MétaCan
Menu
Back to cohort
Record W4412511591 · doi:10.1149/ma2025-01351680mtgabs

Nearly Dislocation Free Top-Down Blue Nano-LED Pixels on Bulk GaN Substrates

2025· article· en· W4412511591 on OpenAlexaboutno aff
Nirmal Anand, G. Muzioł, Sharif Sadaf

Bibliographic record

VenueECS Meeting Abstracts · 2025
Typearticle
Languageen
FieldPhysics and Astronomy
TopicGaN-based semiconductor devices and materials
Canadian institutionsnot available
Fundersnot available
KeywordsDislocationNano-Materials scienceOptoelectronicsNanotechnologyComposite material

Abstract

fetched live from OpenAlex

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

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.092
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.010
GPT teacher head0.243
Teacher spread0.233 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueECS Meeting AbstractsSame topicGaN-based semiconductor devices and materialsFrench-language works237,207