Extending group‐III nitrides to the infrared: Recent advances in InN (Phys. Status Solidi B 5/2015)
Bibliographic record
Abstract
InN, with its narrow band gap (∼0.65 eV) and large electron mobility (12,000 cm2/V s), is an extremely important material for ultrahigh speed electronics and near-infrared photonics. However, compared to the relatively mature GaN-based devices, the development of InN-based devices has been severely limited by the presence of extremely high residual electron concentrations in nominally undoped structures, the commonly measured surface electron accumulation, and the difficulty in realizing p-type conduction. In their Feature Article (pp. 1050–1062), Mi and Zhao provide an overview on the recent advances made in InN, including both conventional planar structures and emerging nanowires. With the improved epitaxial growth process, the background electron concentration of InN nanowires grown directly on Si can be reduced to ∼1013 cm-3, approaching the intrinsic limit of InN. More importantly, through direct Mg dopant incorporation, the surfaces of InN nanowires can be transformed from intrinsic to nearly p-type degenerate, enabling the first observation of direct p-type conduction of InN. The cover image shows a scanning electron microscope image of intrinsic InN nanowires grown directly on Si substrate and the X-ray photoelectron spectrum of Mg-doped InN nanowires. The near-surface Fermi-level is measured to be ∼0.1 eV above the valence band maximum, showing p-type characteristics for the as-grown InN surfaces. [To honour the laureates of the Nobel Prize in Physics 2014, the cover of this pss (b) issue shows a specially designed pss logo assembled from blue nitride LEDs (courtesy of Armin Dadgar, Technical University of Magdeburg)].
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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 | 0.001 |
| 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".