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Subject: scanning transmission electron microscopy (STEM) ×

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    Analysis of Mechanical Strain in AlGaN/GaN HFETs
    (Weinheim : Wiley-VCH, 2023) Yazdani, Hossein; Graff, Andreas; Simon-Najasek, Michél; Altmann, Frank; Brunner, Frank; Ostermay, Ina; Chevtchenko, Serguei; Würfl, Joachim
    Herein, the influence of mechanical strain induced by passivation layers on the electrical performance of AlGaN/GaN heterostructure field-effect transistor is investigated. We studied the physical mechanism of a threshold voltage (Vth) shift for the monolithically fabricated on/off devices reported earlier by our group. For that, theoretical calculations, simulation-based analysis, and nano-beam electron diffraction (NBED) measurements based on STEM are used. Strain distribution in the gate vicinity of transistors is compared for a SiNx passivation layer with intrinsic stress from ≈0.5 to −1 GPa for normally on and normally off devices, respectively. The strain in epitaxial layers transferred by intrinsic stress of SiNx is quantitatively evaluated using NEBD method. Strain dissimilarity Δε = 0.23% is detected between normally on and normally off devices. Using this method, quantitative correlation between 1.13 V of Vth shift and microscopic strain difference in the epitaxial layers caused by 1.5 GPa intrinsic stress variation in passivation layer is provided. It is showed in this correlation that about half of the reported threshold voltage shift is induced by strain, i.e., by the piezoelectric effect. The rest of Vth shift is caused by the fabrication process. Therefore, various components/mechanisms contributing to the measured Vth shift are distinguished.
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    Atomic-Scale Mapping and Quantification of Local Ruddlesden-Popper Phase Variations
    (Washington, DC : ACS Publ., 2022) Fleck, Erin E.; Barone, Matthew R.; Nair, Hari P.; Schreiber, Nathaniel J.; Dawley, Natalie M.; Schlom, Darrell G.; Goodge, Berit H.; Kourkoutis, Lena F.
    The Ruddlesden-Popper (An+1BnO3n+1) compounds are highly tunable materials whose functional properties can be dramatically impacted by their structural phase n. The negligible differences in formation energies for different n can produce local structural variations arising from small stoichiometric deviations. Here, we present a Python analysis platform to detect, measure, and quantify the presence of different n-phases based on atomic-resolution scanning transmission electron microscopy (STEM) images. We employ image phase analysis to identify horizontal Ruddlesden-Popper faults within the lattice images and quantify the local structure. Our semiautomated technique considers effects of finite projection thickness, limited fields of view, and lateral sampling rates. This method retains real-space distribution of layer variations allowing for spatial mapping of local n-phases to enable quantification of intergrowth occurrence and qualitative description of their distribution suitable for a wide range of layered materials.