Nanowire design by deep learning for energy efficient photonic technologies
Fuente:
arXiv
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866908417851916288 |
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| author | Usman, Muhammad |
| author_facet | Usman, Muhammad |
| contents | This work describes our vision and proposal for the design of next generation photonic devices based on custom-designed semiconductor nanowires. The integration of multi-million-atom electronic structure and optical simulations with the supervised machine learning models will pave the way for transformative nanowire-based technologies, offering opportunities for the next generation energy-efficient greener photonics. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_10911 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Nanowire design by deep learning for energy efficient photonic technologies Usman, Muhammad Optics Materials Science This work describes our vision and proposal for the design of next generation photonic devices based on custom-designed semiconductor nanowires. The integration of multi-million-atom electronic structure and optical simulations with the supervised machine learning models will pave the way for transformative nanowire-based technologies, offering opportunities for the next generation energy-efficient greener photonics. |
| title | Nanowire design by deep learning for energy efficient photonic technologies |
| topic | Optics Materials Science |
| url | https://arxiv.org/abs/2501.10911 |