Inverse design of ultra-narrowband selective thermal emitters designed by artificial neural networks
Sunae So, Dasol Lee, Trevon Badloe, Junsuk Rho
Abstract
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 µ 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.