arXiv subjects

Zineb Saghi

Publications and source records attributed to Zineb Saghi.

6 recordsLinked to original sources

arXiv2026

Unsupervised Deep Learning for Limited-Angle STEM-EDX Tomography -- Application to 3D Chemical Analysis of Phase-Change Memory Devices

Energy Dispersive X-ray (EDX) tomography in Scanning Transmission Electron Microscopy (STEM) enables 3D compositional and elemental mapping at the nanoscale, but its use is limited by restricted tilt ranges and low-dose conditions required to avoid beam damage. Limited-angle acquisition introduces missing-wedge artefacts such as elongation and anisotropic resolution, while noisy low-dose data further degrade reconstruction quality and quantitative reliability. Here, we introduce an unsupervised deep learning framework based on Deep Image Prior with total variation regularization (DIP-TV) for limited-angle STEM-EDX tomography. We extend it to a multi-channel formulation (DIPm-TV) that jointly reconstructs multiple elemental maps by exploiting spatial correlations. Using a synthetic 3-channel phantom, we show that the method compensates for severe missing-wedge artefacts corresponding to approximately $100^\circ$ of missing angular range under moderate noise, outperforming simultaneous iterative reconstruction technique and compressed sensing approaches. We apply the method to 3D chemical analysis of Ge-Sb-Te (GST) memory devices in virgin (as-fabricated) and SET (crystalline) operational states. Samples were prepared as cross-sectional focused ion beam lamellae and acquired under a limited-angle tilt range from $-40^\circ$ to $+40^\circ$ with $5^\circ$ steps and a dose of $2.0\times10^5$ $e^-/Ang^2$. The multi-channel approach enables voxel-by-voxel elemental reconstruction using only EDX signals without external structural priors such as high-angle annular dark-field imaging. The reconstructed volumes show near-isotropic spatial resolution and reveal compositional heterogeneities associated with device operation. This approach enables 3D chemical characterization in experimentally accessible sample geometries where conventional methods fail due to severe angular limitations.

eess.IV↗

arXiv2026

Low-Dose 3D Bonding Mapping Through "Soft" Core-Loss EELS Tomography and Unsupervised Deep Learning

Resolving the 3D chemical configuration of beam-sensitive nanomaterials at high spatial resolution remains a persistent frontier in scanning transmission electron microscopy (STEM). The main limitation lies in the trade-off between high electron dose required for analytical signals and the large number of projections needed for tomographic reconstruction. Here, we achieve dose-efficient 3D bonding mapping of FeO/Fe$_3$O$_4$ core-shell nanocubes with high resolution via electron energy loss spectroscopy (EELS). Our approach relies on two developments. First, a standardless "soft" core-loss EELS methodology exploiting Fe-M$_{2,3}$ edges provides ${\sim}50\times$ higher dose efficiency than conventional Fe-L$_{2,3}$ edges, using the latter only as a source of FeO and Fe$_3$O$_4$ standards. Second, we introduce multi-channel deep image prior with total variation regularization (DIPm-TV), an unsupervised method for spectroscopic tomography that jointly reconstructs multiple channels by exploiting spatial correlations under sparse-view and low-dose conditions. Using simulated datasets, high-quality reconstructions are obtained from as few as nine projections over $-70^\circ$ to $+70^\circ$, without HAADF-STEM signal or symmetry constraints. Applied to FeO/Fe$_3$O$_4$ nanocubes, Fe-M$_{2,3}$ EELS maps show improved SNR and spatial resolution, revealing a thin outer FeO shell surrounding the magnetite shell. DIPm-TV yields ${\sim}1$ nm isotropic resolution oxidation-state volumes preserving cubic morphology, recovering the outer FeO shell, and revealing a small internal void, features not accessible with conventional reconstruction methods. This work establishes a pathway for low-dose 2D and 3D analytical mapping of beam-sensitive materials using shallow core-loss edges, enabling orders-of-magnitude dose reduction while maintaining spectral fidelity and reliable 3D information.

eess.IV↗

arXiv2026

Unsupervised Deep Image Prior for Sparse-View and Limited-Angle Electron Tomography

