Axial p–n-junctions in nanowires
Bibliographic record
Abstract
The charge distribution and potential profile of p-n-junctions in thin semiconductor nanowires (NWs) were analyzed. The characteristics of screening in one-dimensional systems result in a specific profile with large electric field at the boundary between the n- and p- regions, and long tails with a logarithmic drop in the potential and charge density. As a result of these tails, the junction properties depend sensitively on the geometry of external contacts and its capacity has an anomalously large value and frequency dispersion. In the presence of an external voltage, electrons and holes in the NWs can not be described by constant quasi-Fermi levels, due to small values of the average electric field, mobility, and lifetime of carriers. Thus, instead of the classical Sah-Noice-Shockley theory, the junction current-voltage characteristic was described by an alternative theory suitable for fast generation-recombination and slow diffusion-drift processes. For the non-uniform electric field in the junction, this theory predicts the forward branch of the characteristic to have a non-ideality factor η several times larger than the values 1 < η < 2 from classical theory. Such values of η have been experimentally observed by a number of researchers, as well as in the present work.
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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".