Fabrication of Grating-based Solar Thermal
Absorber using Deep Neural Network andNanoimprint Lithography
Pil-Hoon Jung*1, Junyong Seo*2, Mingeon Kim*2, Son soomin Son*1, andBong Jae Lee*2, Heon Lee*1
1

Department of Materials Science and Engineering, Korea University, 5-1 Anam-dong, Sungbuk-gu,Seoul 136-713 and2Department of Mechanical Engineering, Korea Advanced Institute of Science and Technology,
Daejeon 34141, South Korea
E-mail: nanoimprint@naver.com

In recent decades, clean and abundant solar energy has been considered as a promising
renewable energy source because of the rising fossil fuel prices, global warming andenvironmental pollution. One of the efficient ways to utilize solar energy is to make use ofsolar thermal energy system. Compared to conventional photovoltaics, advantage of solar
thermal energy conversion is the use of almost all solar spectrum and enables high energy
conversion efficiency. To obtain the maximum conversion efficiency of solar radiation, solar
absorber should possess an optical absorption of visible and near-infrared (IR) spectral region
in the solar spectrum while suppressing mid-IR emission at high temperatures. However, it isremains a challenge to obtain a high efficiency solar absorber by a conventional fabricating
methods. In order to overcome the fabrication difficulty, we propose the subwavelength-sizednanostructures.
In designing solar absorber using nanostructure, numerical simulations are often used toestimate electromagnetic behaviors of structures. For example, rigorous coupled-wave
analysis and finite-difference time-domain are commonly employed. The simulation methods,
however, are based on heavy computation resources and time. In this paper, we will employ astate-of-theart modelling technique, deep learning to reduce the cost of calculation. To avoid
computing extreme number of samples, only regulated amounts are prepared and used as data
set to train deep neural network. Designing nanostructured metamaterial for a broad-bandsolar thermal absorber is considered as an example problem. Moreover, several analyses
utilizing evolutionary algorithm or Monte Carlo method, which require tremendous numberof performance calculations, will be performed on the trained network. Here, we showed thatdeep learning could most accurately imitate the original performance and also effectively
reduce overall designing costs 0.5 to almost zero times than designing with simulation only.

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Figure 1. Two dimensional and one periodic schematics of Cr embedded-grating solar absorberstructure

Figure 2. (a) Fabrication processes of Cr embedded-grating solar absorber structure, (b) TEM-SEM
image of fabricated Cr embedded-grating solar absorber structure; (c)-(e) HAADF-STEM crosssectional image and EDX elemental mapping image of Cr embedded grating structures. Twodimensional and one periodic schematics of Cr embedded-grating solar absorber structure.