The capabilities of modern precision nanofabrication and the wide choice of materials [plasmonic metals, high-index dielectrics, phase change materials (PCM), and 2D materials] make the inverse design of nanophotonic structures such as metasurfaces increasingly difficult. Deep learning is becoming increasingly relevant for nanophotonics inverse design. Although deep learning design methodologies are becoming increasingly sophisticated, the problem of the simultaneous inverse design of structure and material has not received much attention. In this contribution, we propose a deep learning-based inverse design methodology for simultaneous material choice and device geometry optimization. To demonstrate the utility of the proposed method, we consider the topical problem of active metasurface design using PCMs. We consider a set of four commonly used PCMs in both fully amorphous and crystalline material phases for the material choice and an arbitrarily specifiable polygonal meta-atom shape for the geometry part, which leads to a vast structure/material design space. We find that a suitably designed deep neural network can achieve good optical spectrum prediction capability in an ample design space. Furthermore, we show that this forward model has a sufficiently high predictive ability to be used in a surrogate-optimization setup resulting in the inverse design of active metasurfaces of switchable functionality.

中文翻译：

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深度学习有助于同步结构-材料设计发现：相变材料超表面设计案例研究

现代精密纳米加工的能力和材料的广泛选择[等离子体金属、高折射率电介质、相变材料（PCM）和二维材料]使得超表面等纳米光子结构的逆向设计变得越来越困难。深度学习与纳米光子学逆向设计变得越来越相关。尽管深度学习设计方法变得越来越复杂，但结构和材料的同步逆向设计问题尚未受到太多关注。在这篇文章中，我们提出了一种基于深度学习的逆向设计方法，用于同时进行材料选择和器件几何优化。为了证明所提出方法的实用性，我们考虑了使用 PCM 进行主动超表面设计的热门问题。我们考虑了一组四种常用的完全非晶态和结晶材料相的相变材料来进行材料选择，并考虑任意指定的多边形元原子形状作为几何部分，这导致了巨大的结构/材料设计空间。我们发现，适当设计的深度神经网络可以在充足的设计空间中实现良好的光谱预测能力。此外，我们表明该正向模型具有足够高的预测能力，可用于替代优化设置，从而实现可切换功能的活动超表面的逆向设计。我们发现，适当设计的深度神经网络可以在充足的设计空间中实现良好的光谱预测能力。此外，我们表明该正向模型具有足够高的预测能力，可用于替代优化设置，从而实现可切换功能的活动超表面的逆向设计。我们发现，适当设计的深度神经网络可以在充足的设计空间中实现良好的光谱预测能力。此外，我们表明该正向模型具有足够高的预测能力，可用于替代优化设置，从而实现可切换功能的活动超表面的逆向设计。