Inverse design of ultra-narrowband selective thermal emitters designed by artificial neural networks

The inverse design of photonic devices through the training of artificial neural networks (ANNs) has been proven as an invaluable tool for researchers to uncover interesting structures and designs that produce optical devices with enhanced performance. Here, we demonstrate the inverse design of ultra-narrowband selective thermal emitters that operate in the wavelength regime of 2-8 mu m using ANNs. By training the network on a dataset of around 200,000 samples, wavelength-selective thermal emitters are designed with an average mean squared error of less than 0.006. Q-factors as high as 109.2 are achieved, proving the ultra-narrowband properties of the thermal emitters. We further investigate the physical mechanisms of the designed emitters and characterize their angular responses to verify their use as thermal emitters for practical applications such as thermophotovoltaics, IR sensing and imaging, and infrared heating. As the quest for sustainable sources of energy is ever present, methods of effectively utilizing the power of the sun are paramount. Photovoltaic techniques use sunlight to excite electron-hole pairs in semiconductors to produce a usable flow of electrons [1]. This is then generally used to power electrical equipment or to charge a battery to store the energy to use later. A second well-known method is to use the heat from the sunlight to drive mechanical heat engines [2]. Photonic devices can be integrated into both strategies in order to increase the efficiency and generated power by engineering the optical properties in a way to enhance the absorption at the desired wavelengths [3-5]. Furthermore, according to Kirchhoff's law of thermal radiation, the emissivity of an arbitrary body in thermodynamic equilibrium is equal to the absorptivity. Therefore, the design of broad and narrowband absorbers/emitters is of great interest for use in energy applications such as thermophotovoltaics [6,7] and radiative cooling [8-12], as well as in

Automated summary

The inverse design of thermal emitters using artificial neural networks has shown to be effective in achieving high efficiency and enhancing performance of photonic devices. This design approach can be applied to practical applications such as thermophotovoltaics, IR sensing and imaging, and infrared heating to utilize sustainable energy sources effectively.