Cornell University researchers combine high-power X-rays, phase retrieval algorithms, and machine learning to reveal the complex nanotextures of thin-film materials, and to analyze potential candidates such as quantum computing and microelectronics. It provided scientists with a new streamlined approach. application.
Scientists are particularly interested in nanotextures, which are unevenly distributed throughout thin films, as they may impart new properties to the material. The most effective way to study nanotextures is to visualize them directly, but this usually requires complex electron microscopy and does not preserve samples.
A new imaging technique, detailed July 6 in Proceedings of the National Academy of Sciences, uses phase retrieval and machine learning to extract conventionally collected X-ray diffraction data generated at the Cornell high-energy synchrotron source. We overcome these challenges by inverting the . Data for the study were collected on real-space visualization of materials at the nanoscale.
The use of X-ray diffraction made the technique more accessible to scientists, allowing them to image large portions of the sample, says David Kroll, Assistant Professor of Materials Science Engineering and Centennial Faculty Fellow of Cornell Engineering, who led the study. said Andrei Singer. With doctoral student Ziming Shao.
“Imaging a large area is important because it represents the true state of the material,” says Singer. “Nanotexture measured by local probes can depend on the selection of the probed spots.”
Another advantage of this new method is that it allows dynamic studies of thin films, such as introducing light to see how the structure evolves, as the sample does not need to be decomposed.
“The method can be easily applied to dynamics and operando studies in the field,” says Shao. “For example, we plan to use this method to study how structures change within picoseconds after excitation with short laser pulses. This will enable new concepts for future terahertz technologies. There is a possibility.”
The technique was tested on two thin films, the first containing known nanotextures used to validate the imaging results. Testing a second thin film, a Mott insulator with physics related to superconductivity, the researchers discovered a new type of morphology never before observed in materials: We found strain-induced nanopatterns that spontaneously formed during cooling.
“The images are extracted without prior knowledge and may set new benchmarks and bring new physical hypotheses in phase field modeling, molecular dynamics simulations and quantum mechanics calculations,” said Shao.
Co-authors include the late Lena Kurktis, Associate Professor of Applied and Engineering Physics. Kyle Shen, James A. Weeks Professor of Physical Sciences, University of Arts and Sciences. Darrell Schlom, Herbert Fisk-Johnson Professor of Industrial Chemistry, Professor of Materials Science and Engineering, University of Tisch. Hari Naal is an Assistant Professor of Materials Science and Engineering.
This research was supported by the U.S. Department of Energy and the National Science Foundation.
Syl Kacapyr is an Associate Director of Marketing and Communications at Cornell Engineering.
