Nanophotonic devices manipulate light at sub-wavelength scales, enabling tasks such as
light concentration, routing, and filtering. Designing these devices to achieve precise light …

This paper proposes a deep-learning-based inverse design framework for a one-
dimensional, defective phononic crystal (PnC) as a narrow bandpass filter under …

Hydrogel materials endow the flat optics platform with active tuning capability, owing to their
remarkable humidity‐responsive swelling behavior. Despite recent advances in hydrogel …

Photonics inverse design relies on human experts to search for a design topology that
satisfies certain optical specifications with their experience and intuitions, which is relatively …

Conventional meta-atom designs rely heavily on researchers' prior knowledge and trial-and-
error searches using full-wave simulations, resulting in time-consuming and inefficient …

Artificial intelligence applications to enhance materials science have reduced the efforts and
costs of developing new materials. Although it is still a recent research field, some promising …

Optical multilayer thin film structures have been widely used in numerous photonic
applications. However, existing inverse design methods have many drawbacks because …

Deep learning models, with a prerequisite of large databases, are common approaches in
applying machine learning for inverse design in photonics. For these models, less …

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 …

Herein, deep learning (DL) is used to predict the structural parameters of Ag nanohole
arrays (NAs) for spectrum‐driving and color‐driving plasmonic applications. A dataset of …