Abstract
AbstractComplex 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 and material 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.
Highlights
Nanophotonic materials, including metasurfaces and metamaterials, have greatly expanded our ability to tailor light–matter interaction and deliver new functionalities for information processing and sensing applications [1,2,3,4]
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
We investigate the inverse design of large multiplexed supercell metasurfaces with over 100 subunit elements that can achieve a diverse set of broadband spectral responses
Summary
Nanophotonic materials, including metasurfaces and metamaterials, have greatly expanded our ability to tailor light–matter interaction and deliver new functionalities for information processing and sensing applications [1,2,3,4]. Conventional design processes for periodic and complex supercell metasurfaces rely on electromagnetic (EM) simulations that are iteratively optimized by tuning key design parameters until the desired optical properties are obtained. With training datasets ranging from several hundred [34] to several thousand [27] instances, previously explored machine learning and DNN-based photonics design include the forward and inverse modeling of multishell nanoparticles, multilayer thin films, and various classes of metasurfaces [35,36,37,38,39]. An ML-based strategy for complex supercells that: 1) directly solves the inverse design problem, 2) generates structures with a wide range of unique elements, and 3) considers strong coupling interactions or mode hybridization between individual elements is lacking today, but could allow for the demonstration of complex nanophotonic architectures with a broader range of spectral responses. We show that the network itself can be harnessed to approximate the structure– property relationships of the explored class of metasurfaces
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