Deep neural networks for the evaluation and design of photonic devices

By Jiaqi Jiang, Mingkun Chen, Jonathan A. Fan. 2020

528 citations recorded by the source corpus.

The data-science revolution is poised to transform the way photonic systems are simulated and designed. Photonic systems are, in many ways, an ideal substrate for machine learning: the objective of much of computational electromagnetics is the capture of nonlinear relationships in high-dimensional spaces, which is the core strength of neural networks. Additionally, the mainstream availability of Maxwell solvers makes the training and evaluation of neural networks broadly accessible and tailorable to specific problems. In this Review, we show how deep neural networks, configured as discriminative networks, can learn from training sets and operate as high-speed surrogate electromagnetic solvers. We also examine how deep generative networks can learn geometric features in device distributions and even be configured to serve as robust global optimizers. Fundamental data-science concepts framed within the context of photonics are also discussed, including the network-training process, delineation of different network classes and architectures, and dimensionality reduction. Neural networks can capture nonlinear relationships in high-dimensional spaces and are powerful tools for photonic-system modelling. This Review discusses how deep neural networks can serve as surrogate electromagnetic solvers, inverse modelling tools and global device optimizers.

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

1. Yongmin Liu
2. Yihao Xu
3. Peter R. Wiecha
4. Yichen Shen
5. Yun Raymond Fu
6. O. Muskens
7. Yu Kong

In Atomic and Molecular Physics, and Optics

1. Yongmin Liu
2. Xianzhe Zhang
3. Yun Raymond Fu
4. Yihao Xu
5. Peter R. Wiecha
6. O. Muskens
7. C. Girard
8. A. Arbouet

In Electrical and Electronic Engineering

1. Peter R. Wiecha
2. Yun Raymond Fu
3. C. Girard
4. O. Muskens
5. A. Arbouet

Citations in the corpus

• Interfacing photonics with artificial intelligence: an innovative design strategy for photonic structures and devices based on artificial neural networks (2021, background): “Artificial neural networks (ANNs) provide a new and powerful approach for photonic designs [29 – 37].”
• Photonic matrix multiplication lights up photonic accelerator and beyond (2022, background): “A lot of review works on photonic acceleration have been made, these works mainly focused on integrated photonic neuro-morphic systems 1,15,23 – 28 , nanophotonics and machine learning blend 29,30 , reservoir computing 31 , programmable nanophotonics 21,22,32 .”
• Deep learning in nano-photonics: inverse design and beyond (2020, background): “For an exhaustive overview we therefore invite the reader to consult these articles [29–33].”