Wafer-scale surface roughening for enhanced light extraction of high power AlGaInP-based light-emitting diodes
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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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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 it