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

Deep learning is now playing a major role in designing photonic devices, including
nanostructured photonics. In this article, we investigate three models for designing …

Deep neural networks (DNNs) have been introduced to achieve the rapid design of photonic
devices by creating a nonlinear function mapping the geometric structure to the optical …

Engineering a physical system to feature designated characteristics states an inverse design
problem, which is often determined by several discrete and continuous parameters. If such a …

Waveguide bend is an indispensable component in the on-chip compact photonic integrated
circuits (PICs) and the minimum bend size greatly limits the increase of integration density of …

This article applies deep learning-accelerated inverse design algorithms and discovers a
spectrum of photonic power dividers with exceptional performance metrics despite the …

We demonstrate three ultra-compact integrated-photonics devices, which are designed via a
machine-learning algorithm coupled with finite-difference time-domain (FDTD) modeling. By …

The on-chip nano-integration of large-scale optical phased arrays (OPAs) is a development
trend. However, the current scale of integrated OPAs is not large because of the limitations …

Recently proposed nonvolatile chalcogenide phase change materials Sb 2 Se 3 and Sb 2 S
3 exhibit low loss and significant refractive index modulation in the visible and NIR, which …

We demonstrate efficient ultra-compact power splitters designed via machine learning
algorithm viz. binary-additive reinforcement learning algorithm. Two different splitter …