arXiv subjects

Zhaslan Baraissov

Publications and source records attributed to Zhaslan Baraissov.

13 recordsLinked to original sources

arXiv2026

Synthesis and Characterization of Atomically-Sharp Superconductor-Dielectric Interface

Modification of superconductor-dielectric interfaces is known to strongly impact coherence times of superconducting quantum devices. This relationship is thought to arise from differences in the concentration of "two-level system" defects in the disordered dielectrics and superconductor-dielectric interfaces; these defects couple to electromagnetic modes in the device and cause dissipation. Zirconium oxide barrier layers on niobium have emerged as a promising pathway to low-loss interfaces in recent years, evidently due to the crystalline nature of these layers in comparison to the amorphous niobium native oxide. We explain the unique ability of zirconium oxide to form a crystalline layer, to maintain a sharp interface with metallic niobium, and to prevent niobium oxide re-growth in terms of the chemical properties of ZrO$_2$ and the Nb-Zr-O ternary system. We demonstrate a new method to grow air-stable zirconium oxide layers on niobium with a higher level of crystallinity and a sharper oxide-metal interface than previously shown, and provide the first comprehensive microscopic analysis of ZrO$_2$ capping layer properties. These developments pave the way toward vital performance advances in superconducting quantum devices.

cond-mat.mtrl-sci↗

arXiv2025

Active-Learning Inspired $\textit{Ab Initio}$ Theory-Experiment Loop Approach for Management of Material Defects: Application to Superconducting Qubits

Surface oxides are associated with two-level systems (TLSs) that degrade the performance of niobium-based superconducting quantum computing devices. To address this, we introduce a predictive framework for selecting metal capping layers that inhibit niobium oxide formation. Using DFT-calculated oxygen interstitial and vacancy energies as thermodynamic descriptors, we train a logistic regression model on a limited set of experimental outcomes to successfully predict the likelihood of oxide formation beneath different capping materials. This approach identifies Zr, Hf, and Ta as effective diffusion barriers. Our analysis further reveals that the oxide formation energy per oxygen atom serves as an excellent standalone descriptor for predicting barrier performance. By combining this new descriptor with lattice mismatch as a secondary criterion to promote structurally coherent interfaces, we identify Zr, Ta, and Sc as especially promising candidates. This closed-loop strategy integrates first-principles theory, machine learning, and limited experimental data to enable rational design of next-generation materials.

cond-mat.mtrl-sci↗

arXiv2025

Cepstral Strain Mapping for Small Pixel-Count Detectors

With the decreasing sizes of integrated-circuit components, the semiconductor industry is in growing need of high-throughput strain mapping techniques that offer high precision and spatial resolution, with desired industry goals of 0.01-0.1% and 1 nm respectively. As the fundamental limitation on the measurement precision is set by the Poisson noise, pixel array detectors with high saturation current, high dynamic range and fast readout are ideally suited for this purpose. However, due to the limited pixel count on these detectors, they do not work well with traditional strain mapping algorithms that were optimized to work on datasets with a large pixel count. Here, we evaluate the cepstral transform that was designed to address this problem, with the precision determined by the convergence, collection angles and dose while remaining insensitive to the pixel count. We test the performance of our method on silicon wedges and Si-SiGe multilayers, and using datasets collected at different conditions, we show how the measured strain precision scales as a function of dose, aperture size and sample thickness. Using precession gives a further improvement in precision by about 1.5-2x, whereas energy filtering does not have a significant impact on the cepstral method for device-relevant sample thickness ranges.

physics.ins-det↗

arXiv2025

Low barrier ZrO$_x$-based Josephson junctions

The Josephson junction is a crucial element in superconducting devices, and niobium is a promising candidate for the superconducting material due to its large energy gap relative to aluminum. AlO$_x$ has long been regarded as the highest quality oxide tunnel barrier and is often used in niobium-based junctions. Here we propose ZrO$_x$ as an alternative tunnel barrier material for Nb electrodes. We theoretically estimate that zirconium oxide has excellent oxygen retention properties and experimentally verify that there is no significant oxygen diffusion leading to NbO$_x$ formation in the adjacent Nb electrode. We develop a top-down, subtractive fabrication process for Nb/Zr-ZrO$_x$/Nb Josephson junctions, which enables scalability and large-scale production of superconducting electronics. Using cross sectional scanning transmission electron microscopy, we experimentally find that depending on the Zr thickness, ZrO$_x$ tunnel barriers can be fully crystalline with chemically abrupt interfaces with niobium. Further analysis using electron energy loss spectroscopy reveals that ZrO$_x$ corresponds to tetragonal ZrO$_2$. Room temperature characterization of fabricated junctions using Simmons' model shows that ZrO$_2$ exhibits a low tunnel barrier height, which is promising in merged-element transmon applications. Low temperature transport measurements reveal sub-gap structure, while the low-voltage sub-gap resistance remains in the megaohm range.

cond-mat.supr-con↗

arXiv2025

Mic-hackathon 2024: Hackathon on Machine Learning for Electron and Scanning Probe Microscopy

