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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Examining the ferroelectric characteristics of aluminum nitride‐based thin films
Abstract The discovery of ferroelectricity in AlN‐based thin films, including Al1‐xScxN and Al1‐xBxN, over the past few years has spurred great research interests worldwide. In this review, we carefully examined the latest developments for these ferroelectric films with respect to alloy composition, temperature, film thickness, deposition condition, and fatigue endurance by electric field cycling. Looking ahead, there is an urgent need to resolve the challenge of large current leakage faced by these films, which necessitates a combined efforts from both simulations and experiments to identify the root cause and eventually come up with engineering strategies to suppress such leakage. In addition, overcoming the thickness scaling challenge to push ferroelectric thin film down to a few nanometers for better device miniaturization will also be of great interest. Considering the somewhat unexpected discovery of AlN‐based thin films with potential ferroelectric application, we believe that it will be also rewarding to further explore other III‐V‐based semiconductor materials.
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- Award ID(s):
- 2015557
- PAR ID:
- 10509688
- Publisher / Repository:
- Wiley
- Date Published:
- Journal Name:
- Journal of the American Ceramic Society
- Volume:
- 107
- Issue:
- 3
- ISSN:
- 0002-7820
- Page Range / eLocation ID:
- 1571 to 1581
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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