Optimization of Doping Concentration to Obtain High Internal Quantum Efficiency and Wavelength Stability in An InGaN/GaN Blue Light-Emitting Diode
Bibliographic record
Abstract
We conduct a comprehensive study to investigate the feasibility of achieving high internal quantum efficiency (IQE) and wavelength stability in an InGaN/GaN blue light-emitting diode (LED) through numerical simulations with different doping concentrations. To ensure accurate calculations, we emulated the structure of an LED, fabricated on freestanding GaN with low defect density, abrupt interfaces, and high-performing characteristics, which resemble ideal conditions. Our objective is to determine the optimal doping concentration of the claddings using the Quantum Drift-Diffusion (QDD) model. We tested three concentrations ( C low , C middle , C h ig h ), and found that C middle produced the highest IQE of 82.5%, the most stable wavelength λ ˆ = 457.0 ± 1.2 nm in the range of (0.08–63.25) mA, an optical power of P = 14.76 mWs−1, and a forward voltage of V middle = 3.81 V at 20 mA. We suggest that using this concentration leads to the parameters closest to those of the reference device.
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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 | 0.001 |
| 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 | 0.001 | 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".