arXiv subjects

Gabriele Navarro

Publications and source records attributed to Gabriele Navarro.

3 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↗

arXiv2021

Electronic and Thermal Properties of $\text{GeTe/Sb}_{2}\text{Te}_{3}$ Superlattices by ab-initio Approach: Impact of Van der Waals Gaps on Vertical Lattice Thermal Conductivity

In the last decade, several works have focused on exploring the material and electrical properties of $\text{GeTe/Sb}_{2}\text{Te}_{3}$ superlattices (SLs) in particular because of some first device implementations demonstrating interesting performances such as fast switching speed, low energy consumption, and non-volatility. However, the switching mechanism in such SL-based devices remains under debate. In this work, we investigate the prototype $\text{GeTe/Sb}_{2}\text{Te}_{3}$ SLs, to analyze fundamentally their electronic and thermal properties by ab initio methods. We find that the resistive contrast is small among the different phases of $\text{GeTe/Sb}_{2}\text{Te}_{3}$ because of a small electronic gap (about 0.1 eV) and a consequent semi-metallic-like behavior. At the same time the out-of-plane lattice thermal conductivity is rather small, while varying up to four times among the different phases, from 0.11 to 0.45 W/m$^{-1}$K$^{-1}$, intimately related to the number of Van der Waals (VdW) gaps in a unit block. Such findings confirm the importance of the thermal improvement achievable in $\text{GeTe/Sb}_{2}\text{Te}_{3}$ super-lattices devices, highlighting the impact of the material stacking and the role of VdW gaps on the thermal engineering of the Phase-Change Memory cell.

cond-mat.mtrl-sci↗

arXiv2021

PCM-trace: Scalable Synaptic Eligibility Traces with Resistivity Drift of Phase-Change Materials

Dedicated hardware implementations of spiking neural networks that combine the advantages of mixed-signal neuromorphic circuits with those of emerging memory technologies have the potential of enabling ultra-low power pervasive sensory processing. To endow these systems with additional flexibility and the ability to learn to solve specific tasks, it is important to develop appropriate on-chip learning mechanisms.Recently, a new class of three-factor spike-based learning rules have been proposed that can solve the temporal credit assignment problem and approximate the error back-propagation algorithm on complex tasks. However, the efficient implementation of these rules on hybrid CMOS/memristive architectures is still an open challenge. Here we present a new neuromorphic building block,called PCM-trace, which exploits the drift behavior of phase-change materials to implement long lasting eligibility traces, a critical ingredient of three-factor learning rules. We demonstrate how the proposed approach improves the area efficiency by >10X compared to existing solutions and demonstrates a techno-logically plausible learning algorithm supported by experimental data from device measurements

cs.ET↗