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Record W4386250361 · doi:10.21203/rs.3.rs-3257620/v1

Semi-classical physics based model in AlGaN/BGaN based Ultraviolet LED with p-AlGaN layer sandwiched around electron-blocking layer for droop-free efficiency

2023· preprint· en· W4386250361 on OpenAlexaff
G. Saranya, N. M. Sivamangai, J. Ajayan, S. Sreejith

Bibliographic record

VenueResearch Square · 2023
Typepreprint
Languageen
FieldPhysics and Astronomy
TopicGaN-based semiconductor devices and materials
Canadian institutionsHorizon College and Seminary
FundersKarunya University
KeywordsVoltage droopOptoelectronicsQuantum efficiencyLight-emitting diodeQuantum tunnellingPhysicsUltravioletAuger effectElectronDiodeMaterials scienceLuminous fluxRadiative transferSpontaneous emissionOpticsVoltageLaser

Abstract

fetched live from OpenAlex

Abstract This work reports the droop-free efficiency of an Ultraviolet Light Emitting Diode (UV LED) of Multiple Quantum Well (MQW) with an Electron Blocking Layer (EBL) sandwiched with a p-AlGaN layer. In the proposed device structure, the BGaN Quantum Well's thickness and boron content are set at 3 nm and 10%, respectively. The simulation is carried out by using varius physical models such as K.P. model, Auger recombination model, Shcokley-Reed-Hall (SRH) recombination model, and Lorentz model are used to produce the realistic optical performances. Internal Quantum Efficiency, Output Luminous power, and radiative recombination rate are the variables examined in this study. Also, the polarization effect decreases due to the insertion of a thin p-AlGaN layer which in turn reduces electron leakage to the p-type layer while enhancing the efficiency of hole injection via intra-band tunneling. In order to understand the structures of radiative and non-radiative recombination mechanisms, a semi-classical physics-based model is created. It is discovered that the simsulated results and modeled data fit well with each other.

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)0.002
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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.242
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.002
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 head0.082
GPT teacher head0.370
Teacher spread0.288 · 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 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

Citations0
Published2023
Admission routes1
Has abstractyes

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