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Implantation of Nitrogen Atoms in 4H-SiC Epitaxial Layers: A Comparison between Standard and Plasma Immersion Processes

2011· article· en· W2035948999 on OpenAlexaff
Laurent Ottaviani, Stéphane Biondo, M. Kazan, Olivier Palais, Julian Duchaine, F. Milési, Rachid Daineche, Blandine Courtois, Frank Torregrosa

Bibliographic record

VenueAdvanced materials research · 2011
Typearticle
Languageen
FieldEngineering
TopicSilicon Carbide Semiconductor Technologies
Canadian institutionsSaint Paul University
Fundersnot available
KeywordsMaterials scienceAnnealing (glass)Silicon carbideIon implantationPlasma-immersion ion implantationEpitaxyNitrogenPlasmaSheet resistanceAnalytical Chemistry (journal)DopantCarbideResistive touchscreenIon beamIonMetallurgyDopingOptoelectronicsComposite materialChemistry

Abstract

fetched live from OpenAlex

This paper focuses on the formation of thin n+p junctions in p-type Silicon Carbide (SiC) epitaxial layers using two kinds of Nitrogen implantations. The standard beam ion implantations and PULSIONTM processes were performed at two distinct energies (700 eV and 7 keV), and the subsequent annealing was held at 1600°C in a resistive furnace specifically adapted to SiC material. No measurable electrical activity was obtained for both implantations performed at 700 eV, due to some outdiffusion of N dopants during the annealing despite a low surface roughness (rms ~ 1.4 nm) and no residual damage detected by RBS/C. A higher sheet resistance was measured in plasma-implanted samples at 7 keV (in comparison with beam-line implanted samples), which is partly related to N outdiffusion. The profiles of N atoms beam-implanted at 7 keV are not affected by the annealing. The corresponding electrical activation is fully completed.

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 score0.022
Threshold uncertainty score0.523

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 head0.081
GPT teacher head0.344
Teacher spread0.263 · 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

Citations1
Published2011
Admission routes1
Has abstractyes

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