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Physics-informed Neural Networks for Imaging and Parameter Retrieval of Photonic Nanostructures from Near-field Data by Chen, Yuyao; Negro, Luca Dal is a scholarly article available to read on EtoBox.

What is Physics-informed Neural Networks for Imaging and Parameter Retrieval of Photonic Nanostructures from Near-field Data about?

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

Author

Chen, Yuyao; Negro, Luca Dal

Published

2021

Language

EN