High efficiency AlGaN deep ultraviolet light emitting diodes on silicon
Bibliographic record
Abstract
The performance of conventional Al(Ga)N planar devices decays drastically with increasing Al content, leading to low internal quantum efficiencies (IQEs) and high device operation voltages. In this paper, we show that these challenges can be addressed by utilizing epitaxially grown nitrogen polar (N-polar) Al(Ga)N nanowires. With a careful control of the growth conditions, a strong AlN band edge emission at 210 nm can be observed at room temperature, and an IQE of 80% was derived. Furthermore, the Mg incorporation can be drastically enhanced by controlling the growth rate. The hole concentrations of AlN:Mg nanowires were estimated to be on the order of 1016 cm-3, or higher at room temperature. 210 nm emitting AlN nanowire LEDs were achieved, which exhibit excellent electrical performance (at a forward current of 20 mA, the forward bias is about 8 V for a standard 300×300 μm2 device.). This can be ascribed to both efficient Mg doping and N-polarity induced internal electrical field that enhances hole injection. In the end, high performance AlGaN nanowire LEDs were demonstrated. This work provides a practical path for high efficiency DUV light sources with nanotechnology.
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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) | 0.001 | 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".