Impurity and Defect Monitoring in Hexagonal Si and SiGe Nanocrystals
Bibliographic record
Abstract
Silicon and Germanium naturally occur in a cubic crystal structure and have an indirect bandgap 1 making them unsuitable materials for light emission. When synthesized in a hexagonal crystal structure Germanium and Silicon-Germanium are however predicted to have a direct bandgap 2 which makes these crystal phases candidate materials for integrating optical functionality into silicon based CMOS. We have recently demonstrated the synthesis of hexagonal silicon 3 using Gallium-phosphide nanowires with a hexagonal crystal lattice 4 as a virtual substrate. Based on this approach we have succeeded in growing hexagonal Germanium and Silicon-Germanium both as shells around and branches on Gallium-phosphide nanowires. We are now aiming to grow optically active hexagonal Germanium and Silicon-Germanium with a direct bandgap. However, in order to create efficient light sources, we will have to suppress non-radiative recombination channels in the material and hence grow defect and impurity free hexagonal Germanium and Silicon-Germanium. This makes it necessary to measure the defect and impurity level inside the hexagonal shells and branches. Unfortunately, the size and the geometry of the structures make these measurements challenging as typical bulk characterization methods like Secondary Ion Mass Spectroscopy or X-Ray Diffraction are not applicable. Here we will show how we use Transmission Electron Microscopy (TEM) and Atom Probe Tomography (APT) to monitor the crystal structure, crystal defect density, impurity level, interface roughness and matrix concentration in this complex material system. TEM allows us to investigate the defect density and defect formation during the growth of the hexagonal lattices making it possible to work towards the growth of defect free structures. APT allows us to create three-dimensional images of the elemental distribution inside the structures and hence makes it possible to supervise the impurity level, interface roughness and matrix concentration in the structures. Combining the two methods thus enables us to optimize the growth parameters, minimizing both the defect and the impurity density. [1] M. Cardona et al. Phys Rev., 142:530, 1966. [2] C.Raffy et al. Phys. Rev. B, 66:075201, 2002. [3] H. I. T. Hauge et al. Nano Lett., 15(9):5855, 2015. [4] S. Assali et al. Nano Lett., 13(4):1559, 2013. Figure 1: Defects and impurities in a hexagonal Si shell (a-d) and hexagonal Ge branches (e,f) grown on GaP nanowires. Side-view imaging with TEM (a,c,f) enables us to identify defects. In combination with cross-section TEM images (b) we can confirm the epitaxial growth of Si on the hexagonal GaP lattice. APT analyses of the Si shells (d) and the Ge branches (e) allow us to map impurity atoms in the hexagonal crystals and reveal the diffusion of Ga and P into the Si and the Ge respectively. Figure 1
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".