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Record W4297496748 · doi:10.1364/ao.470083

Impact of a prestrained graded InGaN/GaN interlayer towards enhanced optical characteristics of a multi-quantum well LED based on silicon substrate

2022· article· en· W4297496748 on OpenAlexaff
Samadrita Das, Trupti Ranjan Lenka, Fazal Ahmed Talukdar, Sharif Sadaf, Ravi Teja Velpula, Hieu Pham Trung Nguyen

Bibliographic record

VenueApplied Optics · 2022
Typearticle
Languageen
FieldPhysics and Astronomy
TopicGaN-based semiconductor devices and materials
Canadian institutionsInstitut National de la Recherche Scientifique
FundersScience and Engineering Research BoardNational Science Foundation
KeywordsMaterials scienceOptoelectronicsLight-emitting diodeIndium gallium nitrideIndiumSiliconDiodeSubstrate (aquarium)Quantum efficiencyPolarization (electrochemistry)Quantum wellLayer (electronics)Gallium nitrideOpticsLaserNanotechnology

Abstract

fetched live from OpenAlex

This paper presents alternate pairs of InGaN/GaN prestrained layers with varying indium compositions, which are inserted between the GaN/InGaN MQW active region and the n-GaN layer in a light-emitting diode (LED) nanostructure in order to obtain enhanced optical characteristics. The device is mounted on a silicon substrate followed by a GaN buffer layer that promotes charge injection by minimizing the energy barrier between the electrode and active layers. The designed device attains more than 2.897% enhancement in efficiency when compared with the conventional LED, which is attributed to the reduction of a polarization field within the MQW region. The proposed device with 15% indium composition in the prestrained layer attains a maximum efficiency of 85.21% and a minimized efficiency droop of 3.848% at an injection current of 40 mA, with high luminous power in the output spectral range. The device also shows a minimum blueshift in the spectral range, indicating a decrease in the piezoelectric polarization.

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 score0.036
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.018
GPT teacher head0.269
Teacher spread0.251 · 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

Citations8
Published2022
Admission routes1
Has abstractyes

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