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Deep learning approach based on dimensionality reduction for designing electromagnetic nanostructures
npj Computational Materials ( IF 9.7 ) Pub Date : 2020-02-04 , DOI: 10.1038/s41524-020-0276-y
Yashar Kiarashinejad , Sajjad Abdollahramezani , Ali Adibi

In this paper, we demonstrate a computationally efficient new approach based on deep learning (DL) techniques for analysis, design and optimization of electromagnetic (EM) nanostructures. We use the strong correlation among features of a generic EM problem to considerably reduce the dimensionality of the problem and thus, the computational complexity, without imposing considerable errors. By employing the dimensionality reduction concept using the more recently demonstrated autoencoder technique, we redefine the conventional many-to-one design problem in EM nanostructures into a one-to-one problem plus a much simpler many-to-one problem, which can be simply solved using an analytic formulation. This approach reduces the computational complexity in solving both the forward problem (i.e., analysis) and the inverse problem (i.e., design) by orders of magnitude compared to conventional approaches. In addition, it provides analytic formulations that, despite their complexity, can be used to obtain intuitive understanding of the physics and dynamics of EM wave interaction with nanostructures with minimal computation requirements. As a proof-of-concept, we applied such an efficacious method to design a new class of on-demand reconfigurable optical metasurfaces based on phase-change materials (PCMs). The experimental results of the fabricated devices are in good agreement with those predicted by the proposed approach. We envision that the integration of such a DL-based technique with full-wave commercial software packages offers a powerful toolkit to facilitate the analysis, design, and optimization of the EM nanostructures as well as explaining, understanding, and predicting the observed responses in such structures. It will thus enable to solve complex design problems that could not be solved with existing techniques.



中文翻译:

基于降维的深度学习方法设计电磁纳米结构

在本文中,我们演示了一种基于深度学习(DL)技术的高效计算新方法,用于电磁(EM)纳米结构的分析,设计和优化。我们使用通用EM问题的特征之间的强相关性来显着降低问题的维数,从而降低计算复杂度,而不会造成很大的错误。通过使用最近证明的自动编码器技术采用降维概念,我们将EM纳米结构中的常规多对一设计问题重新定义为一对一问题以及更简单的多对一问题,可以只需使用解析公式即可解决。这种方法在解决正向问题(即分析)和逆向问题(即 与传统方法相比)。此外,它提供了分析公式,尽管它们很复杂,但可以用最少的计算需求来直观地了解EM波与纳米结构相互作用的物理和动力学。作为概念验证,我们应用了一种有效的方法来设计基于相变材料(PCM)的新型按需可重构光学超表面。所制造的装置的实验结果与所提出的方法所预测的结果非常吻合。我们设想将这种基于DL的技术与全波商业软件包集成在一起,将提供一个功能强大的工具包,以促进EM纳米结构的分析,设计和优化以及解释,理解,并预测在这种结构中观察到的响应。因此,它将能够解决现有技术无法解决的复杂设计问题。

更新日期:2020-02-04
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