Engineering topics

Barbour, Andi M.

Publications and source records attributed to Barbour, Andi M..

4 records

DOE OSTI2023

Chemical Profiles of the Oxides on Tantalum in State of the Art Superconducting Circuits

Abstract Over the past decades, superconducting qubits have emerged as one of the leading hardware platforms for realizing a quantum processor. Consequently, researchers have made significant effort to understand the loss channels that limit the coherence times of superconducting qubits. A major source of loss has been attributed to two level systems that are present at the material interfaces. It is recently shown that replacing the metal in the capacitor of a transmon with tantalum yields record relaxation and coherence times for superconducting qubits, motivating a detailed study of the tantalum surface. In this work, the chemical profile of the surface of tantalum films grown on c‐plane sapphire using variable energy X‐ray photoelectron spectroscopy (VEXPS) is studied. The different oxidation states of tantalum that are present in the native oxide resulting from exposure to air are identified, and their distribution through the depth of the film is measured. Furthermore, it is shown how the volume and depth distribution of these tantalum oxidation states can be altered by various chemical treatments. Correlating these measurements with detailed measurements of quantum devices may elucidate the underlying microscopic sources of loss.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Machine learning enabling high-throughput and remote operations at large-scale user facilities

Imaging, scattering, and spectroscopy are fundamental in understanding and discovering new functional materials. Contemporary innovations in automation and experimental techniques have led to these measurements being performed much faster and with higher resolution, thus producing vast amounts of data for analysis. These innovations are particularly pronounced at user facilities and synchrotron light sources. Machine learning (ML) methods are regularly developed to process and interpret large datasets in real-time with measurements. However, there remain conceptual barriers to entry for the facility general user community, whom often lack expertise in ML, and technical barriers for deploying ML models. Herein, we demonstrate a variety of archetypal ML models for on-the-fly analysis at multiple beamlines at the National Synchrotron Light Source II (NSLS-II). We describe these examples instructively, with a focus on integrating the models into existing experimental workflows, such that the reader can easily include their own ML techniques into experiments at NSLS-II or facilities with a common infrastructure. The framework presented here shows how with little effort, diverse ML models operate in conjunction with feedback loops via integration into the existing Bluesky Suite for experimental orchestration and data management.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Real Space Imaging of Spin Stripe Domain Fluctuations in a Complex Oxide

Understanding the formation and dynamics of charge and spin-ordered states in low-dimensional transition metal oxide materials is crucial to understanding unconventional high-temperature superconductivity. La 2-x Sr x NiO 4+δ (LSNO) has attracted much attention due to its interesting spin dynamics. Recent x-ray photon correlation spectroscopy studies have revealed slow dynamics of the spin order (SO) stripes in LSNO. Here, we applied resonant soft x-ray ptychography to map the spatial distribution of the SO stripe domain inhomogeneity in real space. The reconstructed images show the SO domains are spatially anisotropic, in agreement with previous diffraction studies. For the SO stripe domains, it is found that the correlation lengths along different directions are strongly coupled in space. Surprisingly, fluctuations were observed in the real space amplitude signal, rather than the phase or position. Overall, we attribute the observed slow dynamics of the stripe domains in LSNO to thermal fluctuations of the SO domain boundaries.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2021

Noise reduction in X-ray photon correlation spectroscopy with convolutional neural networks encoder–decoder models

Abstract Like other experimental techniques, X-ray photon correlation spectroscopy is subject to various kinds of noise. Random and correlated fluctuations and heterogeneities can be present in a two-time correlation function and obscure the information about the intrinsic dynamics of a sample. Simultaneously addressing the disparate origins of noise in the experimental data is challenging. We propose a computational approach for improving the signal-to-noise ratio in two-time correlation functions that is based on convolutional neural network encoder–decoder (CNN-ED) models. Such models extract features from an image via convolutional layers, project them to a low dimensional space and then reconstruct a clean image from this reduced representation via transposed convolutional layers. Not only are ED models a general tool for random noise removal, but their application to low signal-to-noise data can enhance the data’s quantitative usage since they are able to learn the functional form of the signal. We demonstrate that the CNN-ED models trained on real-world experimental data help to effectively extract equilibrium dynamics’ parameters from two-time correlation functions, containing statistical noise and dynamic heterogeneities. Strategies for optimizing the models’ performance and their applicability limits are discussed.

36 MATERIALS SCIENCE↗