Dislocation-Free InGaN Nanoscale Light-Emitting Diode Pixels on Single-Crystal GaN Substrates
Bibliographic record
Abstract
Indium gallium nitride (InGaN) quantum well (QW) micro- and nanoscale light-emitting diodes (LEDs) are promising for next-generation ultrafast optical interconnects and augmented/virtual reality displays. However, scaling to nanoscale dimensions presents significant challenges including enhanced nonradiative surface recombination, defect and/or dislocation-related emission degradation, and nanoscale pixel contact formation. In this work, we demonstrate strain-engineered nanoscale blue LED pixels fabricated via top-down nanostructuring of an all-InGaN quantum well/barrier heterostructure grown by plasma-assisted molecular beam epitaxy (PAMBE) on significantly low dislocation-density single-crystal GaN substrates (in the order of ∼10 5 –10 6 cm –2; 2 to 3 orders of magnitude lower than commercial GaN/Sapphire templates). Sidewall passivation using atomic layer deposition (ALD) of Al 2 O 3 enables excellent diode behavior, including a rectification ratio >10 4 between −5 V and +5 V and extremely low reverse leakage (∼0.01 A/cm 2 at −10 V). Monte Carlo analyses suggest almost 100% yield of completely dislocation-free active regions for ∼450 nm nanopixels. Electroluminescence measurements show bright blue emission with a peak external quantum efficiency (EQE) of 0.46% at ∼1.2 kA/cm 2 . Poisson–Schrödinger simulations reveal ∼20% strain relaxation in the QW, effectively mitigating the quantum-confined Stark effect (QCSE). Additionally, finite-difference time-domain (FDTD) simulations confirm that the nanoscale geometry enhances light extraction efficiency by over 40% compared to planar designs, independent of substrate materials. These results establish a scalable pathway for dislocation-free, high-brightness InGaN μLED arrays suitable for advanced display and photonic systems.
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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 | 0.000 | 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 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".