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Record W2004424558 · doi:10.1166/jnn.2010.2639

Light Extraction Efficiency Enhancement of GaN-Based Light Emitting Diodes on <i>n</i>-GaN Layer Using a SiO<sub>2</sub> Photonic Quasi-Crystal Overgrowth

2010· article· en· W2004424558 on OpenAlexaff
Hung-Wen Huang, K. Y. Lee, J. K. Huang, Chu-Hsuan Lin, Chia‐Feng Lin, Chang-Hung Yu, Hao‐Chung Kuo

Bibliographic record

VenueJournal of Nanoscience and Nanotechnology · 2010
Typearticle
Languageen
FieldPhysics and Astronomy
TopicGaN-based semiconductor devices and materials
Canadian institutionsLuxmux Technology (Canada)
Fundersnot available
KeywordsMaterials scienceOptoelectronicsLight-emitting diodeLayer (electronics)Photonic crystalLithographyNanosphere lithographyDiodeGallium nitridePhotonicsPower (physics)TransistorNanotechnologyFabrication

Abstract

fetched live from OpenAlex

In this paper, GaN-based LEDs with a SiO2 photonic quasi-crystal (PQC) pattern on an n-GaN layer by nano-imprint lithography (NIL) are fabricated and investigated. At a driving current of 20 mA on Transistor Outline (TO)-can package, the better light output power of LED III (d = 1.2 microm) was enhanced by a factor of 1.20. After 1000 h life test (55 degrees C/50 mA) condition, Normalized output power of LED with a SiO2 PQC pattern (LED III (d = 1.2 microm)) on an n-GaN layer only decreased by 5%. This results offer promising potential to enhance the light output power of commercial light-emitting devices using the technique of nano-imprint lithography.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.253
Teacher spread0.243 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations1
Published2010
Admission routes1
Has abstractyes

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