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arXiv 2109.12754physics.opticsphysics.comp-ph
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基于物理信息神经网络的近场数据光子纳米结构成像与参数反演

Physics-informed neural networks for imaging and parameter retrieval of photonic nanostructures from near-field data

Yuyao Chen, Luca Dal Negro

AI总结:

本文提出基于物理信息神经网络的全矢量麦克斯韦方程反演方法,从近场数据中同时恢复纳米结构的复介电常数和磁导率,并引入自适应损失权重提升高折射率材料重构精度,实现高效非侵入式三维参数成像。

摘要:

在本文中,我们开发了一种深度学习方法,用于精确求解近场显微术中的挑战性问题,该方法利用物理信息神经网络(PINNs)的强大框架来反演纳米结构环境的复杂光学参数。具体而言,我们表明可以基于全矢量麦克斯韦方程组灵活设计PINNs,从近场数据中反向获取共振区未知散射物体的复介电常数和复磁导率的空间分布。此外,我们证明PINNs在平面波和点源(局域)激发下都能出色地收敛到真实材料参数,从而能够在扫描近场光学显微镜(SNOM)中实现参数反演。与传统的依赖数据的深度学习方法相比,我们的方法计算效率更高,因为训练只需要单个数据集。此外,我们开发并成功演示了具有可训练损失权重的自适应PINNs,与标准PINNs相比,该方法大幅提高了高折射率材料逆重构的精度。最后,我们通过从近场数据中反演三维(3D)未知物体的空间相关介电常数,展示了该方法的全部潜力。所提出的框架为开发一种计算驱动、精确且非侵入式的平台铺平了道路,该平台可从测得的光学图像中同时反演共振纳米结构的电参数和磁参数,并可用于生物医学成像、光学遥感以及超材料器件的表征。

英文摘要:

In this paper, we develop a deep learning approach for the accurate solution of challenging problems of near-field microscopy that leverages the powerful framework of physics-informed neural networks (PINNs) for the inversion of the complex optical parameters of nanostructured environments. Specifically, we show that PINNs can be flexibly designed based on the full-vector Maxwell's equations to inversely retrieve the spatial distributions of the complex electric permittivity and magnetic permeability of unknown scattering objects in the resonance regime from near-field data. Moreover, we demonstrate that PINNs achieve excellent convergence to the true material parameters under both plane wave and point source (localized) excitations, enabling parameter retrieval in scanning near-field optical microscopy (SNOM). Our method is computationally efficient compared to traditional data-driven deep learning approaches as it requires only a single dataset for training. Furthermore, we develop and successfully demonstrate adaptive PINNs with trainable loss weights that largely improve the accuracy of the inverse reconstruction for high-index materials compared to standard PINNs. Finally, we demonstrate the full potential of our approach by retrieving the space-dependent permittivity of a three-dimensional (3D) unknown object from near-field data. The presented framework paves the way to the development of a computationally-driven, accurate, and non-invasive platform for the simultaneous retrieval of the electric and magnetic parameters of resonant nanostructures from measured optical images, with applications to biomedical imaging, optical remote sensing, and characterization of metamaterial devices.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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