arXiv subjects

Peter R. Wiecha

Publications and source records attributed to Peter R. Wiecha.

At least 19 recordsLinked to original sources

arXiv2026

Frugal Effective Models for Nanophotonic Scattering: Optimizing Global Polarizability Matrices for Metasurface Design

Accurate nano-photonics simulations of large scale devices like optical metasurfaces require high accuracy reduced models for the device constituents. We present an automated framework for the optimization of Global Polarizability Matrix (GPM) models, which represent a complex scatterer as a small set of non-local effective dipoles. Our goal is to find the most frugal model that reproduces a particle's scattering response within a user-defined accuracy. The method iteratively removes redundant dipoles while re-adapting the positions of the remaining ones via gradient based optimization, stopping at the smallest model that still meets the target. Automatic differentiation, combined with an untrained neural network that reparametrizes the dipole positions, helps to place the dipoles at physically intuitive locations. We demonstrate the versatility of this approach across diverse geometries, from two dimensional ridges over simple spheres to complex three-dimensional particles, achieving compression factors of typically two orders of magnitude compared to full-wave simulations, for target accuracies in the order of few percent. We finally demonstrate how accurate, frugal effective models enable large-scale meta-deflector optimization without periodic approximations. This robust recipe for constructing frugal effective models paves the way for the rapid simulation of large-scale photonic assemblies, required for example for metasurface design.

physics.optics↗

arXiv2026

PyMieDiff: A differentiable Mie scattering library

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 nanotechnology. With the new capabilities offered by machine learning, there is a great interest in end-to-end differentiable frameworks for scattering calculations. Here we introduce PyMieDiff, a fully differentiable, GPU-compatible implementation of Mie scattering for layered, spherical particles in PyTorch. The library provides native, autograd-compatible spherical Bessel and Hankel functions, vectorized evaluation of Mie coefficients, and APIs for computing efficiencies, angular scattering, and near-fields. All inputs - geometry, material dispersion, wavelengths, and observation angles and positions - are represented as tensors, enabling seamless integration with gradient-based optimisation or physics-informed neural networks. The toolkit can also be combined with "TorchGDM" for end-to-end differentiable multi-particle scattering simulations. PyMieDiff is available under an open source licence at https://github.com/UoS-Integrated-Nanophotonics-group/MieDiff.

physics.optics↗

arXiv2025

Silicon nanoantennas for tailoring the optical properties of MoS2 monolayers

Silicon-based dielectric nanoantennas provide an effective platform for engineering light-matter interactions in van der Waals semiconductors. Here, we demonstrate near-field coupling between monolayer MoS2 and silicon nanoantennas arranged in hexagonal lattices with tunable geometric parameters, leading to a three-fold enhancement in photoluminescence and an excitation-wavelength-dependent emission that aligns with Mie-resonant modes. Raman spectroscopy reveals an up to 8-fold enhancement in the vibrational modes of MoS2, while second-harmonic generation exhibits a 20 to 30-fold increase in efficiency, closely correlating with the presence of the underlying nanoantennas. Our experiments and simulations quantify the tunable benefits of the near-field interactions, taking into account thin-film interference and strain-induced effects. Our findings present dielectric nanoantennas as a promising platform for tailoring linear and nonlinear optical properties in 2D materials, with potential applications in nanophotonic devices and integrated photonics.

physics.optics↗

arXiv2025

TorchGDM: A GPU-Accelerated Python Toolkit for Multi-Scale Electromagnetic Scattering with Automatic Differentiation

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 discretized nano-structures and structures approximated by sets of effective electric and magnetic dipoles. It supports simulations in three dimensions and for infinitely long, two-dimensional structures. This capability is particularly suited for multi-scale modeling, enabling accurate near-field calculations within or around a discretized structure embedded in a complex environment of scatterers represented by effective models. Importantly, torchGDM is entirely implemented in PyTorch, a well-optimized and GPU-enabled automatic differentiation framework. This allows for the efficient calculation of exact derivatives of any simulated observable with respect to various inputs, including positions, wavelengths or permittivity, but also intermediate parameters like Green's tensor components, which can be interesting for physics informed deep learning applications. We anticipate that this toolkit will be valuable for applications merging nano-photonics and machine learning, as well as for solving nano-photonic optimization and inverse problems, such as the global design and characterization of metasurfaces, where optical interactions between structures are critical.

physics.optics↗

arXiv2025

Data-driven Azimuthal RHEED construction for in-situ crystal growth characterization