Electron tomography (ET) plays an important role in the three-dimensional (3D) characterization of nanomaterials. However, under limited-angle and sparse-view conditions, conventional algorithms produce degraded reconstructions, which compromise the quality and interpretability of resulting 3D data. In this paper, we present deep image prior (DIP), an unsupervised deep learning (DL) approach, for highly degraded tomography acquisitions and demonstrate, using simulated data, that its performance is comparable to that of supervised approaches requiring training datasets, even for tilt ranges as limited as 60° and tilt increments of 10°. We then apply it to experimental data and show that it enables reliable 3D quantification under both sparse-view and limited-angle conditions, highlighting its potential for a wide range of materials and acquisition modalities.

eess.IV↗

arXiv2023

Conformal TiO$_2$ aerogel-like films by plasma deposition: from omniphobic antireflective coatings to perovskite solar cells photoelectrodes

The ability to control porosity in oxide thin films is one of the key factors that determine their properties. Despite the abundance of dry processes for the synthesis of oxide porous layers, the high porosity range is typically achieved by spin-coating-based wet chemical methods. Besides, special techniques such as supercritical drying are required to replace the pore liquid with air while maintaining the porous network. In this study, we propose a new method for the fabrication of ultra-porous titanium dioxide thin films at room or mild temperatures (T lower or equal to 120 degrees Celsius) by the sequential process involving plasma deposition and etching. These films are conformal to the substrate topography even for high-aspect-ratio substrates and show percolated porosity values above 85 percent that are comparable to advanced aerogels. The films deposited at room temperature are amorphous. However, they become partly crystalline at slightly higher temperatures presenting a distribution of anatase clusters embedded in the sponge-like structure. Surprisingly, the porous structure remains after annealing the films at 450 degrees Celsius in air, which increases the fraction of the embedded anatase nanocrystals. The films are antireflective, omniphobic, and photoactive becoming super-hydrophilic subjected to UV light irradiation The supported percolated nanoporous structure can be used as an electron-conducting electrode in perovskite solar cells. The properties of the cells depend on the aerogel film thickness reaching efficiencies close to those of commercial mesoporous anatase electrodes. This generic solvent-free synthesis is scalable and is applicable to ultra-high porous conformal oxides of different compositions with potential applications in photonics, optoelectronics, energy storage, and controlled wetting.

physics.app-ph↗

arXiv2023

Effect of extended defects on AlGaN QDs for electron-pumped UV-emitters

We study the origin of bimodal emission in AlGaN/AlN QD superlattices displaying high internal quantum efficiency (around 50%) in the 230-300 nm spectral range. The secondary emission at longer wavelengths is linked to the presence of cone-like defects starting at the first AlN buffer/superlattice interface and propagating vertically. These defects are associated with a dislocation that produces strong shear strain, which favors the formation of 30° faceted pits. The cone-like structures present Ga enrichment at the boundary facets and larger QDs within the defect. The bimodality is attributed to the differing dot size/composition within the defects and at the defect boundaries, which is confirmed by the correlation of microscopy results and Schrödinger-Poisson calculations.

physics.app-ph↗

arXiv2013

Learning from Nature to Improve the Heat Generation of Iron-Oxide Nanoparticles for Magnetic Hyperthermia Applications

The performance of magnetic nanoparticles is intimately entwined with their structure, mean size and magnetic anisotropy. Besides, ensembles offer a unique way of engineering the magnetic response by modifying the strength of the dipolar interactions between particles. Here we report on an experimental and theoretical analysis of magnetic hyperthermia, a rapidly developing technique in medical research and oncology. Experimentally, we demonstrate that single-domain cubic iron oxide particles resembling bacterial magnetosomes have superior magnetic heating efficiency compared to spherical particles of similar sizes. Monte Carlo simulations at the atomic level corroborate the larger anisotropy of the cubic particles in comparison with the spherical ones, thus evidencing the beneficial role of surface anisotropy in the improved heating power. Moreover we establish a quantitative link between the particle assembling, the interactions and the heating properties. This knowledge opens new perspectives for improved hyperthermia, an alternative to conventional cancer therapies.

cond-mat.mes-hall↗