近日，沙特阿拉伯阿卜杜拉国王科技大学的Ying Wu&Xiangliang Zhang及其研究团队取得一项新进展。经过不懈努力，他们研制出用于增强超薄硅膜宽带吸收的机器学习辅助表面等离子体超表面屏。相关研究成果已于2025年1月9日国际学术期刊《光：科学与应用》上发表。

该研究团队提出并展示了一种数据驱动的表面等离子体超表面屏，它能在超薄硅膜上高效吸收宽光谱范围内的入射光。通过在20纳米厚的非晶硅（a-Si）层中嵌入双纳米环银阵列，研究人员实现了光吸收的显著提升。这种增强源于谐振腔模式与局域表面等离子体模式的相互作用，需要精确调谐等离子体共振以匹配硅活性层的吸收区域。

为了简化器件设计并在不增加活性层厚度的情况下提高光吸收，研究人员开发了一个深度学习框架，该框架能够学习从吸收光谱到设计空间的映射。这种逆向设计策略有助于调整吸收以实现选择性光谱功能。

这一优化设计超越了裸露的硅平面器件，表现出了超过100%的显著增强。实验验证证实了所提出配置中光吸收的宽带增强。所提出的超表面屏吸收器在光捕获应用中具有巨大潜力，并可用于提高超薄硅太阳能电池、光电探测器和光学滤波器的光转换效率。

附：英文原文

Title: Machine learning assisted plasmonic metascreen for enhanced broadband absorption in ultra-thin silicon films

Author: Ahmed, Waqas W., Cao, Haicheng, Xu, Changqing, Farhat, Mohamed, Amin, Muhammad, Li, Xiaohang, Zhang, Xiangliang, Wu, Ying

Issue&Volume: 2025-01-09

Abstract: We propose and demonstrate a data-driven plasmonic metascreen that efficiently absorbs incident light over a wide spectral range in an ultra-thin silicon film. By embedding a double-nanoring silver array within a 20nm ultrathin amorphous silicon (a-Si) layer, we achieve a significant enhancement of light absorption. This enhancement arises from the interaction between the resonant cavity modes and localized plasmonic modes, requiring precise tuning of plasmon resonances to match the absorption region of the silicon active layer. To facilitate the device design and improve light absorption without increasing the thickness of the active layer, we develop a deep learning framework, which learns to map from the absorption spectra to the design space. This inverse design strategy helps to tune the absorption for selective spectral functionalities. Our optimized design surpasses the bare silicon planar device, exhibiting a remarkable enhancement of over 100%. Experimental validation confirms the broadband enhancement of light absorption in the proposed configuration. The proposed metascreen absorber holds great potential for light harvesting applications and may be leveraged to improve the light conversion efficiency of ultra-thin silicon solar cells, photodetectors, and optical filters.

DOI: 10.1038/s41377-024-01723-8

Source: https://www.nature.com/articles/s41377-024-01723-8