494 citations · 1.9k across the 37 of their papers we have counts for
38 papers
PyMieDiff: A differentiable Mie scattering library
Oscar K. C. Jackson, Simone De Liberato, Otto L. Muskens +1
Light scattering by spherical-shaped particles of sizes comparable to the wavelength is foundational in many areas of science, from chemistry to atmospheric science, photonics and…
Programmable metasurfaces for future photonic artificial intelligence
Loubnan Abou-Hamdan, Emil Marinov, Peter Wiecha +3
Photonic neural networks (PNNs), which share the inherent benefits of photonic systems, such as high parallelism and low power consumption, could challenge traditional digital neur…
TorchGDM: A GPU-Accelerated Python Toolkit for Multi-Scale Electromagnetic Scattering with Automatic Differentiation
Sofia Ponomareva, Adelin Patoux, Clément Majorel +5
We present "torchGDM", a numerical framework for nano-optical simulations based on the Green's Dyadic Method (GDM). This toolkit combines a hybrid approach, allowing for both fully…
Silicon nanoantennas for tailoring the optical properties of MoS2 monolayers
Danae Katrisioti, Peter R. Wiecha, Aurélien Cuche +11
Silicon-based dielectric nanoantennas provide an effective platform for engineering light-matter interactions in van der Waals semiconductors. Here, we demonstrate near-field coupl…
Gradient-Based Optimization of Core-Shell Particles with Discrete Materials for Directional Scattering
Dalin Soun, Antoine Azéma, Lucien Roach +2
Designing nanophotonic structures traditionally grapples with the complexities of discrete parameters, such as real materials, often resorting to costly global optimization methods…
Inverse Design of Unitary Transmission Matrices in Silicon Photonic Coupled Waveguide Arrays using a Neural Adjoint Model
Thomas W. Radford, Peter R. Wiecha, Alberto Politi +2
The development of low-loss reconfigurable integrated optical devices enables further research into technologies including photonic signal processing, analogue quantum computing, a…
Deep learning for nano-photonic materials -- The solution to everything!?
Peter R. Wiecha
Deep learning is currently being hyped as an almost magical tool for solving all kinds of difficult problems that computers have not been able to solve in the past. Particularly in…
Directional silicon nano-antennas for quantum emitter control designed by evolutionary optimization
Romain Hernandez, Peter R. Wiecha, Jean-Marie Poumirol +5
We optimize silicon nano-antennas to enhance and steer the emission of local quantum sources. We combine global evolutionary optimization (EO) with frequency domain electrodynamica…
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…
Probing the optical near-field interaction of Mie nanoresonators with atomically thin semiconductors
Ana Estrada-Real, Ioannis Paradisanos, Peter R. Wiecha +10
Optical Mie resonators based on silicon nanostructures allow tuning of light-matter-interaction with advanced design concepts based on CMOS compatible nanofabrication. Optically ac…
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…
One pot chemical vapor deposition of high optical quality large area monolayer Janus transition metal dichalcogenides
Ziyang Gan, Ioannis Paradisanos, Ana Estrada-Real +13
We report one-pot chemical vapor deposition (CVD) growth of large-area Janus SeMoS monolayers, with the asymmetric top (Se) and bottom (S) chalcogen atomic planes with respect to t…
Infrared nanoplasmonic properties of hyperdoped embedded Si nanocrystals in the few electrons regime
Meiling Zhang, Jean-Marie Poumirol, Nicolas Chery +13
Using Localized Surface Plasmon Resonance (LSPR) as an optical probe we demonstrate the presence of free carriers in phosphorus doped silicon nanocrystals (SiNCs) embedded in a sil…
Generalizing the exact multipole expansion: Density of multipole modes in complex photonic nanostructures
Clément Majorel, Adelin Patoux, Ana Estrada-Real +4
The multipole expansion of a nano-photonic structure's electromagnetic response is a versatile tool to interpret optical effects in nano-optics, but it only gives access to the mod…
Magnetic and electric Purcell factor control through geometry optimization of high index dielectric nanostructures
Yoann Brûlé, Peter R. Wiecha, Aurélien Cuche +2
We design planar silicon antennas for controlling the emission rate of magnetic or electric dipolar emitters. Evolutionary algorithms coupled to the Green Dyadic Method lead to dif…
Deep learning enabled strategies for modelling of complex aperiodic plasmonic metasurfaces of arbitrary size
Clément Majorel, Christian Girard, Arnaud Arbouet +2
Optical interactions have an important impact on the optical response of nanostructures in complex environments. Accounting for interactions in large ensembles of structures requir…
pyGDM -- new functionalities and major improvements to the python toolkit for nano-optics full-field simulations
Peter R. Wiecha, Clément Majorel, Arnaud Arbouet +4
pyGDM is a python toolkit for electro-dynamical simulations of individual nano-structures, based on the Green Dyadic Method (GDM). pyGDM uses the concept of a generalized propagato…
Deep learning in nano-photonics: inverse design and beyond
Peter R. Wiecha, Arnaud Arbouet, Christian Girard +1
Deep learning in the context of nano-photonics is mostly discussed in terms of its potential for inverse design of photonic devices or nanostructures. Many of the recent works on m…
Hyper-doped silicon nanoantennas and metasurfaces for tunable infrared plasmonics
Jean-Marie Poumirol, Clément Majorel, Nicolas Chery +11
We present the experimental realization of ordered arrays of hyper-doped silicon nanodisks, which exhibit a localized surface plasmon resonance. The plasmon is widely tunable in a…
Deep learning enabled design of complex transmission matrices for universal optical components
Nicholas J. Dinsdale, Peter R. Wiecha, Matthew Delaney +8
Recent breakthroughs in photonics-based quantum, neuromorphic and analogue processing have pointed out the need for new schemes for fully programmable nanophotonic devices. Univers…
Unveiling the optical emission channels of monolayer semiconductors coupled to silicon nanoantennas
Jean-Marie Poumirol, Ioannis Paradisanos, Shivangi Shree +13
Monolayers (MLs) of transition metal dichalcogenides (TMDs) such as WSe2 and MoSe2 can be placed by dry stamping directly on broadband dielectric resonators, which have the ability…
Quantum Theory of Near-field Optical Imaging with Rare-earth Atomic Clusters
Clément Majorel, Christian Girard, Aurélien Cuche +2
Scanning near-field optical imaging (SNOM) using local active probes provides in general images of the electric part of the photonic local density of states. However, certain atomi…
Polarizabilities of complex individual dielectric or plasmonic nanostructures
Adelin Patoux, Clément Majorel, Peter R. Wiecha +4
When the sizes of photonic nanoparticles are much smaller than the excitation wavelength, their optical response can be efficiently described with a series of polarizability tensor…