Efficiency improvement for micro light-emitting diodes with n-doped quantum barriers and single quantum well
Bibliographic record
Abstract
In this article, efficiency performance of blue GaN/InGaN micro light emitting diodes (μ-LEDs) are investigated as functions of chip sizes, quantum barrier doping level and the number of quantum wells (QWs) by simulation. Internal quantum efficiency (IQE) and external quantum efficiency (EQE) drastically decrease with decreasing mesa sizes of μ-LEDs. The simulation indicates that μ-LEDs with n-doped quantum barriers can effectively suppress the Shockley-Read-Hall (SRH) nonradiative recombination and improve the efficiency compared to those with intrinsic quantum barriers in small μ-LEDs. The simulated results also show that decreasing the number of QWs can improve the IQE of μ-LEDs with higher radiative recombination rate in a single QW. An optimized design for 5×5 μm2 GaN/InGaN μ-LEDs with n-doped barriers and a single QW shows around 367% efficiency improvement at 1 A/cm2 comparing to the conventional intrinsic multiple QWs-based design in simulation.
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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 | 0.000 |
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".