Improving Charge Carrier Transport Properties in AlGaN Deep Ultraviolet Light Emitters Using Al-Content Engineered Superlattice Electron Blocking Layer
Bibliographic record
Abstract
In this study, we investigate a unique Al-content engineered superlattice electron blocking layer (AESL-EBL) for improving the charge carrier transport properties of AlGaN quantum well (QW) deep ultraviolet (DUV) light-emitting diode (LED) structures. LED structures without EBL, with conventional bulk EBL (BEBL), and superlattice EBL (SL-EBL) are used for comparison. It is found that the LED structure with the AESL-EBL can exhibit superior electron blocking and hole injection, leading to reduced efficiency droop and improved light output power, compared to LED structures without EBL and with BEBL. Notably, the LED structure with AESL-EBL also outperforms the LED structure with SL-EBL, benefitting from the Al-content engineered SL. In the end, such an ASEL-EBL is applied to a DUV laser diode structure, and by optimizing the device structure low Mg-induced internal loss of around 1 cm−1 can be obtained.
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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".