Optimizing structured surfaces for diffractive waveguides
Abstract We introduce universal diffractive waveguide designs that can match the performance of conventional dielectric waveguides and achieve various functionalities. Optimized using deep learning, diffractive waveguides can be cascaded to form any desired length and are comprised of transmissive d...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-06-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-025-60626-3 |
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| _version_ | 1849725065543811072 |
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| author | Yuntian Wang Yuhang Li Tianyi Gan Kun Liao Mona Jarrahi Aydogan Ozcan |
| author_facet | Yuntian Wang Yuhang Li Tianyi Gan Kun Liao Mona Jarrahi Aydogan Ozcan |
| author_sort | Yuntian Wang |
| collection | DOAJ |
| description | Abstract We introduce universal diffractive waveguide designs that can match the performance of conventional dielectric waveguides and achieve various functionalities. Optimized using deep learning, diffractive waveguides can be cascaded to form any desired length and are comprised of transmissive diffractive surfaces that permit the propagation of desired modes with low loss and high mode purity. In addition to guiding the targeted modes through cascaded diffractive units, we also developed various waveguide components and introduced bent diffractive waveguides, rotating the direction of mode propagation, as well as spatial and spectral mode filtering and mode splitting diffractive waveguide designs, and mode-specific polarization control. This framework was experimentally validated in the terahertz spectrum to selectively pass certain spatial modes while rejecting others. Without the need for material dispersion engineering diffractive waveguides can be scaled to operate at different wavelengths, including visible and infrared spectrum, covering potential applications in, e.g., telecommunications, imaging, sensing and spectroscopy. |
| format | Article |
| id | doaj-art-874d2e32f43f42db99b23e9e255cbdd7 |
| institution | DOAJ |
| issn | 2041-1723 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj-art-874d2e32f43f42db99b23e9e255cbdd72025-08-20T03:10:34ZengNature PortfolioNature Communications2041-17232025-06-0116112110.1038/s41467-025-60626-3Optimizing structured surfaces for diffractive waveguidesYuntian Wang0Yuhang Li1Tianyi Gan2Kun Liao3Mona Jarrahi4Aydogan Ozcan5Electrical and Computer Engineering Department, University of CaliforniaElectrical and Computer Engineering Department, University of CaliforniaElectrical and Computer Engineering Department, University of CaliforniaElectrical and Computer Engineering Department, University of CaliforniaElectrical and Computer Engineering Department, University of CaliforniaElectrical and Computer Engineering Department, University of CaliforniaAbstract We introduce universal diffractive waveguide designs that can match the performance of conventional dielectric waveguides and achieve various functionalities. Optimized using deep learning, diffractive waveguides can be cascaded to form any desired length and are comprised of transmissive diffractive surfaces that permit the propagation of desired modes with low loss and high mode purity. In addition to guiding the targeted modes through cascaded diffractive units, we also developed various waveguide components and introduced bent diffractive waveguides, rotating the direction of mode propagation, as well as spatial and spectral mode filtering and mode splitting diffractive waveguide designs, and mode-specific polarization control. This framework was experimentally validated in the terahertz spectrum to selectively pass certain spatial modes while rejecting others. Without the need for material dispersion engineering diffractive waveguides can be scaled to operate at different wavelengths, including visible and infrared spectrum, covering potential applications in, e.g., telecommunications, imaging, sensing and spectroscopy.https://doi.org/10.1038/s41467-025-60626-3 |
| spellingShingle | Yuntian Wang Yuhang Li Tianyi Gan Kun Liao Mona Jarrahi Aydogan Ozcan Optimizing structured surfaces for diffractive waveguides Nature Communications |
| title | Optimizing structured surfaces for diffractive waveguides |
| title_full | Optimizing structured surfaces for diffractive waveguides |
| title_fullStr | Optimizing structured surfaces for diffractive waveguides |
| title_full_unstemmed | Optimizing structured surfaces for diffractive waveguides |
| title_short | Optimizing structured surfaces for diffractive waveguides |
| title_sort | optimizing structured surfaces for diffractive waveguides |
| url | https://doi.org/10.1038/s41467-025-60626-3 |
| work_keys_str_mv | AT yuntianwang optimizingstructuredsurfacesfordiffractivewaveguides AT yuhangli optimizingstructuredsurfacesfordiffractivewaveguides AT tianyigan optimizingstructuredsurfacesfordiffractivewaveguides AT kunliao optimizingstructuredsurfacesfordiffractivewaveguides AT monajarrahi optimizingstructuredsurfacesfordiffractivewaveguides AT aydoganozcan optimizingstructuredsurfacesfordiffractivewaveguides |