Electrical characterization of chemical and dielectric passivation of InAs nanowires
Bibliographic record
Abstract
Abstract The native oxide at the surface of III−V nanowires, such as InAs, can be a major source of charge noise and scattering in nanowire-based electronics, particularly for quantum devices operated at low temperatures. Surface passivation provides a means to remove the native oxide and prevent its regrowth. Here, we study the effects of surface passivation and conformal dielectric deposition by measuring electrical conductance through nanowire field effect transistors treated with a variety of surface preparations. By extracting field effect mobility, subthreshold swing, threshold shift with temperature, and the gate hysteresis for each device, we infer the relative effects of the different treatments on the factors influencing transport. It is found that a combination of chemical passivation followed by deposition of an aluminum oxide dielectric shell yields the best results compared to the other treatments, and comparable to untreated nanowires. Finally, it is shown that an entrenched, top-gated device using an optimally treated nanowire can successfully form a stable double quantum dot at low temperatures. The device has excellent electrostatic tunability owing to the conformal dielectric layer and the combination of local top gates and a global back gate.
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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) | 0.000 | 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".