Interfacing photonics with artificial intelligence: an innovative design strategy for photonic structures and devices based on artificial neural networks
By Yihao Xu, Xianzhe Zhang, Yun Raymond Fu, Yongmin Liu. 2021
58 citations recorded by the source corpus.
Over the past decades, photonics has transformed many areas in both fundamental research and practical applications. In particular, we can manipulate light in a desired and prescribed manner by rationally designed subwavelength structures. However, constructing complex photonic structures and devices is still a time-consuming process, even for experienced researchers. As a subset of artificial intelligence, artificial neural networks serve as one potential solution to bypass the complicated design process, enabling us to directly predict the optical responses of photonic structures or perform the inverse design with high efficiency and accuracy. In this review, we will introduce several commonly used neural networks and highlight their applications in the design process of various optical structures and devices, particularly those in recent experimental works. We will also comment on the future directions to inspire researchers from different disciplines to collectively advance this emerging research field.
Subfields: Artificial Intelligence, Atomic and Molecular Physics, and Optics, Electrical and Electronic Engineering
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Zhi Chen
- Cynthia Rudin
- Tong Wang
- Octavio Loyola-González
- Berk Ustun
- Lesia Semenova
- Chudi Zhong
- S. Lo Piano
In Atomic and Molecular Physics, and Optics
In Electrical and Electronic Engineering
Citations in the corpus
- How to See Hidden Patterns in Metamaterials with Interpretable Machine Learning (2021, background): “[0, 10] kHz [10, 20] kHz [20, 30] kHz [30, 40] kHz [40, 50] kHz”