Resonant-mode metasurface thermal super mirror by deep learning-assisted optimization algorithms

IF 2.3 3区 物理与天体物理 Q2 OPTICS Journal of Quantitative Spectroscopy & Radiative Transfer Pub Date : 2024-09-12 DOI:10.1016/j.jqsrt.2024.109195
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Abstract

A “super-mirror” having ultrahigh infrared reflectance is achieved by an optimized photonic contrast grating metasurface. Finding ways to achieve this exceptional performance can be enabled by implementing global optimization and machine learning elements, such as Bayesian optimization and genetic algorithm. Here, we acquired an optimized grating design made of high-index germanium, which excites resonances that result in ultralow emittance at certain wavelengths. Our optimizations assisted in the discovery of hybridized coupling of Fabry-Pérot modes and guided modes in a monolithic microscale multilayered coating. We demonstrate constraints in the given geometric variable ranges improves the overall performance of algorithms. We also show the enhanced performance of a deep learning Feedforward Neural Network, which is implemented as the inverse design using the network trained with dataset obtained from Bayesian optimization and Genetic Algorithm approaches. The performance of the Feedforward Neural Network-assisted design produced normal emissivity difference by only +3.5 %, with lower sensitivity to grating dimensional parameter variations. The improvement is achieved by predicting and better understanding of the optical physics of resonant gratings. The proposed few-layer grating coating can be applied to space components, enclosures, and vessels to suppress thermal radiative heat loss.

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通过深度学习辅助优化算法实现共振模式元表面热超级反射镜
通过优化光子对比光栅元表面,实现了具有超高红外反射率的 "超级反射镜"。通过实施全局优化和机器学习元素(如贝叶斯优化和遗传算法),可以找到实现这一卓越性能的方法。在这里,我们获得了一种由高指数锗制成的优化光栅设计,它能激发共振,从而在特定波长下实现超低发射率。我们的优化有助于在整体微尺度多层涂层中发现法布里-佩罗模式和导波模式的混合耦合。我们证明了给定几何变量范围内的约束可以提高算法的整体性能。我们还展示了深度学习前馈神经网络的增强性能,该网络是利用贝叶斯优化和遗传算法方法获得的数据集训练的网络作为逆向设计实现的。前馈神经网络辅助设计产生的正常发射率差异仅为 +3.5%,对光栅尺寸参数变化的敏感性更低。这一改进是通过预测和更好地理解共振光栅的光学物理特性实现的。建议的几层光栅涂层可应用于空间元件、外壳和容器,以抑制热辐射热损失。
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来源期刊
CiteScore
5.30
自引率
21.70%
发文量
273
审稿时长
58 days
期刊介绍: Papers with the following subject areas are suitable for publication in the Journal of Quantitative Spectroscopy and Radiative Transfer: - Theoretical and experimental aspects of the spectra of atoms, molecules, ions, and plasmas. - Spectral lineshape studies including models and computational algorithms. - Atmospheric spectroscopy. - Theoretical and experimental aspects of light scattering. - Application of light scattering in particle characterization and remote sensing. - Application of light scattering in biological sciences and medicine. - Radiative transfer in absorbing, emitting, and scattering media. - Radiative transfer in stochastic media.
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