Tunable, full-color nanowire light emitting diode arrays monolithically integrated on Si and sapphire
Bibliographic record
Abstract
The monolithic integration of red, green and blue (RGB) GaN-based light-emitting diodes (LEDs) directly on a single chip is critically important for smart lighting and full color display applications. In this work, RGB InGaN/GaN dot-in-a-wire LED arrays were laterally arranged on a Si wafer using a three-step SiOx-mask selective area growth (SAG) technique, and on a sapphire wafer using a Ti-mask SAG technique. Tunable emission across the entire visible spectral range (~ 450 nm to 700 nm) can be readily achieved on a single Si wafer by varying the sizes and/or compositions of the dots. By separately biasing lateral-arranged multi-color LED subpixels, the correlated color temperature (CCT) values of such a ~ 0.016 mm2 pixel can be varied from ~ 1900 K to 6800 K. The RGB pixel size can be further reduced by using the Ti-mask SAG technique on sapphire wafer. Full-color InGaN/GaN nanowire arrays with sizes of 2.8 × 2.8 μm2 have been monolithically fabricated into the same pixel.
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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".