(Invited) High Efficiency, Color-Tunable InGaN/GaN Nanowire Light Emitting Diode Arrays
Bibliographic record
Abstract
The monolithic integration of red, green and blue (RGB) light-emitting diodes (LEDs) directly on a single chip is critically important for smart lighting and full-color display applications. The resulting color-tunable LEDs can exhibit excellent color rendering capability, high efficiency, and low power consumption, compared to conventional phosphor-based white LEDs. In this context, we have investigated the molecular beam epitaxial growth, fabrication and characterization of InGaN nanowire RGB LEDs monolithically integrated on a single chip, including Si and sapphire substrate. We have achieved controllable and tunable full-color light generation from InGaN LEDs. The correlated color temperature can be continuously varied in the range of 1900 K to 6800 K, while maintaining excellent color rendering index capability (CRI > 90). In this work, RGB InGaN/GaN dot-in-a-wire LED arrays were grown on Si using a three-step selective area growth (SAG) technique, wherein blue, green, and red-emitting LEDs were grown in sequence. In this process, a SiO x mask layer was used to protect the previously grown devices and also to precisely define the growth position of each nanowire LED pixel. Tunable PL 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 such lateral-arranged multi-color LED subpixels, the CCT value can be varied from ~1900 K to 6800 K, while maintaining relatively high CRI values (up to ~ 90, or higher). We have further investigated the molecular epitaxial growth and fabrication of multi-color nanowire LED arrays on GaN template on sapphire substrate. In this process, a thin (~ 10 nm) Ti layer was utilized as the mask, and nanoscale growth patterns were defined by e-beam lithography. By controlling the nanowire diameters, multi-color emission can be achieved in a single epitaxial growth step. We have also demonstrated multi-color nanowire LEDs on sapphire substrate that can exhibit excellent current-voltage characteristics, high power operation, and reduced efficiency droop. In summary, we have developed tunable, full-color InGaN nanowire LED arrays on Si and sapphire substrate by using special selective area growth techniques. Through controlled light mixing at the wafer-level, we have demonstrated color-tunable light emission with an unprecedented tuning range for the CCT, from 1900 K to 6800K, while maintaining excellent CRI values. The realization of electrically injected RGB InGaN nanowire lasers is being studied and will also be reported.
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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 | 0.001 | 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".