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Influence of Heating and Cooling Rates of Post-Implantation Annealing Process on Al-Implanted 4H-SiC Epitaxial Samples

2010· article· en· W2087293422 on OpenAlexaff
Laurent Ottaviani, Stéphane Biondo, Stéphane Morata, Olivier Palais, Thierry Sauvage, Frank Torregrosa

Bibliographic record

VenueMaterials science forum · 2010
Typearticle
Languageen
FieldEngineering
TopicSilicon Carbide Semiconductor Technologies
Canadian institutionsSaint Paul University
Fundersnot available
KeywordsMaterials scienceAnnealing (glass)DopantSheet resistanceSurface roughnessEpitaxyDopant ActivationSilicon carbideAnalytical Chemistry (journal)Ion implantationAluminiumComposite materialMetallurgyOptoelectronicsDopingLayer (electronics)IonChemistry

Abstract

fetched live from OpenAlex

We report on topographical, structural and electrical measurements of aluminum-implanted and annealed 4H-SiC epitaxial samples. The influence of heating-up and cooling-down temperature rates on the SiC surface roughness, the crystal volume reordering and the dopant electrical activation was particularly studied. A higher heating-rate was found to preserve the rms roughness for annealing temperatures lower than 1700°C, and to improve the sheet resistance whatever the annealing temperature due to a better dopant activation (except for 1600°C process, which induced a dark zone in the sample volume). A complete activation was calculated for an annealing at 1700°C during 30 minutes, with a ramp-up at 20°C/s. Rising the cooling-down rate appeared to increase the sheet resistance, probably due to a higher concentration of point defects in the implanted layer.

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)0.001
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 score0.016
Threshold uncertainty score0.501

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.013
GPT teacher head0.269
Teacher spread0.257 · 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
Published2010
Admission routes1
Has abstractyes

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