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

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.