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Secondary‐ion mass spectrometry (SIMS) is used to determine impurity concentrations of carbon and oxygen in two scandium‐containing nitride semiconductor multilayer heterostructures: ScxGa1−xN/GaN and ScxAl1−xN/AlN grown by molecular beam epitaxy (MBE). In the ScxGa1−xN/GaN heterostructure grown in metal‐rich conditions on GaN–SiC template substrates with Sc contents up to 28 at%, the oxygen concentration is found to be below 1 × 1019 cm−3, with an increase directly correlated with the scandium content. In the ScxAl1−xN–AlN heterostructure grown in nitrogen‐rich conditions on AlN–Al2O3template substrates with Sc contents up to 26 at%, the oxygen concentration is found to be between 1019and 1021 cm−3, again directly correlated with the Sc content. The increase in oxygen and carbon takes place during the deposition of scandium‐alloyed layers.
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This content will become publicly available on December 27, 2025
Machine learning–guided optimization of coercive field in Al 1− x Sc x N thin films for nonvolatile memory
Abstract This study employs a data‐driven machine learning approach to investigate specific ferroelectric properties of Al1−xScxN thin films, targeting their application in next‐generation nonvolatile memory (NVM) devices. This approach analyzes a vast design space, encompassing over a million data points, to predict a wide range of coercive field values that are crucial for optimizing Al1−xScxN‐based NVM devices. We evaluated seven machine learning models to predict the coercive field across a range of conditions, identifying the random forest algorithm as the most accurate, with a testR2value of 0.88. The model utilized five key features: film thickness, measurement frequency, operating temperature, scandium concentration, and growth temperature to predict the design space. Our analysis spans 13 distinct scandium concentrations and 13 growth temperatures, encompassing thicknesses from 9–1000 nm, frequencies from 1 to 100 kHz, and operating temperatures from 273 to 700 K. The predictions revealed dominant coercive field values between 3.0 and 4.5 MV/cm, offering valuable insights for the precise engineering of Al1−xScxN‐based NVM devices. This work underscores the potential of machine learning in guiding the development of advanced ferroelectric materials with tailored properties for enhanced device performance.
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- PAR ID:
- 10571435
- Publisher / Repository:
- Wiley-Blackwell
- Date Published:
- Journal Name:
- Journal of the American Ceramic Society
- Volume:
- 108
- Issue:
- 4
- ISSN:
- 0002-7820
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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