Measuring complex refractive index through deep-learning-enabled optical reflectometry
Measuring complex refractive index through deep-learning-enabled optical reflectometry
复制标题
通过支持深度学习的光学反射测量法测量复折射率
DOI：
10.1088/2053-1583/acc59b
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发表时间：
2023-03
期刊：
影响因子：
5.5
通讯作者：
Ziyang Wang;Y. Lin;Kunyan Zhang;Wenjing Wu;Shengxi Huang
中科院分区：
文献类型：
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作者：
Ziyang Wang;Y. Lin;Kunyan Zhang;Wenjing Wu;Shengxi Huang
光谱学是纳米科学和纳米技术、微电子、能源和先进制造业的研究和开发不可或缺的。先进的光谱工具通常需要专门设计的高端仪器和复杂的数据分析技术。除了常见的分析工具，深度学习方法非常适合解释高维和复杂的光谱数据。它们提供了很好的机会，可以通过更简单的光学设置提取有关材料光学特性的微妙和深入的信息，否则需要复杂的仪器。在这项工作中，我们提出了一种基于传统桌面光学显微镜和称为ReflectoNet的深度学习模型的计算方法。在没有任何关于多层基板的先验知识的情况下，ReflectoNet可以从实验测量的光学反射光谱以高精度预测这些非平凡基板上的薄膜和2D材料的复折射率。这项任务是不可行的，以前与传统的反射或椭圆偏振方法。基本的物理原理，如Kramers-Kronig关系，是由模型自发学习的，无需任何进一步的训练。这种方法能够对复杂光子结构或光电器件中的功能材料和2D材料进行操作中光学表征。
Optical spectroscopy is indispensable for research and development in nanoscience and nanotechnology, microelectronics, energy, and advanced manufacturing. Advanced optical spectroscopy tools often require both specifically designed high-end instrumentation and intricate data analysis techniques. Beyond the common analytical tools, deep learning methods are well suited for interpreting high-dimensional and complicated spectroscopy data. They offer great opportunities to extract subtle and deep information about optical properties of materials with simpler optical setups, which would otherwise require sophisticated instrumentation. In this work, we propose a computational approach based on a conventional tabletop optical microscope and a deep learning model called ReflectoNet. Without any prior knowledge about the multilayer substrates, ReflectoNet can predict the complex refractive indices of thin films and 2D materials on top of these nontrivial substrates from experimentally measured optical reflectance spectra with high accuracies. This task was not feasible previously with traditional reflectometry or ellipsometry methods. Fundamental physical principles, such as the Kramers–Kronig relations, are spontaneously learned by the model without any further training. This approach enables in-operando optical characterization of functional materials and 2D materials within complex photonic structures or optoelectronic devices.