Nearly Dislocation Free Top-Down Blue Nano-LED Pixels on Bulk GaN Substrates
Notice bibliographique
Résumé
InGaN quantum well (QW) light emitters are the forefront of various technologies, such as solid-state lighting, visible light communication (VLC) and advanced near-eye displays (augmented- and virtual reality glasses) 1, 2 . However, InGaN QW based long wavelength emitters (i.e. green and red spectral region) face an insurmountable challenge known as the green gap ; which is the systematic drop in efficiency in the green‒red spectral range. The high indium (In) content required in InGaN QWs emitting in the green spectral region, degrades the crystal quality and aggravates the quantum confined Stark effect (QCSE), mainly due to the 11% lattice mismatch between InN and GaN 3 . This results in increased nonradiative recombination and reduced overlap of electron and hole wave functions. In general, the epitaxial growth of InGaN remains challenging due to the high threading dislocation densities (>10⁹/cm²) in commercially available GaN templates grown on c-plane sapphire 4 . These dislocations not only act as nonradiative recombination centers (NRCs) but also hinder In incorporation, which is essential for achieving long-wavelength (green/red) emission. Previous studies have shown that nanopatterning can alleviate compressive strain in InGaN QWs and also reduce dislocation densities, however, these reports have solely focused on InGaN QWs with GaN barriers grown via metal organic chemical vapor deposition (MOCVD) 4, 5 . To date, no study has compared the strain relaxation in top-down nanowires with InGaN QWs grown on single-crystal GaN substrates using plasma-assisted molecular beam epitaxy (PAMBE) versus those grown via MOCVD. In this context, we develop a unique hybrid approach that combines epitaxial growth on single-crystal GaN substrates with subsequent top-down nanowire processing in In-rich InGaN heterostructures. Figure 1(a) schematically illustrates the PAMBE-grown InGaN QW heterostructure with InGaN barriers. The blue-emitting In 0.179 Ga 0.821 N QW layer was grown under optimized growth conditions with increased nitrogen flux. We further applied the top-down nanowire fabrication technique to the PAMBE-grown InGaN QW heterostructure (shown in Figure 1(a)). Next, a 500 nm diameter and 700 nm pitch in a [20] μm² ultra-dense nanowire array was fabricated to investigate their performance as µLED pixels. Figures 1(b) and (c) show the SEM images of ultra-dense nanowires and high-magnification view of the nanowires after passivation with atomic layer deposited (ALD) Al 2 O 3 and spin-on glass (SOG) planarization, respectively. Figures 1(d) shows the electroluminescence spectra of the nanowire µLED pixel, measured as a function of the injection current density ranging from 35 A/cm 2 to 1280 A/cm 2 . The inset shows the electroluminescence in log-scale. A noticeable shift of ~8 nm exists when injection current density increases from 35 A/cm 2 to 1280 A/cm 2 . Figure 1(e) presents the I-V characteristics of the nanowire µLED pixel, demonstrating excellent rectifying behavior, with several orders of magnitude in higher current density during forward bias and significantly reduced leakage current under reverse bias. Finally, Figure 1(f) shows the relative external quantum efficiency (EQE) and the light output power (LOP) of the nanowire µLED pixel. Current work is in progress to investigate and compare the efficiency droop trend and mechanisms of the blue PAMBE-grown nanowire LED pixel with its MOCVD-grown counterpart. Additionally, the effect of nanopatterning on PAMBE grown wide InGaN QWs with InGaN barriers will be discussed. Acknowledgement: This work was supported by the Natural Sciences and Engineering Research Council of Canada (NSERC) through Alliance Grant Programs. (1) M. Zak et al., Nat. Commun. , 14 , 7562 (2023). (2) N. Anand et al., ACS Nano , 18 , 26882–26890 (2024). (3) R. Ley et al., Opt. Express , 27 , 30081–30089 (2019). (4) G. T. Wang et al., Phys. Status Solidi A , 211 , 748–751 (2014). (5) Y. Kawakami et al., J. Appl. Phys. , 107 , 023522 (2010). Figure 1
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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».