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Record W2020634000 · doi:10.1063/1.2113409

Solid-phase epitaxial regrowth of amorphous layers in Si(100) created by low-energy, high-fluence phosphorus implantation

2005· article· en· W2020634000 on OpenAlexaff
S. Ruffell, I. V. Mitchell, P. J. Simpson

Bibliographic record

VenueJournal of Applied Physics · 2005
Typearticle
Languageen
FieldEngineering
TopicIon-surface interactions and analysis
Canadian institutionsWestern University
Fundersnot available
KeywordsAnnealing (glass)EpitaxyMaterials scienceRecrystallization (geology)Amorphous solidIon implantationTransmission electron microscopyAnalytical Chemistry (journal)IonFluenceCrystallographyChemistryNanotechnologyMetallurgyLayer (electronics)

Abstract

fetched live from OpenAlex

Medium energy ion scattering has been used to study the kinetics of solid-phase epitaxial regrowth (SPEG) of ultrathin amorphous layers formed by room-temperature implantation of 5keV energy phosphorus ions into Si (100). The implants create P distributions with peak concentrations up to ∼7×1021cm−3. SPEG has been driven by rapid thermal annealing, 475°C⩽TA⩽600°C, for times up to 2000s. At each temperature, the regrowth velocity is enhanced in the early stages due to the presence of phosphorus but then slows sharply to a value more than an order of magnitude below the intrinsic rate. The critical phosphorus concentration at the transition point for TA=475°C regrowth is ∼6×1020cm−3 and increases steadily with anneal temperature. Time-of-flight secondary ion mass spectroscopy profiles confirm the onset of phosphorus push out, where the advancing recrystallization front enters the transition region. Supplementary cross-sectional transmission electron microscopy evidence confirms the existence of a local strain field.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.001

Distilled classifier scores by category (both heads)

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 head0.005
GPT teacher head0.253
Teacher spread0.248 · 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 source (direct Gemma or distilled Codex), 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

Citations11
Published2005
Admission routes1
Has abstractyes

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