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, especially when the wafer is rotating. A more accessible representation of the information is therefore the so-called Azimuthal RHEED (ARHEED), an angularly resolved plot of the electron diffraction pattern during a full wafer rotation. However, ARHEED requires precise information about the rotation angle as well as of the position of the specular spot of the electron beam. We present a Deep Learning technique to automatically construct the Azimuthal RHEED from bare RHEED images, requiring no further measurement equipment. We use two artificial neural networks: an image segmentation model to track the center of the specular spot and a regression model to determine the orientation of the crystal with respect to the incident electron beam of the RHEED system. Our technique enables accurate, and potentially real-time ARHEED construction on any growth chamber equipped with a RHEED system.

cond-mat.mes-hall↗

arXiv2025

Gradient-Based Optimization of Core-Shell Particles with Discrete Materials for Directional Scattering

Designing nanophotonic structures traditionally grapples with the complexities of discrete parameters, such as real materials, often resorting to costly global optimization methods. This paper introduces an approach that leverages generative deep learning to map discrete parameter sets into a continuous latent space, enabling direct gradient-based optimization. For scenarios with non-differentiable physics evaluation functions, a neural network is employed as a differentiable surrogate model. The efficacy of this methodology is demonstrated by optimizing the directional scattering properties of core-shell nanoparticles composed of a selection of realistic materials. We derive suggestions for core-shell geometries with strong forward scattering and minimized backscattering. Our findings reveal significant improvements in computational efficiency and performance when compared to global optimization techniques. Beyond nanophotonics design problems, this framework holds promise for broad applications across all types of inverse problems constrained by discrete variables.

physics.optics↗

arXiv2024

Inverse Design of Unitary Transmission Matrices in Silicon Photonic Coupled Waveguide Arrays using a Neural Adjoint Model

The development of low-loss reconfigurable integrated optical devices enables further research into technologies including photonic signal processing, analogue quantum computing, and optical neural networks. Here, we introduce digital patterning of coupled waveguide arrays as a platform capable of implementing unitary matrix operations. Determining the required device geometry for a specific optical output is computationally challenging and requires a robust and versatile inverse design protocol. In this work we present an approach using high speed neural network surrogate based gradient optimization, capable of predicting patterns of refractive index perturbations based on switching of the ultra-low loss chalcogenide phase change material, antimony tri-selinide ($\text{Sb}_{2}\text{Se}_{3}$). Results for a $3 \times 3$ silicon waveguide array are presented, demonstrating control of both amplitude and phase for each transmission matrix element. Network performance is studied using neural network optimization tools such as dataset augmentation and supplementation with random noise, resulting in an average fidelity of 0.94 for unitary matrix targets. Our results show that coupled waveguide arrays with perturbation patterns offer new routes for achieving programmable integrated photonics with a reduced footprint compared to conventional interferometer-mesh technology.

physics.optics↗

arXiv2023

Deep learning for nano-photonic materials -- The solution to everything!?

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 the fields of computer vision and natural language processing, spectacular results have been achieved. The hype has now infiltrated several scientific communities. In (nano-)photonics, researchers are trying to apply deep learning to all kinds of forward and inverse problems. A particularly challenging problem is for instance the rational design of nanophotonic materials and devices. In this opinion article, I will first discuss the public expectations of deep learning and give an overview of the quite different scales at which actors from industry and research are operating their deep learning models. I then examine the weaknesses and dangers associated with deep learning. Finally, I'll discuss the key strengths that make this new set of statistical methods so attractive, and review a personal selection of opportunities that shouldn't be missed in the current developments.

physics.comp-ph↗

arXiv2023

A newcomer's guide to deep learning for inverse design in nano-photonics

Nanophotonic devices manipulate light at sub-wavelength scales, enabling tasks such as light concentration, routing, and filtering. Designing these devices is a challenging task. Traditionally, solving this problem has relied on computationally expensive, iterative methods. In recent years, deep learning techniques have emerged as promising tools for tackling the inverse design of nanophotonic devices. While several review articles have provided an overview of the progress in this rapidly evolving field, there is a need for a comprehensive tutorial that specifically targets newcomers without prior experience in deep learning. Our goal is to address this gap and provide practical guidance for applying deep learning to individual scientific problems. We introduce the fundamental concepts of deep learning and critically discuss the potential benefits it offers for various inverse design problems in nanophotonics. We present a suggested workflow and detailed, practical design guidelines to help newcomers navigate the challenges they may encounter. By following our guide, newcomers can avoid frustrating roadblocks commonly experienced when venturing into deep learning for the first time. In a second part, we explore different iterative and direct deep learning-based techniques for inverse design, and evaluate their respective advantages and limitations. To enhance understanding and facilitate implementation, we supplement the manuscript with detailed Python notebook examples, illustrating each step of the discussed processes. While our tutorial primarily focuses on researchers in (nano-)photonics, it is also relevant for those working with deep learning in other research domains. We aim at providing a solid starting point to empower researchers to leverage the potential of deep learning in their scientific pursuits.

