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Three-Dimensional Composition and Electric Potential Mapping of III–V Core–Multishell Nanowires by Correlative STEM and Holographic Tomography
The nondestructive characterization of nanoscale devices, such as those based on semiconductor nanowires, in terms of functional potentials is crucial for correlating device properties with their morphological/materials features, as well as for precisely tuning and optimizing their growth process. Electron holographic tomography (EHT) has been used in the past to reconstruct the total potential distribution in three-dimension but hitherto lacked a quantitative approach to separate potential variations due to chemical composition changes (mean inner potential, MIP) and space charges. In this Letter, we combine and correlate EHT and high-angle annular dark-field scanning transmission electron microscopy (HAADF-STEM) tomography on an individual ⟨111⟩ oriented GaAs–AlGaAs core–multishell nanowire (NW). We obtain excellent agreement between both methods in terms of the determined Al concentration within the AlGaAs shell, as well as thickness variations of the few nanometer thin GaAs shell acting as quantum well tube. Subtracting the MIP determined from the STEM tomogram, enables us to observe functional potentials at the NW surfaces and at the Au–NW interface, both ascribed to surface/interface pinning of the semiconductor Fermi level.
Dislocation-free Ge nano-crystals via pattern independent selective Ge heteroepitaxy on Si nano-tip wafers
The integration of dislocation-free Ge nano-islands was realized via selective molecular beam epitaxy on Si nano-tip patterned substrates. The Si-tip wafers feature a rectangular array of nanometer sized Si tips with (001) facet exposed among a SiO2 matrix. These wafers were fabricated by complementary metal-oxide-semiconductor (CMOS) compatible nanotechnology. Calculations based on nucleation theory predict that the selective growth occurs close to thermodynamic equilibrium, where condensation of Ge adatoms on SiO2 is disfavored due to the extremely short re-evaporation time and diffusion length. The growth selectivity is ensured by the desorption-limited growth regime leading to the observed pattern independence, i.e. the absence of loading effect commonly encountered in chemical vapor deposition. The growth condition of high temperature and low deposition rate is responsible for the observed high crystalline quality of the Ge islands which is also associated with negligible Si-Ge intermixing owing to geometric hindrance by the Si nano-tip approach. Single island as well as area-averaged characterization methods demonstrate that Ge islands are dislocation-free and heteroepitaxial strain is fully relaxed. Such well-ordered high quality Ge islands present a step towards the achievement of materials suitable for optical applications.
Numerical simulation of TEM images for In(Ga)As/GaAs quantum dots with various shapes
We present a mathematical model and a tool chain for the numerical simulation of TEM images of semiconductor quantum dots (QDs). This includes elasticity theory to obtain the strain profile coupled with the Darwin–Howie–Whelan equations, describing the propagation of the electron wave through the sample. We perform a simulation study on indium gallium arsenide QDs with different shapes and compare the resulting TEM images to experimental ones. This tool chain can be applied to generate a database of simulated TEM images, which is a key element of a novel concept for model-based geometry reconstruction of semiconductor QDs, involving machine learning techniques.
Numerical simulation of TEM images for In(Ga)As/GaAs quantum dots with various shapes
We present a mathematical model and a tool chain for the numerical simulation of TEM images of semiconductor quantum dots (QDs). This includes elasticity theory to obtain the strain profile coupled with the Darwin-Howie-Whelan equations, describing the propagation of the electron wave through the sample. We perform a simulation study on indium gallium arsenide QDs with different shapes and compare the resulting TEM images to experimental ones. This tool chain can be applied to generate a database of simulated TEM images, which is a key element of a novel concept for model-based geometry reconstruction of semiconductor QDs, involving machine learning techniques.
Correction to: Numerical simulation of TEM images for In(Ga)As/GaAs quantum dots with various shapes
Correction to: Optical and Quantum Electronics (2020) 52:257 https://doi.org/10.1007/s11082-020-02356-y