Diatom algae, abundantly found in the ocean, has hierarchical micro- and nanopores which inspired lots of metamaterial designs including dielectric metasurfaces. The conventional approach taken in the metamaterial design process is to generate the corresponding optical spectrum by utilizing physics-based simulation software. Although this approach provides high accuracy, the downside is that it is time-consuming and there are also constraints. By setting design parameters and the structure of the material, the optical response could be easily achieved. However, this approach is not able to deal with the inverse problem as simple as in the forward problem. In this study, a deep learning model that is capable of solving both the forward and the inverse problem of a diatom-inspired metamaterial design was developed and it was further verified experimentally. This method serves as an alternative way for the traditional metamaterial design process which greatly saves time and also presents functionality that simulation does not provide. To investigate the feasibility of this method, different input training datasets were examined, and several strategies were taken to improve the model performance. Though with the success in some cases, effort is still needed to employ the technique in a broader aspect. / 2024-01-15T00:00:00Z
| Identifer | oai:union.ndltd.org:bu.edu/oai:open.bu.edu:2144/45449 |
| Date | 16 January 2023 |
| Creators | Shih, Ting-An |
| Contributors | Zhang, Xin |
| Source Sets | Boston University |
| Language | en_US |
| Detected Language | English |
| Type | Thesis/Dissertation |
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