Wet etch methods for InAs nanowire patterning and self-aligned electrical contacts
Bibliographic record
Abstract
Advanced synthesis of semiconductor nanowires (NWs) enables their application in diverse fields, notably in chemical and electrical sensing, photovoltaics, or quantum electronic devices. In particular, indium arsenide (InAs) NWs are an ideal platform for quantum devices, e.g. they may host topological Majorana states. While the synthesis has been continously perfected, only a few techniques have been developed to tailor individual NWs after growth. Here we present three wet chemical etch methods for the post-growth morphological engineering of InAs NWs on the sub-100 nm scale. The first two methods allow the formation of self-aligned electrical contacts to etched NWs, while the third method results in conical shaped NW profiles ideal for creating smooth electrical potential gradients and shallow barriers. Low temperature experiments show that NWs with etched segments have stable transport characteristics and can serve as building blocks of quantum electronic devices. As an example we report the formation of a single electrically stable quantum dot between two etched NW segments.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".