Machine learning enhances X-ray imaging of nanotextures

Using a combination of high-powered X-rays, phase-retrieval algorithms and machine learning, Cornell researchers revealed the intricate nanotextures in thin-film materials, offering scientists a new, streamlined approach to analyzing potential candidates for quantum computing and microelectronics, among other applications.

Scientists are especially interested in nanotextures that are distributed non-uniformly throughout a thin film because they can give the material novel properties. The most effective way to study the nanotextures is to visualize them directly, a challenge that typically requires complex electron microscopy and does not preserve the sample.

The new imaging technique detailed July 6 in the Proceedings of the National Academy of Sciences overcomes these challenges by using phase retrieval and machine learning to invert conventionally-collected X-ray diffraction data – such as that produced at the Cornell High Energy Synchrotron Source, where data for the study was collected – into real-space visualization of the material at the nanoscale.

The use of X-ray diffraction makes the technique more accessible to scientists and allows for imaging a larger portion of the sample, said Andrej Singer, assistant professor of materials science and engineering and David Croll Sesquicentennial Faculty Fellow in Cornell Engineering, who led the research with doctoral student Ziming Shao.

“Imaging a large area is important because it represents the true state of the material,” Singer said. “The nanotexture measured by a local probe could depend on the choice of the probed spot.”

Read more on the CHESS website

Connecting the dots between material properties and qubit performance

Engineers and materials scientists studying superconducting quantum information bits (qubits)—a leading quantum computing material platform based on the frictionless flow of paired electrons—have collected clues hinting at the microscopic sources of qubit information loss. This loss is one of the major obstacles in realizing quantum computers capable of stringing together millions of qubits to run demanding computations. Such large-scale, fault-tolerant systems could simulate complicated molecules for drug development, accelerate the discovery of new materials for clean energy, and perform other tasks that would be impossible or take an impractical amount of time (millions of years) for today’s most powerful supercomputers.

An understanding of the nature of atomic-scale defects that contribute to qubit information loss is still largely lacking. The team helped bridge this gap between material properties and qubit performance by using state-of-the-art characterization capabilities at the Center for Functional Nanomaterials (CFN) and National Synchrotron Light Source II (NSLS-II), both U.S. Department of Energy (DOE) Office of Science User Facilities at Brookhaven National Laboratory. Their results pinpointed structural and surface chemistry defects in superconducting niobium qubits that may be causing loss. 

Read more on the BNL website

Image: Scientists performed transmission electron microscopy and x-ray photoelectron spectroscopy (XPS) at Brookhaven Lab’s Center for Functional Nanomaterials and National Synchrotron Light Source II to characterize the properties of niobium thin films made into superconducting qubit devices at Princeton University. A transmission electron microscope image of one of these films is shown in the background; overlaid on this image are XPS spectra (colored lines representing the relative concentrations of niobium metal and various niobium oxides as a function of film depth) and an illustration of a qubit device. Through these and other microscopy and spectroscopy studies, the team identified atomic-scale structural and surface chemistry defects that may be causing loss of quantum information—a hurdle to enabling practical quantum computers.