Wafer-scale surface roughening for enhanced light extraction of high power AlGaInP-based light-emitting diodes
Bibliographic record
Abstract
A new approach to surface roughening was established and optimized in this paper for enhancing the light extraction of high power AlGaInP-based LEDs, by combining ultraviolet (UV) assisted imprinting with dry etching techniques. In this approach, hexagonal arrays of cone-shaped etch pits are fabricated on the surface of LEDs, forming gradient effective-refractive-index that can mitigate the emission loss due to total internal reflection and therefore increase the light extraction efficiency. For comparison, wafer-scale FLAT-LEDs without any surface roughening, WET-LEDs with surface roughened by wet etching, and DRY-LEDs with surface roughened by varying the dry etching time of the AlGaInP layer, were fabricated and characterized. The average output power for wafer-scale FLAT-LEDs, WET-LEDs, and DRY3-LEDs (optimal) at 350 mA was found to be 102, 140, and 172 mW, respectively, and there was no noticeable electrical degradation with the WET-LEDs and DRY-LEDs. The light output was increased by 37.3% with wet etching, and 68.6% with dry etching surface roughening, respectively, without compromising the electrical performance of LEDs. A total number of 1600 LED chips were tested for each type of LEDs. The yield of chips with an optical output power of 120 mW and above was 0.3% (4 chips), 42.8% (684 chips), and 90.1% (1441 chips) for FLAT-LEDs, WET-LEDs, and DRY3-LEDs, respectively. The dry etching surface roughening approach developed here is potentially useful for the industrial mass production of wafer-scale high power LEDs.
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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".