Improvement of light extraction efficiency in AlGaInP-based vertical miniaturized-light-emitting diodes via surface texturing
Bibliographic record
Abstract
AlGaInP-based light-emitting diodes (LEDs) suffer from a low external quantum efficiency (EQE), which is mainly restrained by the poor light extraction efficiency. Here, we demonstrate AlGaInP-based vertical miniaturized-LEDs (mini-LEDs) with a porous n-AlGaInP surface using a wet etching process to boost light extraction. We investigated the effects of etching time on the surface morphology of the porous n-AlGaInP surface. We found that as the etching time is prolonged, the density of pores increases initially and decreases subsequently. In comparison with the vertical mini-LED with a smooth n-AlGaInP surface, the vertical mini-LEDs with the porous n-AlGaInP surface reveal improvement in light output power and EQE, meanwhile, without the deterioration of electrical performance. The highest improvement of 38.9% in EQE measured at 20 mA is observed from the vertical mini-LED with the maximum density of the pores. Utilizing a three-dimensional finite-difference time-domain method, we reveal the underlying mechanisms of improved performance, which are associated with suppressed total internal reflection and efficient light scattering effect of the pores.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".