研究目的
To develop and apply a method for determining nitrogen distribution in dilute nitride GaAsSbN superlattices using STEM imaging to overcome challenges in characterizing N due to low content and simultaneous presence of Sb.
研究成果
The proposed method successfully separates Sb and N contributions in STEM images, enabling semi-quantitative mapping of N distribution. It reveals differences in N clustering between type-I and type-II superlattices, with type-I showing more N-rich regions, potentially impacting optical properties. This approach provides a valuable tool for characterizing dilute nitride materials.
研究不足
The method assumes linearity in intensity-concentration relationships, which may not hold for higher N contents. It relies on reference regions without N, and the accuracy is affected by sample thickness variations and detector noise. The semi-quantitative nature depends on complementary techniques like EDX and XRD.
1:Experimental Design and Method Selection:
The method combines simultaneous acquisition of Low Angle (LA-) and High Angle (HA-) Annular Dark Field (ADF) images in STEM mode, using mathematical normalization to separate Sb and N contributions.
2:Sample Selection and Data Sources:
Two types of superlattices (SL-I: GaAsSbN/GaAs and SL-II: GaAsSb/GaAsN) grown by molecular beam epitaxy on GaAs substrates were used.
3:List of Experimental Equipment and Materials:
Equipment includes a FIB FEI Quanta 3D Dual Beam for sample preparation, a FEI Titan3 Cubed Themis STEM operated at 200 kV for imaging and EDX, and a PANalytical HR-XRD system. Materials include GaAs substrates and superlattice samples.
4:Experimental Procedures and Operational Workflow:
Cross-sectional samples were prepared using FIB. HAADF and LAADF images were acquired simultaneously with EDX mapping. Intensity normalization was performed using GaAs substrate as reference, and N distribution maps were derived from intensity ratios.
5:Data Analysis Methods:
Data analysis involved mathematical processing of image intensities, integration with EDX and HR-XRD results for semi-quantitative mapping, and statistical evaluation of cluster densities.
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