W-band frequency selective digital metasurface using active learning-based binary optimization
The W-band is essential for applications like high-resolution imaging and advanced monitoring systems, but high-frequency signal attenuation leads to poor signal-to-noise ratios, posing challenges for compact and multi-channel systems. This necessitates distinct frequency selective surfaces (FSS) on...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
De Gruyter
2025-02-01
|
| Series: | Nanophotonics |
| Subjects: | |
| Online Access: | https://doi.org/10.1515/nanoph-2024-0628 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850106286243315712 |
|---|---|
| author | Kim Young-Bin Park Jaehyeon Kim Jun-Young Seo Seok-Beom Kim Sun-Kyung Lee Eungkyu |
| author_facet | Kim Young-Bin Park Jaehyeon Kim Jun-Young Seo Seok-Beom Kim Sun-Kyung Lee Eungkyu |
| author_sort | Kim Young-Bin |
| collection | DOAJ |
| description | The W-band is essential for applications like high-resolution imaging and advanced monitoring systems, but high-frequency signal attenuation leads to poor signal-to-noise ratios, posing challenges for compact and multi-channel systems. This necessitates distinct frequency selective surfaces (FSS) on a single substrate, a complex task due to inherent substrate resonance modes. In this study, we use a digital metasurface platform to design W-band FSS on a glass substrate, optimized through binary optimization assisted by active learning. The digital metasurface is composed of a periodic array of sub-wavelength unit cells, each containing hundreds of metal or dielectric pixels that act as binary states. By utilizing a machine learning model, we apply active learning-aided binary optimization to determine the optimal binary state configurations for a given target FSS profile. Specifically, we identify optimal designs for distinct FSS on a conventional glass substrate, with transmittance peaks at 79.3 GHz and Q-factors of 32.7. |
| format | Article |
| id | doaj-art-0cee8c5c47a64002b1df2c916eeb56a0 |
| institution | OA Journals |
| issn | 2192-8614 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | De Gruyter |
| record_format | Article |
| series | Nanophotonics |
| spelling | doaj-art-0cee8c5c47a64002b1df2c916eeb56a02025-08-20T02:38:52ZengDe GruyterNanophotonics2192-86142025-02-0114101597160610.1515/nanoph-2024-0628W-band frequency selective digital metasurface using active learning-based binary optimizationKim Young-Bin0Park Jaehyeon1Kim Jun-Young2Seo Seok-Beom3Kim Sun-Kyung4Lee Eungkyu5Department of Applied Physics, Kyung Hee University, Yongin-Si, Gyeonggi-Do, 17104, Republic of KoreaDepartment of Electronic Engineering, Kyung Hee University, Yongin-Si, Gyeonggi-Do, 17104, Republic of KoreaDepartment of Applied Physics, Kyung Hee University, Yongin-Si, Gyeonggi-Do, 17104, Republic of KoreaDepartment of Applied Physics, Kyung Hee University, Yongin-Si, Gyeonggi-Do, 17104, Republic of KoreaDepartment of Applied Physics, Kyung Hee University, Yongin-Si, Gyeonggi-Do, 17104, Republic of KoreaDepartment of Electronic Engineering, Kyung Hee University, Yongin-Si, Gyeonggi-Do, 17104, Republic of KoreaThe W-band is essential for applications like high-resolution imaging and advanced monitoring systems, but high-frequency signal attenuation leads to poor signal-to-noise ratios, posing challenges for compact and multi-channel systems. This necessitates distinct frequency selective surfaces (FSS) on a single substrate, a complex task due to inherent substrate resonance modes. In this study, we use a digital metasurface platform to design W-band FSS on a glass substrate, optimized through binary optimization assisted by active learning. The digital metasurface is composed of a periodic array of sub-wavelength unit cells, each containing hundreds of metal or dielectric pixels that act as binary states. By utilizing a machine learning model, we apply active learning-aided binary optimization to determine the optimal binary state configurations for a given target FSS profile. Specifically, we identify optimal designs for distinct FSS on a conventional glass substrate, with transmittance peaks at 79.3 GHz and Q-factors of 32.7.https://doi.org/10.1515/nanoph-2024-0628digital metasurfacefrequency selective surfaceactive learningbinary optimization |
| spellingShingle | Kim Young-Bin Park Jaehyeon Kim Jun-Young Seo Seok-Beom Kim Sun-Kyung Lee Eungkyu W-band frequency selective digital metasurface using active learning-based binary optimization Nanophotonics digital metasurface frequency selective surface active learning binary optimization |
| title | W-band frequency selective digital metasurface using active learning-based binary optimization |
| title_full | W-band frequency selective digital metasurface using active learning-based binary optimization |
| title_fullStr | W-band frequency selective digital metasurface using active learning-based binary optimization |
| title_full_unstemmed | W-band frequency selective digital metasurface using active learning-based binary optimization |
| title_short | W-band frequency selective digital metasurface using active learning-based binary optimization |
| title_sort | w band frequency selective digital metasurface using active learning based binary optimization |
| topic | digital metasurface frequency selective surface active learning binary optimization |
| url | https://doi.org/10.1515/nanoph-2024-0628 |
| work_keys_str_mv | AT kimyoungbin wbandfrequencyselectivedigitalmetasurfaceusingactivelearningbasedbinaryoptimization AT parkjaehyeon wbandfrequencyselectivedigitalmetasurfaceusingactivelearningbasedbinaryoptimization AT kimjunyoung wbandfrequencyselectivedigitalmetasurfaceusingactivelearningbasedbinaryoptimization AT seoseokbeom wbandfrequencyselectivedigitalmetasurfaceusingactivelearningbasedbinaryoptimization AT kimsunkyung wbandfrequencyselectivedigitalmetasurfaceusingactivelearningbasedbinaryoptimization AT leeeungkyu wbandfrequencyselectivedigitalmetasurfaceusingactivelearningbasedbinaryoptimization |