69 citations · 146 across the 6 of their papers we have counts for
6 papers
Data-driven Azimuthal RHEED construction for in-situ crystal growth characterization
Abdourahman Khaireh-Walieh, Alexandre Arnoult, Sébastien Plissard +1
Reflection High-Energy Electron Diffraction (RHEED) is a powerful tool to probe the surface reconstruction during MBE growth. However, raw RHEED patterns are difficult to interpret…
Illustrated tutorial on global optimization in nanophotonics
Pauline Bennet, Denis Langevin, Chaymae Essoual +4
Numerical optimization for the inverse design of photonic structures is a tool which is providing increasingly convincing results -- even though the wave nature of problems in phot…
PyMoosh : a comprehensive numerical toolkit for computing the optical properties of multilayered structures
Denis Langevin, Pauline Bennet, Abdourahman Khaireh-Walieh +3
We present PyMoosh, a Python-based simulation library designed to provide a comprehensive set of numerical tools allowing to compute essentially all optical characteristics of mult…
A newcomer's guide to deep learning for inverse design in nano-photonics
Abdourahman Khaireh-Walieh, Denis Langevin, Pauline Bennet +3
Nanophotonic devices manipulate light at sub-wavelength scales, enabling tasks such as light concentration, routing, and filtering. Designing these devices is a challenging task. T…
Monitoring MBE substrate deoxidation via RHEED image-sequence analysis by deep learning
Abdourahman Khaireh-Walieh, Alexandre Arnoult, Sébastien Plissard +1
Reflection high-energy electron diffraction (RHEED) is a powerful tool in molecular beam epitaxy (MBE), but RHEED images are often difficult to interpret, requiring experienced ope…
Inverse design with flexible design targets via deep learning: Tailoring of electric and magnetic multipole scattering from nano-spheres
Ana Estrada-Real, Abdourahman Khaireh-Walieh, Bernhard Urbaszek +1
Deep learning is a promising, ultra-fast approach for inverse design in nano-optics, but despite fast advancement of the field, the computational cost of dataset generation, as wel…