School of Science and Technology, City University of London, London, EC1V 0HB, UK.
MasterCard, EC4R 3AB, London, UK.
Sci Rep. 2023 Jan 20;13(1):1129. doi: 10.1038/s41598-023-28076-3.
Nanophotonics exploits the best of photonics and nanotechnology which has transformed optics in recent years by allowing subwavelength structures to enhance light-matter interactions. Despite these breakthroughs, design, fabrication, and characterization of such exotic devices have remained through iterative processes which are often computationally costly, memory-intensive, and time-consuming. In contrast, deep learning approaches have recently shown excellent performance as practical computational tools, providing an alternate avenue for speeding up such nanophotonics simulations. This study presents a DNN framework for transmission, reflection, and absorption spectra predictions by grasping the hidden correlation between the independent nanostructure properties and their corresponding optical responses. The proposed DNN framework is shown to require a sufficient amount of training data to achieve an accurate approximation of the optical performance derived from computational models. The fully trained framework can outperform a traditional EM solution using on the COMSOL Multiphysics approach in terms of computational cost by three orders of magnitude. Furthermore, employing deep learning methodologies, the proposed DNN framework makes an effort to optimise design elements that influence the geometrical dimensions of the nanostructure, offering insight into the universal transmission, reflection, and absorption spectra predictions at the nanoscale. This paradigm improves the viability of complicated nanostructure design and analysis, and it has a lot of potential applications involving exotic light-matter interactions between nanostructures and electromagnetic fields. In terms of computational times, the designed algorithm is more than 700 times faster as compared to conventional FEM method (when manual meshing is used). Hence, this approach paves the way for fast yet universal methods for the characterization and analysis of the optical response of nanophotonic systems.
纳米光子学利用了光子学和纳米技术的优势，通过允许亚波长结构增强光物质相互作用，近年来改变了光学。尽管取得了这些突破，但这些奇异器件的设计、制造和特性化仍然需要通过迭代过程来完成，这通常计算成本高、内存密集且耗时。相比之下，深度学习方法最近作为实用的计算工具表现出了优异的性能，为加速这种纳米光子学模拟提供了另一种途径。本研究提出了一种 DNN 框架，通过掌握独立纳米结构特性与其相应光学响应之间的隐藏相关性，来预测传输、反射和吸收光谱。所提出的 DNN 框架需要足够数量的训练数据，才能实现对来自计算模型的光学性能的准确逼近。在计算成本方面，完全训练的框架可以优于传统的 EM 解决方案，使用 COMSOL Multiphysics 方法可以提高三个数量级。此外，采用深度学习方法，所提出的 DNN 框架努力优化影响纳米结构几何尺寸的设计元素，为纳米尺度上的通用传输、反射和吸收光谱预测提供了深入的见解。这种方法提高了复杂纳米结构设计和分析的可行性，并且在涉及纳米结构和电磁场之间的奇异光物质相互作用的许多潜在应用中具有很大的潜力。在计算时间方面，与传统的 FEM 方法（当手动网格划分时）相比，设计的算法速度快 700 多倍。因此，这种方法为纳米光子系统的光学响应的快速而通用的特性化和分析方法铺平了道路。