Microscopy is a primary source of information on materials structure and functionality at nanometer and atomic scales. The data generated is often well-structured, enriched with metadata and sample histories, though not always consistent in detail or format. The adoption of Data Management Plans (DMPs) by major funding agencies promotes preservation and access. However, deriving insights remains difficult due to the lack of standardized code ecosystems, benchmarks, and integration strategies. As a result, data usage is inefficient and analysis time is extensive. In addition to post-acquisition analysis, new APIs from major microscope manufacturers enable real-time, ML-based analytics for automated decision-making and ML-agent-controlled microscope operation. Yet, a gap remains between the ML and microscopy communities, limiting the impact of these methods on physics, materials discovery, and optimization. Hackathons help bridge this divide by fostering collaboration between ML researchers and microscopy experts. They encourage the development of novel solutions that apply ML to microscopy, while preparing a future workforce for instrumentation, materials science, and applied ML. This hackathon produced benchmark datasets and digital twins of microscopes to support community growth and standardized workflows. All related code is available at GitHub: https://github.com/KalininGroup/Mic-hackathon-2024-codes-publication/tree/1.0.0.1

cond-mat.mtrl-sci↗

arXiv2024

Emittance Minimization for Aberration Correction II: Physics-informed Bayesian Optimization of an Electron Microscope

Aberration-corrected Scanning Transmission Electron Microscopy (STEM) has become an essential tool in understanding materials at the atomic scale. However, tuning the aberration corrector to produce a sub-{\AA}ngstr\"om probe is a complex and time-costly procedure, largely due to the difficulty of precisely measuring the optical state of the system. When measurements are both costly and noisy, Bayesian methods provide rapid and efficient optimization. To this end, we develop a Bayesian approach to fully automate the process by minimizing a new quality metric, beam emittance, which is shown to be equivalent to performing aberration correction. In part I, we derived several important properties of the beam emittance metric and trained a deep neural network to predict beam emittance growth from a single Ronchigram. Here we use this as the black box function for Bayesian Optimization and demonstrate automated tuning of simulated and real electron microscopes. We explore different surrogate functions for the Bayesian optimizer and implement a deep neural network kernel to effectively learn the interactions between different control channels without the need to explicitly measure a full set of aberration coefficients. Both simulation and experimental results show the proposed method outperforms conventional approaches by achieving a better optical state with a higher convergence rate.

physics.ins-det↗

arXiv2024

Emittance Minimization for Aberration Correction I: Aberration correction of an electron microscope without knowing the aberration coefficients

Precise alignment of the electron beam is critical for successful application of scanning transmission electron microscopes (STEM) to understanding materials at atomic level. Despite the success of aberration correctors, aberration correction is still a complex process. Here we approach aberration correction from the perspective of accelerator physics and show it is equivalent to minimizing the emittance growth of the beam, the span of the phase space distribution of the probe. We train a deep learning model to predict emittance growth from experimentally accessible Ronchigrams. Both simulation and experimental results show the model can capture the emittance variation with aberration coefficients accurately. We further demonstrate the model can act as a fast-executing function for the global optimization of the lens parameters. Our approach enables new ways to quickly quantify and automate aberration correction that takes advantage of the rapid measurements possible with high-speed electron cameras. In part II of the paper, we demonstrate how the emittance metric enables rapid online tuning of the aberration corrector using Bayesian optimization.

physics.ins-det↗

arXiv2023

Thermodynamic route of Nb3Sn nucleation: Role of oxygen

Intermetallic Nb3Sn alloys have long been believed to form through Sn diffusion into Nb. However, our observations of significant oxygen content in Nb3Sn prompted an investigation of alternative formation mechanisms. Through experiments involving different oxide interfaces (clean HF-treated, native oxidized, and anodized), we demonstrate a thermodynamic route that fundamentally challenges the conventional Sn diffusion mechanism for Nb3Sn nucleation. Our results highlight the critical involvement of a SnOx intermediate phase. This new nucleation mechanism identifies the principles for growth optimization and new synthesis of high-quality Nb3Sn superconductors.

cond-mat.mtrl-sci↗

arXiv2023

Surface oxides, carbides, and impurities on RF superconducting Nb and Nb3Sn: A comprehensive analysis

Surface structures on radio-frequency (RF) superconductors are crucially important in determining their interaction with the RF field. Here we investigate the surface compositions, structural profiles, and valence distributions of oxides, carbides, and impurities on niobium (Nb) and niobium-tin (Nb3Sn) in situ under different processing conditions. We establish the underlying mechanisms of vacuum baking and nitrogen processing in Nb and demonstrate that carbide formation induced during high-temperature baking, regardless of gas environment, determines subsequent oxide formation upon air exposure or low-temperature baking, leading to modifications of the electron population profile. Our findings support the combined contribution of surface oxides and second-phase formation to the outcome of ultra-high vacuum baking (oxygen processing) and nitrogen processing. Also, we observe that vapor-diffused Nb3Sn contains thick metastable oxides, while electrochemically synthesized Nb3Sn only has a thin oxide layer. Our findings reveal fundamental mechanisms of baking and processing Nb and Nb3Sn surface structures for high-performance superconducting RF and quantum applications

cond-mat.mtrl-sci↗

arXiv2023

ZrNb(CO) RF superconducting thin film with high critical temperature in the theoretical limit

