Davoodi Fatemeh
Institute of Experimental and Applied Physics, Kiel University, Leibnizstr. 11-19, Kiel 24098, Germany.
Kiel Nano, Surface and Interface Science KiNSIS, Christian Albrechts University, Olshausenstraße 75, Kiel 24118, Germany.
Nano Lett. 2025 Jan 15;25(2):768-775. doi: 10.1021/acs.nanolett.4c05120. Epub 2025 Jan 4.
Topological plasmonics combines principles of topology and plasmonics to provide new methods for controlling light, analogous to topological edge states in photonics. However, designing such topological states remains challenging due to the complexity of the high-dimensional design space. We present a novel method that uses supervised, physics-informed deep learning and surrogate modeling to design topological devices for desired wavelengths. By embedding physical constraints in the neural network's training, our model efficiently explores the design space, significantly reducing simulation time. Additionally, we use nonplanar wavefront excitations to probe topologically protected plasmonic modes, making the design and training process nonlinear. Using this approach, we design a topological device with unidirectional edge modes in a ring resonator at specific operational frequencies. Our method reduces computational cost and time while maintaining high accuracy, highlighting the potential of combining machine learning and advanced techniques for photonic device innovation.
拓扑等离激元学结合了拓扑学和等离激元学原理，以提供控制光的新方法，类似于光子学中的拓扑边缘态。然而，由于高维设计空间的复杂性，设计此类拓扑态仍然具有挑战性。我们提出了一种新颖的方法，该方法使用有监督的、基于物理知识的深度学习和代理建模来设计所需波长的拓扑器件。通过在神经网络训练中嵌入物理约束，我们的模型有效地探索了设计空间，显著减少了模拟时间。此外，我们使用非平面波前激发来探测拓扑保护的等离激元模式，使设计和训练过程具有非线性。使用这种方法，我们在特定工作频率下设计了一种在环形谐振器中具有单向边缘模式的拓扑器件。我们的方法在保持高精度的同时降低了计算成本和时间，突出了将机器学习与先进技术相结合用于光子器件创新的潜力。
