Surface Emitting, High Efficiency Near-Vacuum Ultraviolet Light Source with Aluminum Nitride Nanowires Monolithically Grown on Silicon
Bibliographic record
Abstract
To date, it has remained challenging to realize electrically injected light sources in the vacuum ultraviolet wavelength range (∼200 nm or shorter), which are important for a broad range of applications, including sensing, surface treatment, and photochemical analysis. In this Letter, we have demonstrated such a light source with molecular beam epitaxially grown aluminum nitride (AlN) nanowires on low cost, large area Si substrate. Detailed angle dependent electroluminescence studies suggest that, albeit the light is TM polarized, the dominant light emission direction is from the nanowire top surface, that is, along the c axis, due to the strong light scattering effect. Such an efficient surface emitting device was not previously possible using conventional c-plane AlN planar structures. The AlN nanowire LEDs exhibit an extremely large electrical efficiency (>85%), which is nearly ten times higher than the previously reported AlN planar devices. Our detailed studies further suggest that the performance of AlN nanowire LEDs is predominantly limited by electron overflow. This study provides important insight on the fundamental emission characteristics of AlN nanowire LEDs and also offers a viable path to realize an efficient surface emitting near-vacuum ultraviolet light source through direct electrical injection.
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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".