physics.optics↗

arXiv2023

Directional silicon nano-antennas for quantum emitter control designed by evolutionary optimization

We optimize silicon nano-antennas to enhance and steer the emission of local quantum sources. We combine global evolutionary optimization (EO) with frequency domain electrodynamical simulations, and compare design strategies based on resonant and non-resonant building blocks. Specifically, we investigate the performance of models with different degrees of freedom but comparable amount of available material. We find that simpler geometric models allow significantly faster convergence of the optimizer, which, expectedly, comes at the cost of a reduced optical performance. We finally analyze the physical mechanisms underlying the directional emission that also comes with an emission rate enhancement, and find a surprising robustness against perturbations of the source emitter location. This makes the structures highly interesting for actual nano-fabrication. We believe that optimized, all-dielectric silicon nano-antennas have high potential for genuine breakthroughs in a multitude of applications in nanophotonics and quantum technologies.

physics.optics↗

arXiv2022

Monitoring MBE substrate deoxidation via RHEED image-sequence analysis by deep learning

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 operators. We present an approach for automated surveillance of GaAs substrate deoxidation in MBE reactors using deep learning based RHEED image-sequence classification. Our approach consists of an non-supervised auto-encoder (AE) for feature extraction, combined with a supervised convolutional classifier network. We demonstrate that our lightweight network model can accurately identify the exact deoxidation moment. Furthermore we show that the approach is very robust and allows accurate deoxidation detection during months without requiring re-training. The main advantage of the approach is that it can be applied to raw RHEED images without requiring further information such as the rotation angle, temperature, etc.

cond-mat.mes-hall↗

arXiv2022

Probing the optical near-field interaction of Mie nanoresonators with atomically thin semiconductors

Optical Mie resonators based on silicon nanostructures allow tuning of light-matter-interaction with advanced design concepts based on CMOS compatible nanofabrication. Optically active materials such as transition-metal dichalcogenide (TMD) monolayers can be placed in the near-field region of such Mie resonators. Here, we experimentally demonstrate and verify by numerical simulations coupling between a MoSe2 monolayer and the near-field of dielectric nanoresonators. Through a comparison of dark-field (DF) scattering spectroscopy and photoluminescence excitation experiments (PLE), we show that the MoSe2 absorption can be enhanced via the near-field of a nanoresonator. We demonstrate spectral tuning of the absorption via the geometry of individual Mie resonators. We show that we indeed access the optical near-field of the nanoresonators, by measuring a spectral shift between the typical near-field resonances in PLE compared to the far-field resonances in DF scattering. Our results prove that using MoSe2 as an active probe allows accessing the optical near-field above photonic nanostructures, without the requirement of highly complex near-field microscopy equipment.

physics.optics↗

arXiv2022

Inverse design with flexible design targets via deep learning: Tailoring of electric and magnetic multipole scattering from nano-spheres

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 well as of the training procedure itself remains a major bottleneck. This is particularly inconvenient because new data need to be generated and a new network needs to be trained for any modification of the problem. We propose a technique that allows to train a single neural network on a broad range of design targets without any re-training. The key idea of our method is to enrich existing data with random "regions of interest" (ROI) labels. A model trained on such ROI-decorated data becomes capable to operate on a broad range of physical targets, while it learns to focus its design effort on a user-defined ROI, ignoring the rest of the physical domain. We demonstrate the method by training a tandem-network on the design of dielectric core-shell nano-spheres for electric and magnetic dipole and quadrupole scattering over a broad spectral range. The network learns to tailor very distinct, flexible design targets like scattering due to specific multipoles in narrow spectral windows. Varying the design problem does not require any re-training. Our approach is very general and can be directly used with existing datasets. It can be straightforwardly applied to other network architectures and problems.

physics.optics↗

arXiv2022

Generalizing the exact multipole expansion: Density of multipole modes in complex photonic nanostructures

