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Record W1859715300 · doi:10.1139/cjp-2013-0555

Influence of hydrogen concentration on void-related microstructure in low hydrogen amorphous and crystalline silicon materials

2013· article· en· W1859715300 on OpenAlexvenueno aff
W. Beyer, U. Breuer, R. Carius, W. Hilgers, D. Lennartz, Florian Maier, N. H. Nickel, F. Pennartz, P. Prunici, U. Zastrow

Bibliographic record

VenueCanadian Journal of Physics · 2013
Typearticle
Languageen
FieldEngineering
TopicThin-Film Transistor Technologies
Canadian institutionsnot available
Fundersnot available
KeywordsAmorphous solidHydrogenMicrostructureAnnealing (glass)Amorphous siliconMaterials scienceCrystalline siliconHeliumSiliconAnalytical Chemistry (journal)Void (composites)CrystallographyAtomic physicsComposite materialChemistryMetallurgyPhysics

Abstract

fetched live from OpenAlex

The influence of implanted hydrogen (up to a concentration level of 3 at. %) on the microstructure of silicon (Si) materials is investigated by Fourier transform infrared spectroscopy as well as by effusion of hydrogen and of (low dose) implanted helium. Three materials of low original hydrogen concentration, crystalline Si, electron beam evaporated amorphous Si, and plasma-deposited hydrogenated amorphous Si (using high deposition temperature) were investigated. Significant differences between crystalline and amorphous materials were observed. In crystalline Si, implanted hydrogen is found to generate multivacancies that trap diffusing helium while this is not the case in amorphous Si. Accordingly, cavities where hydrogen is located in amorphous Si must be smaller than divacancies. Those cavities in amorphous Si, present from the growth process, that trap helium tend to disappear when the implanted hydrogen concentration increases. Annealing of the materials up to temperatures of 575 °C was also studied. No significant change in the density of voids (trapping helium) occur but in case of crystalline Si the bonding sites of hydrogen as well as the diffusion paths of helium change.

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 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 score0.193
Threshold uncertainty score0.516

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.003
GPT teacher head0.164
Teacher spread0.160 · 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

Citations5
Published2013
Admission routes1
Has abstractyes

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