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Record W2525464233 · doi:10.1116/1.4963369

Influence of hydrogen passivation on the luminescence of Si quantum dots embedded in Si3Nx

2016· article· en· W2525464233 on OpenAlexafffund
Carolyn C. F Cadogan, Lyudmila V. Goncharova, P. J. Simpson, Peter H. Nguyen, Zhiqang Q. Wang, Tsun‐Kong Sham

Bibliographic record

VenueJournal of Vacuum Science & Technology B Nanotechnology and Microelectronics Materials Processing Measurement and Phenomena · 2016
Typearticle
Languageen
FieldMaterials Science
TopicSilicon Nanostructures and Photoluminescence
Canadian institutionsUniversity of AlbertaWestern University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPhotoluminescenceMaterials sciencePassivationLuminescenceElastic recoil detectionAnnealing (glass)Quantum dotAnalytical Chemistry (journal)SiliconSpectroscopySilicon nitrideHydrogenOptoelectronicsChemical vapor depositionThin filmNanotechnologyChemistryMetallurgy

Abstract

fetched live from OpenAlex

Silicon quantum dots (Si-QDs) of various diameters were formed by annealing of Si-rich silicon nitride (Si3Nx) films synthesized using plasma-enhanced chemical vapor deposition. To investigate the effect of annealing temperature on the luminescence of this system, samples were annealed at temperatures from 400 to 1000 °C. Photoluminescence (PL), x-ray absorption near edge spectroscopy, elastic recoil detection, and Fourier-transform infrared spectroscopy measurements were used for characterization. The authors found that luminescence originated from both quantum confinement effects (QCE) and defects, and that hydrogen passivation affects the PL intensity. For lower annealing temperatures, radiative recombination due to the QCE of the Si-QDs films was observed. For higher annealing temperatures (above 600 °C), desorption of hydrogen from the sample caused the PL intensity to decrease significantly. Si3Nx films with a lower Si content were less sensitive to this reduction in PL intensity after annealing at high temperatures (above 600 °C). Our results emphasize the importance of hydrogenation of the silicon nitride matrix if Si QDs are to be used in optoelectronic devices.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.008
Threshold uncertainty score0.909

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.222
Teacher spread0.212 · 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.

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

Citations3
Published2016
Admission routes2
Has abstractyes

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Same venueJournal of Vacuum Science & Technology B Nanotechnology and Microelectronics Materials Processing Measurement and PhenomenaSame topicSilicon Nanostructures and PhotoluminescenceFrench-language works237,207