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 modes that are excited by a specific illumination. In particular the study of various illuminations requires multiple, costly numerical simulations. Here we present a formalism we call "generalized polarizabilities", in which we combine the recently developed exact multipole decomposition [Alaee et al., Opt. Comms. 407, 17-21 (2018)] with the concept of a generalized field propagator. After an initial computation step, our approach allows to instantaneously obtain the exact multipole decomposition for any illumination. Most importantly, since all possible illuminations are included in the generalized polarizabilities, our formalism allows to calculate the total density of multipole modes, regardless of a specific illumination, which is not possible with the conventional multipole expansion. Finally, our approach directly provides the optimum illumination field distributions that maximally couple to specific multipole modes. The formalism will be very useful for various applications in nano-optics like illumination-field engineering, or meta-atom design e.g. for Huygens metasurfaces. We provide a numerical open source implementation compatible with the pyGDM python package.

physics.optics↗

arXiv2022

Magnetic and electric Purcell factor control through geometry optimization of high index dielectric nanostructures

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 different optimized geometries which depend on the nature and orientation of the dipoles. We discuss the physical origin of the obtained configurations thanks to modal analysis but also emphasize the role of nanoscale design of the LDOS. We complete our study using finite element method and demonstrate an enhancement up to 2000 of the magnetic Purcell factor in europium ions. Our work brings together random optimizations to explore geometric parameters without constraint, a first order deterministic approach to understand the optimized designs and a modal analysis which clarifies the physical origin of the exaltation of the magnetic Purcell effect.

physics.optics↗

arXiv2022

Infrared nanoplasmonic properties of hyperdoped embedded Si nanocrystals in the few electrons regime

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 silica matrix. In small SiNCs, with radius ranging from 2.6 to 5.5 nm, the infrared spectroscopy study coupled to numerical simulations allows us to determine the number of electrically active phosphorus atoms with a precision of a few atoms. We demonstrate that LSP resonances can be supported with only about 10 free electrons per nanocrystal, confirming theoretical predictions and probing the limit of the collective nature of plasmons. We reveal a phenomenon, unique to embedded nanocrystals, with the appearance of an avoided crossing behavior linked to the hybridization between the localized surface plasmon in the doped nanocrystals and the silica matrix phonon modes. Finally, a careful analysis of the scattering time dependence versus carrier density in the small size regime allows us to detect the appearance of a new scattering process at high dopant concentration.

physics.optics↗

arXiv2021

Deep learning enabled strategies for modelling of complex aperiodic plasmonic metasurfaces of arbitrary size

Optical interactions have an important impact on the optical response of nanostructures in complex environments. Accounting for interactions in large ensembles of structures requires computationally demanding numerical calculations. In particular if no periodicity can be exploited, full field simulations can become prohibitively expensive. Here we propose a method for the numerical description of aperiodic assemblies of plasmonic nanostructures. Our approach is based on dressed polarizabilities, which are conventionally very expensive to calculate, a problem which we alleviate using a deep convolutional neural network as surrogate model. We demonstrate that the method offers high accuracy with errors in the order of a percent. In cases where the interactions are predominantly short-range, e.g. for out-of-plane illumination of planar metasurfaces, it can be used to describe aperiodic metasurfaces of basically unlimited size, containing many thousands of unordered plasmonic nanostructures. We furthermore show that the model is capable to spectrally resolve coupling effects. The approach is therefore of highest interest for the field of metasurfaces. It provides significant advantages in applications like homogenization of large aperiodic planar metastructures or the design of sophisticated wavefronts at the micrometer scale, where optical interactions play a crucial role.

physics.optics↗

arXiv2021

pyGDM -- new functionalities and major improvements to the python toolkit for nano-optics full-field simulations

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 propagator, which allows to solve cost-efficiently monochromatic problems with a large number of varying illumination conditions such as incident angle scans or focused beam raster-scan simulations. We provide an overview of new features added since the initial publication [Wiecha, Computer Physics Communications 233, pp.167-192 (2018)]. The updated version of pyGDM is implemented in pure python, removing the former dependency on fortran-based binaries. In the course of this re-write, the toolkit's internal architecture has been completely redesigned to offer a much wider range of possibilities to the user such as the choice of the dyadic Green's functions describing the environment. A new class of dyads allows to perform 2D simulations of infinitely long nanostructures. While the Green's dyads in pyGDM are based on a quasistatic description for interfaces, we also provide as new external python package pyGDM2_retard a module with retarded Green's tensors for an environment with two interfaces. We have furthermore added functionalities for simulations using fast-electron excitation, namely electron energy loss spectroscopy and cathodoluminescence. Along with several further new tools and improvements, the update includes also the possibility to calculate the magnetic field and the magnetic LDOS inside nanostructures, field-gradients in- and outside a nanoparticle, optical forces or the chirality of nearfields. All new functionalities remain compatible with the evolutionary optimization module of pyGDM for nano-photonics inverse design.

physics.comp-ph↗