Next-Generation Multi-layer Metasurface Design: Hybrid Deep Learning Models for Beyond-RGB Reconfigurable Structural Colors
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arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866916388738695168 |
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| author | Abdelraouf, Omar A. M. Mousa, Ahmed Ragab, Mohamed |
| author_facet | Abdelraouf, Omar A. M. Mousa, Ahmed Ragab, Mohamed |
| contents | Metasurfaces are key to the development of flat optics and nanophotonic devices, offering significant advantages in creating structural colors and high-quality factor cavities. Multi-layer metasurfaces (MLMs) further amplify these benefits by enhancing light-matter interactions within individual nanopillars. However, the numerous design parameters involved make traditional simulation tools impractical and time-consuming for optimizing MLMs. This highlights the need for more efficient approaches to accelerate their design. In this work, we introduce NanoPhotoNet, an AI-driven design tool based on a hybrid deep neural network (DNN) model that combines convolutional neural networks (CNN) and Long Short-Term Memory (LSTM) networks. NanoPhotoNet enhances the design and optimization of MLMs, achieving a prediction accuracy of over 98.3% and a speed improvement of 50,000x compared to conventional methods. The tool enables MLMs to produce structural colors beyond the standard RGB region, expanding the RGB gamut area by 163%. Furthermore, we demonstrate the generation of tunable structural colors, extending the metasurface functionality to tunable color filters. These findings present a powerful method for applying NanoPhotoNet to MLMs, enabling strong light-matter interactions in applications such as tunable nanolasers and reconfigurable beam steering. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_07121 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Next-Generation Multi-layer Metasurface Design: Hybrid Deep Learning Models for Beyond-RGB Reconfigurable Structural Colors Abdelraouf, Omar A. M. Mousa, Ahmed Ragab, Mohamed Optics Applied Physics Metasurfaces are key to the development of flat optics and nanophotonic devices, offering significant advantages in creating structural colors and high-quality factor cavities. Multi-layer metasurfaces (MLMs) further amplify these benefits by enhancing light-matter interactions within individual nanopillars. However, the numerous design parameters involved make traditional simulation tools impractical and time-consuming for optimizing MLMs. This highlights the need for more efficient approaches to accelerate their design. In this work, we introduce NanoPhotoNet, an AI-driven design tool based on a hybrid deep neural network (DNN) model that combines convolutional neural networks (CNN) and Long Short-Term Memory (LSTM) networks. NanoPhotoNet enhances the design and optimization of MLMs, achieving a prediction accuracy of over 98.3% and a speed improvement of 50,000x compared to conventional methods. The tool enables MLMs to produce structural colors beyond the standard RGB region, expanding the RGB gamut area by 163%. Furthermore, we demonstrate the generation of tunable structural colors, extending the metasurface functionality to tunable color filters. These findings present a powerful method for applying NanoPhotoNet to MLMs, enabling strong light-matter interactions in applications such as tunable nanolasers and reconfigurable beam steering. |
| title | Next-Generation Multi-layer Metasurface Design: Hybrid Deep Learning Models for Beyond-RGB Reconfigurable Structural Colors |
| topic | Optics Applied Physics |
| url | https://arxiv.org/abs/2409.07121 |