Aberration corrected <scp>CVD‐TEM</scp> for in‐situ growth of <scp>III–V</scp> semiconductors
Bibliographic record
Abstract
We will show the first results from a newly designed Environmental TEM with cold FEG emitter, aberration corrector, heating stage and a free choice of up to nine different gaseous reactants to produce heterostructures in‐situ during real‐time observation and analysis. Observing growth of semiconductors on an atomic scale, under MOCVD or CVD‐like conditions, will bring valuable information and understanding of the possibilities and limitations for making devices, solar cells and LED:s from nanowires. We have constructed a 300 kV Environmental TEM, with cold FEG emitter, heating stage and a free choice of up to nine different gaseous reactants to produce heterostructures in‐situ. The use of an aplanatic B‐COR image corrector provides space in the objective lens polepiece for a heating stage reactor with gas inlets, while still achieving an 86 pm point resolution (in vacuum). The gas inlets are designed to give a total pressure of up to10 Pa, which is sufficient to grow nanowires at a reasonable speed. The gas handling system will allow switching between the different gases at will, or through programmed sequences. Changes and defects in the crystal structure, and the effect on the outer shape of the nanowires can be followed by combinations of conventional HREM imaging, STEM‐DF and STEM‐BF, as well as SE‐imaging. Simultaneous analysis by XEDS will provide chemical changes along the growth path.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".