Over the years, researchers have been exploring ways to artificially design chiral structures
and materials, namely metamaterials and metasurfaces. They exhibit unique optical …

Abstract Machine learning holds significant research potential in the field of nanotechnology,
enabling nanomaterial structure and property predictions, facilitating materials design and …

Optical chirality is highly demanded for biochemical sensing, spectral detection, and
advanced imaging, however, conventional design schemes for chiral metamaterials require …

This article presents an inverse design technique of multilayer deep-etched grating (MDEG)
using a deep neural network with adaptive solution space. MDEG is a key component in …

Graphene, as a widely used nanomaterial, has shown great flexibility in designing optically
transparent microwave metasurfaces with broadband absorption. However, the design of …

In this work, we review the last attempts to use nanostructured materials for the
enhancement of the chiro-optical effects at the nanoscale. Starting from the numerical …

In nanophotonics, nanohole arrays (NHAs) are periodic arrangements of nanoscale
apertures in thin films that provide diverse optical functionalities essential for various …

Recently, deep reinforcement learning (DRL) for metasurface design has received
increased attention for its excellent decision-making ability in complex problems. However …

In this paper, a deep learning-based method for random coding metasurface design is
proposed. This method involves constructing a residual convolutional neural network to …

Optical nano-structure designs usually employ computationally expensive and time-
intensive electromagnetic (EM) simulations that call for resorting to modern-day data …