arXiv subjects

N. Brun

Publications and source records attributed to N. Brun.

3 recordsLinked to original sources

arXiv2024

Crystallinity in Niobium oxides: A pathway to mitigate Two-Level System Defects in Niobium 3D Resonator for quantum applications

Materials imperfections in Nniobium based superconducting quantum circuits, in particular, two-level-system (TLS) defects, are a major source of decoherence, ultimately limiting the performance of quantum computation and sensing. Thus, identifying and understanding the microscopic origin of possible TLS defects in these devices and developing strategies to eliminate them is key to superconducting qubit performance improvement. In this paper, we demonstrate the reduction of two-level system losses in three-dimensional superconducting radio frequency (SRF) niobium resonators by a 10-hour high vacuum (HV) heat treatment at 650{\deg}C, even after exposure to air and high pressure rinsing (HPR). By probing the effect of this annealing on niobium samples using X-ray photoelectron spectroscopy (XPS) and high-resolution scanning transmission electron microscopy (STEM), we witness an alteration of the native oxide composition re-grown after air exposure and HPR and the creation of nano-scale crystalline oxide regions, which correlates with the measured tenfold quality factor enhancement at low fields of the 1.3 GHz niobium resonator.

physics.app-ph↗

arXiv2023

Deep Learning for EELS hyperspectral images unmixing -- using autoencoders

Spatially resolved Electron Energy-Loss Spectroscopy (EELS) conducted in a Scanning Transmission Electron Microscope (STEM) enables the acquisition of hyperspectral images (HSIs). Spectral unmixing (SU) is the process of decomposing each spectrum of an HSI into a combination of representative spectra (endmembers) corresponding to compounds present in the sample along with their local proportions (abundances). SU is a complex task, and various methods have been developed in different communities using HSIs. However, none of these methods fully satisfy the STEM-EELS requirements. Recent advancements in remote sensing, which focus on Deep Learning techniques, have the potential to meet these requirements, particularly Autoencoders (AEs). In this study, the performance of Deep Learning methods using AE for SU is evaluated, and their results are compared with traditional methods. Synthetic HSIs have been created to quantitatively assess the outcomes of the unmixing process using specific metrics. The methods are subsequently applied to a series of experimental data. The findings demonstrate the promising potential of AE as a tool for STEM-EELS SU, marking a starting point for exploring more sophisticated Neural Networks.

physics.data-an↗

arXiv2018

Role of interfaces in the biased composition of TbFe(Co) thin films

Ferrimagnetic TbFe or TbFeCo amorphous alloy thin films have been grown by co-evaporation in ultra-high vacuum. They exhibit an out-of-plane magnetic anisotropy up to their Curie temperature with a nucleation and propagation reversal mechanism suitable for current induced domain wall motion. Rutherford back scattering experiments confirmed a fine control of the Tb depth-integrated composition within the evaporation process. However, a large set of experimental techniques were used to evidence an interface related contribution in such thin films as compared to much thicker samples. In particular, scanning transmission electron microscopy experiments evidence a depth dependent composition and perturbed top and bottom interfaces with preferential oxidation and diffusion of terbium. Despite of that, amorphous and homogeneous alloy film remains in a bulk-like part. The composition of that bulk-like part of the magnetic layer, labeled as effective composition, is biased when compared with the depth-integrated composition. The magnetic properties of the film are mostly dictated by this effective composition, which we show changes with different top and bottom interfaces.

cond-mat.mtrl-sci↗