A Deep Learning Approach for Objective-Driven All-Dielectric Metasurface Design

By S. An, Clayton Fowler, B. Zheng, M. Shalaginov, Hong Tang, Hang Li, Li Zhou, Jun Ding, Anuradha Murthy Agarwal, C. Rivero‐Baleine and 4 more. ACS Photonics, 2019

340 citations recorded by the source corpus.

Metasurfaces have become a promising means for manipulating optical wavefronts in flat and high-performance optical devices. Conventional metasurface device design relies on trial-and-error methods...

Subfields: Developmental Biology, Electrical and Electronic Engineering, Electronic, Optical and Magnetic Materials

View Paper: https://www.semanticscholar.org/paper/09e159ab17b336e2e33ab871cba54fd6d72c4585

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Electrical and Electronic Engineering

1. Peter R. Wiecha
2. C. Girard
3. O. Muskens
4. A. Arbouet

In Electronic, Optical and Magnetic Materials

1. Peter R. Wiecha
2. C. Girard
3. O. Muskens
4. A. Arbouet

Citations in the corpus

• Deep learning in nano-photonics: inverse design and beyond (2020, background): “Recently, the idea has been picked up and it has been shown, for instance, that scattering and extinction spectra can be predicted with high accuracy [52] and also that the phase can be included in the predictions [53], which is important for nano-structures in metasurfaces.”