109/OJAP.2022.31902249826784A Beam-Splitting Bianisotropic Metasurface Designed by Optimization and Machine LearningStewart Pearson0https://orcid.org/0000-0003-1849-2915Parinaz Naseri1https://orcid.org/0000-0003-2196-0097Sean V. Hum2https://orcid.org/0000-0002-8797-0039The Edward S. Rogers Sr. Department of Electrical and Computer Engineering, University of Toronto, Toronto, CanadaThe Edward S. Rogers Sr. Department of Electrical and Computer Engineering, University of Toronto, Toronto, CanadaThe Edward S. Rogers Sr. Department of Electrical and Computer Engineering, University of Toronto, Toronto, CanadaElectromagnetic metasurfaces have attracted significant interest recently due to their low profile and advantageous applications. Practically, many metasurface designs start with a set of constraints for the radiated far-field, such as main-beam direction(s) and side lobe levels, and end with a non-uniform physical structure for the surface. This problem is quite challenging, since the required tangential field transformations are not completely known when only constraints are placed on the scattered fields. Hence, the required surface properties cannot be solved for analytically. Moreover, the translation of the desired surface properties to the physical unit cells can be time-consuming and difficult, as it is often a one-to-many mapping in a large solution space. Here, we divide the inverse design process into two steps: a macroscopic and microscopic design step. In the former, we use an iterative optimization process to find the surface properties that radiate a far-field pattern that complies with specified constraints. This iterative process exploits non-radiating currents to ensure a passive and lossless design. In the microscopic step, these optimized surface properties are realized with physical unit cells using machine learning surrogate models. The effectiveness of this end-to-end synthesis process is demonstrated through measurement results of a beam-splitting prototype.https://ieeexplore.ieee.org/document/9826784/metasurfacessurface wavesinverse designmachine learningnon-uniform metasurfaceoptimization |
| spellingShingle | Stewart Pearson Parinaz Naseri Sean V. Hum A Beam-Splitting Bianisotropic Metasurface Designed by Optimization and Machine Learning IEEE Open Journal of Antennas and Propagation metasurfaces surface waves inverse design machine learning non-uniform metasurface optimization |
| title | A Beam-Splitting Bianisotropic Metasurface Designed by Optimization and Machine Learning |
| title_full | A Beam-Splitting Bianisotropic Metasurface Designed by Optimization and Machine Learning |
| title_fullStr | A Beam-Splitting Bianisotropic Metasurface Designed by Optimization and Machine Learning |
| title_full_unstemmed | A Beam-Splitting Bianisotropic Metasurface Designed by Optimization and Machine Learning |
| title_short | A Beam-Splitting Bianisotropic Metasurface Designed by Optimization and Machine Learning |
| title_sort | beam splitting bianisotropic metasurface designed by optimization and machine learning |
| topic | metasurfaces surface waves inverse design machine learning non-uniform metasurface optimization |
| url | https://ieeexplore.ieee.org/document/9826784/ |
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