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 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) | 0.000 | 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 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".