arXiv subjects

Vincent Favre-Nicolin

Publications and source records attributed to Vincent Favre-Nicolin.

13 recordsLinked to original sources

arXiv2026

Automated Burgers Vector Identification for Individual Dislocations in Bulk Crystals

Weak-beam imaging in dark-field X-ray microscopy (DFXM) can resolve individual dislocations in bulk crystals, but assigning Burgers vectors from the resulting contrast typically requires manual comparison with forward simulations. Here, we train a physics-informed convolutional neural network (CNN) on geometrical optics simulations of isolated dislocations in face-centred cubic (FCC) aluminium, incorporating crystallographic constraints into the learning pro- cess. The model identifies Burgers vectors from weak-beam integrated rocking-curve images. On synthetic test data, the model achieves an accuracy of approximately 93%. In an experimental cross-slip case, the constrained model as- signs 72.7% of the layer-wise predictions to the reference Burgers vector. These results show that simulation-trained, physics-informed CNNs represent a step toward automated dislocation identification in DFXM.

cond-mat.mtrl-sci↗

arXiv2025

Phase Retrieval of Highly Strained Bragg Coherent Diffraction Patterns using Supervised Convolutional Neural Network

In Bragg Coherent Diffraction Imaging (BCDI), Phase Retrieval of highly strained crystals is often challenging with standard iterative algorithms. This computational obstacle limits the potential of the technique as it precludes the reconstruction of physically interesting highly-strained particles. Here, we propose a novel approach to this problem using a supervised Convolutional Neural Network (CNN) trained on 3D simulated diffraction data to predict the corresponding reciprocal space phase. This method allows to fully exploit the potential of the CNN by mapping functions within the same space and leveraging structural similarities between input and output. The final object is obtained by the inverse Fourier transform of the retrieved complex diffracted amplitude and is then further refined with iterative algorithms. We demonstrate that our model outperforms standard algorithms on highly strained simulated data not included in the training set, as well as on experimental data.

cond-mat.mtrl-sci↗

arXiv2024

Magnetic X-ray imaging using a single polarization and multimodal-ptychography

Polarized X-rays allow for imaging birefringent or dichroic properties of materials with nanometric resolution. To disentangle these properties from the electronic density, either a polarization analyzer or several measurements with different polarizations (typically two, or more) are needed. Here we demonstrate that ptychography can disentangle these from a single-polarization measurement by using a multimodal analysis. This new method provides an alternative to obtain polarization-resolved images of a sample when manipulating the incident polarization is not possible nor sufficient.

cond-mat.mes-hall↗

arXiv2024

Patching-based Deep Learning model for the Inpainting of Bragg Coherent Diffraction patterns affected by detectors' gaps

We propose a deep learning algorithm for the inpainting of Bragg Coherent Diffraction Imaging (BCDI) patterns affected by detector gaps. These regions of missing intensity can compromise the accuracy of reconstruction algorithms, inducing artifacts in the final result. It is thus desirable to restore the intensity in these regions in order to ensure more reliable reconstructions. The key aspect of our method lies in the choice of training the neural network with cropped sections of both experimental diffraction data and simulated data and subsequently patching the predictions generated by the model along the gap, thus completing the full diffraction peak. This provides us with more experimental training data and allows for a faster model training due to the limited size, while the neural network can be applied to arbitrarily larger BCDI datasets. Moreover, our method not only broadens the scope of application but also ensures the preservation of data integrity and reliability in the face of challenging experimental conditions.

cond-mat.mtrl-sci↗

arXiv2023

Soft X-ray spectro-ptychography on boron nitride nanotubes, carbon nanotubes and permalloy nanorods

Spectro-ptychography offers improved spatial resolution and additional phase spectral information relative to that provided by scanning transmission X-ray microscopes (STXM). However, carrying out ptychography at the lower range of soft X-ray energies (e.g., below 200 eV to 600 eV) on samples with weakly scattering signals can be challenging. We present soft X-ray ptychography results at energies as low as 180 eV and illustrate the capabilities with results from permalloy nanorods (Fe 2p), carbon nanotubes (C 1s), and boron nitride bamboo nanostructures (B 1s, N1s). We describe optimization of low energy X-ray spectro-ptychography and discuss important challenges associated with measurement approaches, reconstruction algorithms, and their effects on the reconstructed images. A method for evaluating the increase in radiation dose when using overlapping sampling is presented.

cond-mat.mtrl-sci↗

arXiv2022

darfix: Data analysis for dark-field X-ray microscopy

A Python package for the analysis of dark-field X-ray microscopy (DFXM) and rocking curve imaging (RCI) data is presented. \textit{darfix} provides a set of data processing and visualization tools that can be either imported as library components or accessed through a graphical user interface (GUI) as an Orange add-on. In the latter case, the different analysis modules can be easily chained to define computational workflows. Operations on larger-than-memory image sets are supported through the implementation of online versions of the data processing algorithms, effectively trading performance for feasibility when the computing resources are limited. The software can automatically extract the relevant instrument angle settings from the input files metadata. The currently available input file format is EDF and in future releases HDF5 will be incorporated.

cond-mat.mtrl-sci↗

arXiv2020

Boosting spatial resolution by incorporating periodic boundary conditions into single-distance hard-x-ray phase retrieval

