A new, single-layer antireflective (AR) coating for silicon solar cells minimizes sunlight reflection across a broad range of wavelengths and angles, surpassing the performance of existing single-layer coatings and approaching the performance levels of multilayered AR coatings. The developers of the coating, from Kharkiv National University, the National Academy of Sciences of Ukraine, and Leibniz Institute of Photonic Technology, used machine learning-enhanced photonic nanostructures, also known as metasurfaces, to create AR coatings with ultralow light reflection. The metasurface-based coatings reduce reflection across the visible and near-infrared (NIR) spectrum of 500-1200 nm — where the irradiance of sunlight is at its maximum — and are effective even when the sunlight hits at steep angles. As such, they have the potential to overcome the limitations of traditional coatings, which improve light transmission in only a narrow range of wavelengths (typically between 100-300 nm) and incidence angles. A newly developed metasurface-based silicon antireflective (AR) coating combines rectangular and cylindrical meta-atom geometries. The metasurface achieves just 5% reflection, compared to about 50% reflection with an unstructured silicon solar cell. Courtesy of Ovcharenko, Polevoy, and Yermakov, doi: 10.1117/1.APN.4.3.036009. The researchers developed the metasurfaces using forward-design and inverse-design optimization algorithms. They based the forward-designed metasurfaces on cross-circular meta-atoms, and the inverse-designed structures on free-form meta-atom geometry. Forward design can produce promising results with the appropriate choice of geometric parameters, whereas geometric parameters do not need to be determined in advance with inverse design. According to the team, both its cross-circular, forward-design and free-form, inverse-design structures demonstrated the highest-performing antireflection properties reported to date for single-layer structures. Inverse design is beneficial for creating multifunctional, broadband metasurfaces. The integration of AI, specifically machine learning algorithms, can further enhance the capabilities of inverse design. The team found that the metasurface-based AR coatings reflected as little as 2% of incoming light at direct angles and about 4.4% of incoming light at oblique incidence in an angular range up to 60°. The metasurface-based coatings enhanced reflection suppression by about one order of magnitude compared with unstructured flat silicon surfaces. The reflection of light from a flat silicon surface ranges from 35-50% in the visible and NIR spectra, reducing the efficiency of solar cells almost by half. Moreover, the use of AR coatings is especially challenging in silicon solar cells due to the high contrast between air and silicon, which results in high reflectance. The standard thin-film AR coatings with the highest performance are multilayered and narrowband, but real-world applications require ultrathin solutions that minimize light reflection over a wide range of wavelengths. This work shows that AI can enhance the efficiency of AR coatings for the silicon solar cells used for mainstream solar panels. Because the metasurface-based coatings are both high-performing and relatively simple, they could potentially speed the transition to clean energy by increasing the power conversion efficiency of solar panels. Beyond solar energy, the machine-learning-assisted design approach could improve the methods used by scientists to design metasurfaces for broader application in optics and photonics. The forward and inverse design of single-layer, metasurface-based, broadband AR coatings for silicon solar cells could open a path to multifunctional photonic coatings that could benefit sensors and other optical devices, in addition to solar power devices. The research was published in Advanced Photonics Nexus (www.doi.org/10.1117/1.APN.4.3.036009).