Malkiel Itzik, Mrejen Michael, Nagler Achiya, Arieli Uri, Wolf Lior, Suchowski Haim
1School of Computer Science, Faculty of Exact Sciences, Tel Aviv University, Tel Aviv, 69978 Israel.
2School of Physics and Astronomy, Faculty of Exact Sciences, Tel Aviv University, Tel Aviv, 69978 Israel.
Light Sci Appl. 2018 Sep 5;7:60. doi: 10.1038/s41377-018-0060-7. eCollection 2018.
Nanophotonics, the field that merges photonics and nanotechnology, has in recent years revolutionized the field of optics by enabling the manipulation of light-matter interactions with subwavelength structures. However, despite the many advances in this field, the design, fabrication and characterization has remained widely an iterative process in which the designer guesses a structure and solves the Maxwell's equations for it. In contrast, the inverse problem, i.e., obtaining a geometry for a desired electromagnetic response, remains a challenging and time-consuming task within the boundaries of very specific assumptions. Here, we experimentally demonstrate that a novel Deep Neural Network trained with thousands of synthetic experiments is not only able to retrieve subwavelength dimensions from solely far-field measurements but is also capable of directly addressing the inverse problem. Our approach allows the rapid design and characterization of metasurface-based optical elements as well as optimal nanostructures for targeted chemicals and biomolecules, which are critical for sensing, imaging and integrated spectroscopy applications.
纳米光子学，这个融合了光子学和纳米技术的领域，近年来通过利用亚波长结构操控光与物质的相互作用，彻底改变了光学领域。然而，尽管该领域取得了诸多进展，其设计、制造和表征在很大程度上仍然是一个迭代过程，即设计者先猜测一种结构，然后针对该结构求解麦克斯韦方程组。相比之下，反问题，即根据所需的电磁响应获取几何结构，在非常特定的假设范围内仍然是一项具有挑战性且耗时的任务。在此，我们通过实验证明，一个经过数千次合成实验训练的新型深度神经网络不仅能够仅从远场测量中检索亚波长尺寸，而且还能够直接解决反问题。我们的方法允许快速设计和表征基于超表面的光学元件以及针对特定化学物质和生物分子的最佳纳米结构，这对于传感、成像和集成光谱应用至关重要。
