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1. Epitaxial Sc _x Al _{1− x} N on GaN exhibits attractive high-K dielectric properties

   https://doi.org/10.1063/5.0075636  

   Casamento, Joseph; Lee, Hyunjea; Maeda, Takuya; Gund, Ved; Nomoto, Kazuki; van Deurzen, Len; Turner, Wesley; Fay, Patrick; Mu, Sai; Van de Walle, Chris G.; et al (April 2022, Applied Physics Letters)

   Epitaxial Sc x Al 1− x N thin films of ∼100 nm thickness grown on metal polar GaN substrates are found to exhibit significantly enhanced relative dielectric permittivity (ε r ) values relative to AlN. ε r values of ∼17–21 for Sc mole fractions of 17%–25% ( x = 0.17–0.25) measured electrically by capacitance–voltage measurements indicate that Sc x Al 1− x N has the largest relative dielectric permittivity of any existing nitride material. Since epitaxial Sc x Al 1− x N layers deposited on GaN also exhibit large polarization discontinuity, the heterojunction can exploit the in situ high-K dielectric property to extend transistor operation for power electronics and high-speed microwave applications. 

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2. Strongly enhanced second-order optical nonlinearity in CMOS-compatible Al _1−x Sc _x N thin films

   https://doi.org/10.1063/5.0061787  

   Yoshioka, Valerie; Lu, Jian; Tang, Zichen; Jin, Jicheng; Olsson, Roy H.; Zhen, Bo (October 2021, APL Materials)
3. Machine learning–guided optimization of coercive field in Al _{1− x} Sc _x N thin films for nonvolatile memory

   https://doi.org/10.1111/jace.20347  

   Das, Shaon; Garg, Prachi; Mazumder, Baishakhi (April 2025, Journal of the American Ceramic Society)

   Abstract This study employs a data‐driven machine learning approach to investigate specific ferroelectric properties of Al_1−xSc_xN 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 Al_1−xSc_xN‐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 Al_1−xSc_xN‐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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4. A Landau–Devonshire analysis of strain effects on ferroelectric Al _1−x Sc _x N

   https://doi.org/10.1063/5.0098979  

   Yazawa, Keisuke; Zakutayev, Andriy; Brennecka, Geoff L. (July 2022, Applied Physics Letters)

   We present a thermodynamic analysis of the recently discovered nitride ferroelectric materials using the classic Landau–Devonshire approach. Electrostrictive and dielectric stiffness coefficients of Al 1− x Sc x N with a wurtzite structure ( 6 mm) are determined using a free energy density function assuming a hexagonal parent phase (6/ mmm), with the first-order phase transition based on the dielectric stiffness relationships. The results of this analysis show that the strain sensitivity of the energy barrier is one order of magnitude larger than that of the spontaneous polarization in these wurtzite ferroelectrics, yet both are less sensitive to strain compared to classic perovskite ferroelectrics. These analysis results reported here explain experimentally reported sensitivity of the coercive field to elastic strain/stress in Al 1− x Sc x N films and would enable further thermodynamic analysis via phase field simulation and related methods. 

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5. Phase stability and mechanical properties of Mo _{1- x} N _x with 0 ≤ x ≤ 1

   https://doi.org/10.1063/1.4998686  

   Balasubramanian, Karthik; Huang, Liping; Gall, Daniel (November 2017, Journal of Applied Physics)