A simple coherent-imaging method due to Paganin et al. is widely employed for phase-amplitude reconstruction of samples using a single paraxial x-ray propagation-based phase-contrast image. The method assumes that the sample-to-detector distance is sufficiently small for the associated Fresnel number to be large compared to unity. The algorithm is particularly effective when employed in a tomographic setting, using a single propagation-based phase-contrast image for each projection. Here we develop a simple extension of the method, which improves the reconstructed contrast of very fine sample features. This provides first-principles motivation for boosting fine spatial detail associated with high Fourier frequencies, relative to the original method, and was inspired by several recent works employing empirically-obtained Fourier filters to a similar end.

eess.IV↗

arXiv2020

PyNX: high performance computing toolkit for coherent X-ray imaging based on operators

The open-source PyNX toolkit [Favre-Nicolin et al (2011) arXiv:1010.2641, Mandula et al (2016)] has been extended to provide tools for coherent X-ray imaging data analysis and simulation. All calculations can be executed on graphical processing units (GPU) to achieve high performance computing speeds. This can be used for Coherent Diffraction Imaging (CDI), Ptychography and wavefront propagation, in the far or near field regime. Moreover, all imaging operations (propagation, projections, algorithm cycles..) can be used in Python as simple mathematical operators, an approach which can be used to easily combine basic algorithms in a tailored chain. Calculations can also be distributed to multiple GPUs, e.g. for large Ptychography datasets. Command-line scripts are also available for on-line CDI and Ptychography analysis, either from raw beamline datasets or using the Coherent X-ray Imaging data format [Maia (2012)].

cond-mat.mtrl-sci↗

arXiv2019

Free log-likelihood as an unbiased metric for coherent diffraction imaging

Coherent Diffraction Imaging (CDI), a technique where an object is reconstructed from a single (2D or 3D) diffraction pattern, recovers the lost diffraction phases without a priori knowledge of the extent (support) of the object, which prevents an unambiguous metric evaluation of solutions. We propose to use a 'free' log-likelihood indicator, where a small percentage of points are masked from the reconstruction algorithms, as an unbiased metric to evaluate the validity of proposed solutions, independent of the sample studied. We also show how a set of solutions can be analysed through an eigen-decomposition to yield a better estimate of the real object. Example analysis on experimental data is presented both for a test pattern dataset, and the diffraction pattern from a live cyanobacteria cell. The method allows the validation of reconstructions on a wide range of materials (hard condensed, biological,..), and should be particularly relevant for 4th generation synchrotrons and X-ray free electron lasers, where large, high-throughput datasets require a method for unsupervised data evaluation.

cond-mat.mtrl-sci↗

arXiv2016

Large and uniform optical emission shifts in quantum dots externally strained along their growth axis

We introduce a method which enables to directly compare the impact of elastic strain on the optical properties of distinct quantum dots (QDs). Specifically, the QDs are integrated in a cross-section of a semiconductor core wire which is surrounded by an amorphous straining shell. Detailed numerical simulations show that, thanks to the mechanical isotropy of the shell, the strain field in a core section is homogeneous. Furthermore, we use the core material as an in situ strain gauge, yielding reliable values for the emitter energy tuning slope. This calibration technique is applied to self-assembled InAs QDs submitted to incremental tensile strain along their growth axis. In contrast to recent studies conducted on similar QDs stressed perpendicularly to their growth axis, optical spectroscopy reveals 5-10 times larger tuning slopes, with a moderate dispersion. These results highlight the importance of the stress direction to optimise QD response to applied strain, with implications both in static and dynamic regimes. As such, they are in particular relevant for the development of wavelength-tunable single photon sources or hybrid QD opto-mechanical systems.

cond-mat.mes-hall↗

arXiv2013

Time-dependent relaxation of strained silicon-on-insulator lines using a partially coherent x-ray nanobeam

We report on the quantitative determination of the strain map in a strained Silicon-On-Insulator (sSOI) line with a 200x70 nm^2 cross-section. In order to study a single line as a function of time, we used an X-ray nanobeam with relaxed coherence properties as a compromise between beam size, coherence and intensity. We demonstrate how it is possible to reconstruct the line deformation at the nanoscale, and follow its evolution as the line relaxes under the influence of the X-ray nanobeam.

cond-mat.mtrl-sci↗

arXiv2012

Interface-driven phase separation in multifunctional materials: the case of GeMn ferromagnetic semiconductor

We use extensive first principle simulations to show the major role played by interfaces in the mechanism of phase separation observed in semiconductor multifunctional materials. We make an analogy with the precipitation sequence observed in over-saturated AlCu alloys, and replace the Guinier-Preston zones in this new context. A new class of materials, the $α$ phases, is proposed to understand the formation of the coherent precipitates observed in the GeMn system. The interplay between formation and interface energies is analyzed for these phases and for the structures usually considered in the literature. The existence of the alpha phases is assessed with both theoretical and experimental arguments.

cond-mat.mtrl-sci↗

arXiv2011

Fast computing of scattering maps of nanostructures using graphical processing units

Scattering maps from strained or disordered nano-structures around a Bragg reflection can either be computed quickly using approximations and a (Fast) Fourier transform, or using individual atomic positions. In this article we show that it is possible to compute up to 4.10^10 $reflections.atoms/s using a single graphic card, and we evaluate how this speed depends on number of atoms and points in reciprocal space. An open-source software library (PyNX) allowing easy scattering computations (including grazing incidence conditions) in the Python language is described, with examples of scattering from non-ideal nanostructures.

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