基于卷积神经网络的薄膜超材料通用逆向设计
General Inverse Design of Thin-Film Metamaterials With Convolutional Neural Networks
AI总结：
本文利用卷积神经网络解决多层薄膜超材料的逆向设计问题，能够高效探索高达10^12种参数组合的设计空间，并建立结构与光谱之间的映射关系，相比传统优化方法在多层级系统中具有显著效率优势。
摘要：
支持独特光学响应的超材料设计是大多数薄膜纳米光子学应用的基础。在实践中，由于通用多层系统相关的巨大设计参数空间，这一逆向设计问题难以系统解决。我们应用卷积神经网络（深度机器学习的一个子集）作为工具，来解决由薄膜堆叠组成的超材料的逆向设计问题。我们展示了神经网络在探测大型全局设计空间（高达10^12种可能的参数组合）并解析超材料结构与对应的椭偏光谱及反射/透射光谱之间所有关系的卓越能力。该方法的适用性进一步扩展到通用设计场景中合成工程光谱的逆向设计。此外，将该方法与传统优化方法进行了比较。我们发现，随着总层数的增加，网络的相对优化效率提高，揭示了机器学习方法在多层级系统中的优势，而传统方法在此类系统中变得不切实际。
英文摘要：
The design of metamaterials which support unique optical responses is the basis for most thin-film nanophotonics applications. In practice this inverse design problem can be difficult to solve systematically due to the large design parameter space associated with general multi-layered systems. We apply convolutional neural networks, a subset of deep machine learning, as a tool to solve this inverse design problem for metamaterials composed of stacks of thin films. We demonstrate the remarkable ability of neural networks to probe the large global design space (up to $10^{12}$ possible parameter combinations) and resolve all relationships between metamaterial structure and corresponding ellipsometric and reflectance / transmittance spectra. The applicability of the approach is further expanded to include the inverse design of synthetic engineered spectra in general design scenarios. Furthermore, this approach is compared with traditional optimization methods. We find an increase in the relative optimization efficiency of the networks with increasing total layer number, revealing the advantage of the machine learning approach in many-layered systems where traditional methods become impractical.
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