Single-step-fabricated disordered metasurfaces for enhanced light extraction from LEDs
Bibliographic record
Abstract
While total internal reflection (TIR) lays the foundation for many important applications, foremost fibre optics that revolutionised information technologies, it is undesirable in some other applications such as light-emitting diodes (LEDs), which are a backbone for energy-efficient light sources. In the case of LEDs, TIR prevents photons from escaping the constituent high-index materials. Advances in material science have led to good efficiencies in generating photons from electron-hole pairs, making light extraction the bottleneck of the overall efficiency of LEDs. In recent years, the extraction efficiency has been improved, using nanostructures at the semiconductor/air interface that outcouple trapped photons to the outside continuum. However, the design of geometrical features for light extraction with sizes comparable to or smaller than the optical wavelength always requires sophisticated and time-consuming fabrication, which causes a gap between lab demonstration and industrial-level applications. Inspired by lightning bugs, we propose and realise a disordered metasurface for light extraction throughout the visible spectrum, achieved with single-step fabrication. By applying such a cost-effective light extraction layer, we improve the external quantum efficiency by a factor of 1.65 for commercialised GaN LEDs, demonstrating a substantial potential for global energy-saving and sustainability.
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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".