Superconducting radio-frequency (SRF) resonators are critical components for particle accelerator applications, such as free-electron lasers, and for emerging technologies in quantum computing. Developing advanced materials and their deposition processes to produce RF superconductors that yield nanoohms surface resistances is a key metric for the wider adoption of SRF technology. Here we report ZrNb(CO) RF superconducting films with high critical temperatures (Tc) achieved for the first time under ambient pressure. The attainment of a Tc near the theoretical limit for this material without applied pressure is promising for its use in practical applications. A range of Tc, likely arising from Zr doping variation, may allow a tunable superconducting coherence length that lowers the sensitivity to material defects when an ultra-low surface resistance is required. Our ZrNb(CO) films are synthesized using a low-temperature (100 - 200 C) electrochemical recipe combined with thermal annealing. The phase transformation as a function of annealing temperature and time is optimized by the evaporated Zr-Nb diffusion couples. Through phase control, we avoid hexagonal Zr phases that are equilibrium-stable but degrade Tc. X-ray and electron diffraction combined with photoelectron spectroscopy reveal a system containing cubic ZrNb mixed with rocksalt NbC and low-dielectric-loss ZrO2. We demonstrate proof-of-concept RF performance of ZrNb(CO) on an SRF sample test system. BCS resistance trends lower than reference Nb, while quench fields occur at approximately 35 mT. Our results demonstrate the potential of ZrNb(CO) thin films for particle accelerator and other SRF applications.

cond-mat.mtrl-sci↗

arXiv2023

Smooth, homogeneous, high-purity Nb3Sn superconducting RF resonant cavity by seed-free electrochemical synthesis

Workbench-size particle accelerators, enabled by Nb3Sn-based superconducting radio-frequency (SRF) cavities, hold the potential of driving scientific discovery by offering a widely accessible and affordable source of high-energy electrons and X-rays. Thin-film Nb3Sn RF superconductors with high quality factors, high operation temperatures, and high-field potentials are critical for these devices. However, surface roughness, non-stoichiometry, and impurities in Nb3Sn deposited by conventional Sn-vapor diffusion prevent them from reaching their theoretical capabilities. Here we demonstrate a seed-free electrochemical synthesis that pushes the limit of chemical and physical properties in Nb3Sn. Utilization of electrochemical Sn pre-deposits reduces the roughness of converted Nb3Sn by five times compared to typical vapor-diffused Nb3Sn. Quantitative mappings using chemical and atomic probes confirm improved stoichiometry and minimized impurity concentrations in electrochemically synthesized Nb3Sn. We have successfully applied this Nb3Sn to the large-scale 1.3 GHz SRF cavity and demonstrated ultra-low BCS surface resistances at multiple operation temperatures, notably lower than vapor-diffused cavities. Our smooth, homogeneous, high-purity Nb3Sn provides the route toward high efficiency and high fields for SRF applications under helium-free cryogenic operations.

cond-mat.mtrl-sci↗

arXiv2022

Single-shot, coherent, pop-out 3D metrology

Three-dimensional (3D) imaging of thin, extended specimens at nanometer resolution is critical for applications in biology, materials science, advanced synthesis, and manufacturing. One route to 3D imaging is tomography, which requires a tilt series of a local region. Here we describe a coherent imaging alternative that recovers the 3D volume of a thin, homogeneously amorphous specimen with only a single, energy-filtered, bright-field image. We demonstrated this technique with a transmission electron microscope to fill a glaring gap for rapid, accessible, non-destructive 3D nanometrology. This technique is applicable, in general, to any coherent bright field imaging with electrons, photons, or any other wavelike particles.

physics.ins-det↗

arXiv2022

Theory of Nb-Zr Alloy Superconductivity and First Experimental Demonstration for Superconducting Radio-Frequency Cavity Applications

Niobium-zirconium (Nb-Zr) alloy is an old superconductor that is a promising new candidate for superconducting radio-frequency (SRF) cavity applications. Using density-functional and Eliashberg theories, we show that addition of Zr to a Nb surface in small concentrations increases the critical temperature $T_c$ and improves other superconducting properties. Furthermore, we calculate $T_c$ for Nb-Zr alloys across a broad range of Zr concentrations, showing good agreement with the literature for disordered alloys as well as the potential for significantly higher $T_c$ in ordered alloys near 75%Nb/25%Zr composition. We provide experimental verification on Nb-Zr alloy samples and SRF sample test cavities prepared with either physical vapor or our novel electrochemical deposition recipes. These samples have the highest measured $T_c$ of any Nb-Zr superconductor to date and indicate a reduction in BCS resistance compared to the conventional Nb reference sample; they represent the first steps along a new pathway to greatly enhanced SRF performance. Finally, we use Ginzburg-Landau theory to show that the addition of Zr to a Nb surface increases the superheating field $B_{sh}$, a key figure of merit for SRF which determines the maximum accelerating gradient at which cavities can operate.

cond-mat.supr-con↗