期刊
NANOPHOTONICS
卷 10, 期 3, 页码 1133-1143出版社
WALTER DE GRUYTER GMBH
DOI: 10.1515/nanoph-2020-0549
关键词
deep learning; metasurfaces; nanophotonics; supercells; tandem residual networks
资金
- Sloan Research Fellowship from the Alfred P. Sloan Foundation
Complex nanophotonic structures can be efficiently designed using deep learning models to explore structure-property relationships and generate a wide range of complex designs. This approach overcomes the challenges of vast design possibilities in a high-dimensional design space, demonstrating the feasibility of using deep neural networks for inverse design of nanophotonic structures.
Complex nanophotonic structures hold the potential to deliver exquisitely tailored optical responses for a range of applications. Metal-insulator-metal (MIM) metasurfaces arranged in supercells, for instance, can be tailored by geometry andmaterial choice to exhibit a variety of absorption properties and resonant wavelengths. With this flexibility, however, comes a vast space of design possibilities that classical design paradigms struggle to effectively navigate. To overcome this challenge, here, we demonstrate a tandem residual network approach to efficiently generate multiplexed supercells through inverse design. By using a training dataset with several thousand full-wave electromagnetic simulations in a design space of over three trillion possible designs, the deep learning model can accurately generate a wide range of complex supercell designs given a spectral target. Beyond inverse design, the presented approach can also be used to explore the structure-property relationships of broadband absorption and emission in such supercell configurations. Thus, this study demonstrates the feasibility of high-dimensional supercell inverse design with deep neural networks, which is applicable to complex nanophotonic structures composed of multiple subunit elements that exhibit coupling.
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