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Zhang, Xiaotian

Publications and source records attributed to Zhang, Xiaotian.

4 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Lattice and electronic structure of ScN observed by angle-resolved photoemission spectroscopy measurements

Scandium nitride (ScN) has recently attracted much attention for its potential applications in thermoelectric energy conversion, as a semiconductor in epitaxial metal/semiconductor superlattices, as a substrate for GaN growth, and alloying it with AlN for 5G technology. This study was undertaken to better understand its stoichiometry and electronic structure. ScN (100) single crystals 2 mm thick were grown on a single crystal tungsten (100) substrate by a physical vapor transport method over a temperature range of 1900–2000 °C and a pressure of 20 Torr. The core level spectra of Sc 2p 3/2,1/2 and N 1s were obtained by x-ray photoelectron spectroscopy (XPS). The XPS core levels were shifted by 1.1 eV toward higher values as the [Sc]:[N] ratio varied from 1.4 at 1900 °C to ~1.0 at 2000 °C due to the higher binding energies in stoichiometric ScN. Angle-resolved photoemission spectroscopy measurements confirmed that ScN has an indirect bandgap of ~1.2 eV.

42 ENGINEERING↗
DOE OSTI2022

Low-temperature processed beta-phase In 2 Se 3 ferroelectric semiconductor thin film transistors

As scaling becomes increasingly difficult, there is growing interest in vertical or three-dimensional stacking of transistors and especially memory. Ferroelectric semiconductor field effect transistors can be key enablers to improve energy efficiency and overall chip and memory performance. In this work, low-temperature processed, back-end-of-the-line compatible transistors were demonstrated by depositing a layered chalcogenide ferroelectric semiconductor, beta-phase In 2 Se 3 , at temperature as low as 400 °C. Top gate n-channel In 2 Se 3 thin film transistors were fabricated with field-effect mobility ∼1 cm 2 V −1 s −1 , and simple polarization switching based memory results are presented.

36 MATERIALS SCIENCE↗
DOE OSTI2020

K-means-driven Gaussian Process data collection for angle-resolved photoemission spectroscopy

Abstract We propose the combination of k-means clustering with Gaussian Process (GP) regression in the analysis and exploration of 4D angle-resolved photoemission spectroscopy (ARPES) data. Using cluster labels as the driving metric on which the GP is trained, this method allows us to reconstruct the experimental phase diagram from as low as 12% of the original dataset size. In addition to the phase diagram, the GP is able to reconstruct spectra in energy-momentum space from this minimal set of data points. These findings suggest that this methodology can be used to improve the efficiency of ARPES data collection strategies for unknown samples. The practical feasibility of implementing this technology at a synchrotron beamline and the overall efficiency implications of this method are discussed with a view on enabling the collection of more samples or rapid identification of regions of interest.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