Machine learning and evolutionary algorithm studies of graphene metamaterials for optimized plasmon-induced transparency

Abstract

Machine learning and optimization algorithms have been widely applied in the design and optimization for photonic devices. In this article, we briefly review recent progress of this field of research and show some data-driven applications (e.g. spectrum prediction, inverse design and performance optimization) for novel graphene metamaterials (GMs). The structure of the GMs is well-designed to achieve the wideband plasmon induced transparency effect, which is regarded as optimization object and can be theoretically demonstrated by using transfer matrix method. Some classical machine learning algorithms, including k nearest neighbour, decision tree, random forest and artificial neural networks, are utilized to equivalently substitute the numerical simulation in the forward spectrum prediction and complete the inverse design for the GMs. The calculated results demonstrate that all the algorithms are effective and the random forest has advantages in terms of accuracy and training speed. Moreover, the single-objective and multi-objective optimization algorithms are used to achieve steep transmission characteristics by synthetically taking many performance metrics into consideration. The maximum difference between the transmission peaks and dips in the optimized transmission spectrum can reach 0.97. In comparison to previous works, we provide a guidance for intelligent design of photonic devices and advanced materials based on machine learning and evolutionary algorithms.

arXiv comment12 pages, 7 figures

References in corpus (26)

• Deep Learning in Neural Networks: An Overview
• Universal Dynamic Conductivity and Quantized Visible Opacity of Suspended Graphene
• Plasmonics in graphene at infra-red frequencies
• Outlook for inverse design in nanophotonics
• Mid-Infrared Plasmonic Biosensing with Graphene
• Training deep neural networks for the inverse design of nanophotonic structures
• Inverse design and demonstration of a compact and broadband on-chip wavelength demultiplexer
• Strong nonlinear optical response of graphene flakes measured by four-wave mixing
• A Generative Model for Inverse Design of Metamaterials
• A perfect absorber made of a graphene micro-ribbon metamaterial
• Edge and waveguide THz surface plasmon modes in graphene micro-ribbons
• Training of photonic neural networks through in situ backpropagation
• Active modulation of electromagnetically induced transparency analogue in terahertz hybrid metal-graphene metamaterials
• Freeform Diffractive Metagrating Design Based on Generative Adversarial Networks
• Adjoint method and inverse design for nonlinear nanophotonic devices
• Intelligent Nanophotonics: Merging Photonics and Artificial Intelligence at the Nanoscale
• Thermal Hyperbolic Metamaterials
• Optimization of photonic crystal nanocavities based on deep learning
• Response of graphene to femtosecond high-intensity laser irradiation
• Photo-thermoelectric and photoelectric contributions to light detection in metal-graphene-metal photodetectors
• Efficient spectrum prediction and inverse design for plasmonic waveguide systems based on artificial neural networks
• Mapping the global design space of nanophotonic components using machine learning pattern recognition
• Plasmonic colours predicted by deep learning
• Designing Silicon Photonic Devices using Artificial Neural Networks
• Efficient training and design of photonic neural network through neuroevolution
• Iterative optimization of photonic crystal nanocavity designs by using deep neural networks