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Record W2072836097 · doi:10.1109/nusod.2010.5595695

Simulation for light power distribution of 3D InGaN/GaN MQW LED with textured surface

2010· article· en· W2072836097 on OpenAlexaff
Liwen Cheng, Yang Sheng, Changsheng Xia, Wei Lü, Michel Lestrade, Zhanming Li

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPhysics and Astronomy
TopicGaN-based semiconductor devices and materials
Canadian institutionsCrosslight Software (Canada)
Fundersnot available
KeywordsRay tracing (physics)Materials scienceTransmittanceFinite-difference time-domain methodOpticsLight-emitting diodePower (physics)OptoelectronicsSurface (topology)GeometryPhysicsMathematics

Abstract

fetched live from OpenAlex

In this paper, we introduce a full 3D simulation for light power distribution of an InGaN/GaN MQW LED with textured surface. Device simulation was performed by APSYS software to get power distribution of light source inside the LED. Based on this, ray tracing simulation was carried out to get light power distribution outside the LED. During the process of ray tracing, the textured surface was treated as a special boundary which means its reflectivity, transmittance and refraction angle are not calculated according to Fresnel equations, but obtained from data extracted from Finite-difference time-domain(FDTD) method. By comparing ray tracing result with and without textured surface, we found that textured surface not only smoothed transmitted power distribution, but also greatly improved power extraction efficiency which can be further improved by changing texture geometry.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.007
GPT teacher head0.244
Teacher spread0.237 · 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 designSimulation or modeling
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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