Optimization of metamaterials and metamaterial-microcavity based on deep neural networks

Abstract

Computational inverse-design and forward perdition approaches provide promising approaches for on-demand nanophotonics. Here, we use a deep-learning method to optimize the design of split-ring metamaterial and metamaterial-microcavities. Once the deep...

Inverse Design of Photonic Surfaces via High throughput Femtosecond Laser Processing and Tandem Neural Networks

“…TNN frameworks combine forward and inverse DNNs and are gaining prominence over alternatives such as AAs, VAEs, and GANs for materials' inverse design. [ 10 , 16 , 21 ] The TNN ensures independent optimization of forward and inverse models for easier training and more precise results. This separation enhances interpretability crucial for the complex nonlinear landscape of laser parameter based photonic surfaces design.…”

“…However, these approaches often fail to adequately handle and make use of one‐to‐many mapping scenarios and nonlinear relationships between the design input and output spaces that are common in real‐world manufacturing. [ 16 ] This challenge becomes particularly pronounced when training data overly emphasizes specific correlations over others. Furthermore, they frequently overlook the practical constraints of real‐world fabrication, scalability, and uncertainties; they are often entirely computational, lacking experimental validation of design predictions from the trained model, particularly for novel situations.…”

Design and Optimization of Silver Nanostructured Arrays in Plasmonic Metamaterials for Sensitive Imaging Applications

“…For example, to improve imaging sensitivity through surface-enhanced Raman scattering (SERS), various approaches have been utilized, including the application of graphene oxide on magnetron-sputtered silver (Ag) thin films [34], the creation of Ag nanowire arrays on paper via automated drawing methods [35], and the synthesis of various Ag-nanostructured substrates through physical vapor deposition and chemical synthetic routes [36], all aimed at the optimization of Ag-nanostructured array structures. Furthermore, the use of machine learning with deep neural networks has been explored for the design and optimization of comprehensive nanostructured array structures [37][38][39].…”

High-Resolution Metalens Imaging with Sequential Artificial Intelligence Models

“…However, conventional refractive lenses remain bulky, thereby hindering the miniaturization of optical components. There has been significant progress in the development of nanostructures on subwavelength scales, leading to a growing interest in metasurfaces. − These nanophotonic metasurfaces make use of collective resonances to manipulate the attributes of electromagnetic waves at the nanoscale. − This advancement opens up possibilities for the creation of low-profile optical components and the demonstration of extensive applications, including active LiDAR, − quantum emission control, − and optical information encryption …”