Opportunities and pitfalls in patterned self-catalyzed GaAs nanowire growth on silicon
Bibliographic record
Abstract
Periodic arrays of self-catalyzed GaAs nanowires (NWs) were grown on Si substrates by gas source molecular beam epitaxy (GS-MBE) using patterned oxide templates. The various challenges of the patterning process that result in undesired outcomes are described, such as pattern transfer by wet/dry etching, oxide thickness variations, and native oxide re-growth. Transmission electron microscopy (TEM) results are used to illustrate each case. In particular, we show that a linearly increasing length–radius distribution, analogous to that observed for unpatterned self-catalyzed growth on substrates with thin oxides, may be obtained even when using patterned oxide masks due to an unintended residual layer of oxide, as confirmed by TEM analysis. We explain how a linear length–radius dependence can result from the individual NWs beginning their growth at different times, accompanied by significant radial growth. The spread in obtained NW dimensions was decreased by improving the patterning method.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 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 | 0.001 | 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 source (direct Gemma or distilled Codex), 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".