Discovering high-performance broadband and broad angle antireflection surfaces by machine learning

Abstract

Eliminating light reflection from the top glass sheet in optoelectronic applications is often desirable across a broad range of wavelengths and large variety of angles. In this paper, we report on a combined simulation and experimental study of single-layer films, nanowire arrays, and nanocone arrays to meet these antireflection (AR) needs. We demonstrate the application of Bayesian learning to the multiobjective optimization of these structures for broadband and broad angle AR and show the superior performance of Bayesian learning to genetic algorithms for optimization. Our simulations indicate that nanocone structures have the best AR performance of these three structures, and we additionally provide physical insight into the AR performance of different structures. Simulations suggest nanocone arrays are able to achieve a solar integrated normal and 65° incidence angle reflection of 0.15% and 1.25%, respectively. A simple and scalable maskless reactive ion etching process is used to create nanocone structures, and etched samples demonstrate a solar integrated normal and 65° reflection of 0.4% and 4.9%, respectively, at the front interface.