论文标题
通过可解释的机器学习算法阐明纳米光结构的行为
Elucidating the Behavior of Nanophotonic Structures Through Explainable Machine Learning Algorithms
论文作者
论文摘要
纳米光结构开发的核心挑战是确定目标功能的最佳设计，并了解实现优化设备功能的物理机制。先前研究的纳米光结构设计方法，包括传统优化方法以及新生的机器学习（ML）策略，都取得了进步，但它们仍然是“黑匣子”，缺乏对其预测的解释。在这里，我们证明，可以解释经过培训的卷积神经网络（CNN），以预测金属 - 二电 - 基层超材料的电磁反应，包括复杂的自由形式设计，以揭示对纳米光子结构的基本物理的更深入的见解。使用可解释的AI（XAI）方法，我们表明我们可以确定纳米仪结构的特定空间区域对于存在或缺乏吸收峰的重要性。我们的结果强调，ML策略可用于光学和光子学中的物理发现以及设计优化。
A central challenge in the development of nanophotonic structures is identifying the optimal design for a target functionality, and understanding the physical mechanisms that enable the optimized device's capabilities. Previously investigated design methods for nanophotonic structures, including both conventional optimization approaches as well as nascent machine learning (ML) strategies, have made progress, yet they remain 'black boxes' that lack explanations for their predictions. Here we demonstrate that convolutional neural networks (CNN) trained to predict the electromagnetic response of classes of metal-dielectric-metal metamaterials, including complex freeform designs, can be explained to reveal deeper insights into the underlying physics of nanophotonic structures. Using an explainable AI (XAI) approach, we show that we can identify the importance of specific spatial regions of a nanophotonic structure for the presence or lack of an absorption peak. Our results highlight that ML strategies can be used for physics discovery, as well as design optimization, in optics and photonics.
