{"title":"Resonant-mode metasurface thermal super mirror by deep learning-assisted optimization algorithms","authors":"Ken Araki , Richard Z. Zhang","doi":"10.1016/j.jqsrt.2024.109195","DOIUrl":null,"url":null,"abstract":"<div><p>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.</p></div>","PeriodicalId":16935,"journal":{"name":"Journal of Quantitative Spectroscopy & Radiative Transfer","volume":"329 ","pages":"Article 109195"},"PeriodicalIF":2.3000,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0022407324003029/pdfft?md5=a2f9f2de203191e402ad155fb43f7533&pid=1-s2.0-S0022407324003029-main.pdf","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Quantitative Spectroscopy & Radiative Transfer","FirstCategoryId":"101","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0022407324003029","RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"OPTICS","Score":null,"Total":0}
引用次数: 0
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.
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.