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Record W2082677378 · doi:10.1063/1.1897826

Impact of growth conditions on vacancy-type defects in silicon–germanium structures grown by molecular-beam epitaxy

2005· article· en· W2082677378 on OpenAlexaff
Kareem M. Shoukri, Yaser M. Haddara, Andrew P. Knights, P. G. Coleman

Bibliographic record

VenueApplied Physics Letters · 2005
Typearticle
Languageen
FieldEngineering
TopicSemiconductor materials and devices
Canadian institutionsMcMaster University
Fundersnot available
KeywordsMolecular beam epitaxyVacancy defectMaterials scienceSiliconGermaniumCrystallographic defectLayer (electronics)EpitaxyOptoelectronicsCrystallographyAnalytical Chemistry (journal)NanotechnologyChemistry

Abstract

fetched live from OpenAlex

Silicon–germanium layers of either 200nm or 250nm have been grown via molecular-beam epitaxy (MBE) on p-type (001) silicon substrates. Each sample was prepared using a unique combination of buffer-layer type, buffer-layer growth temperature, and layer Ge content. Vacancy-type defects have been identified using beam-based positron annihilation. These results, combined with those from previous work, indicate the size and concentration of defects in MBE grown SiGe layers depend strongly upon the buffer-layer growth temperature (T). For T>500°C vacancy point defect concentrations are below the detectable limit of the measurement. As T is decreased to 300°C, vacancy clusters form in the buffer layer and point defects appear in the SiGe film, even for a SiGe growth temperature of 500°C.

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 score0.040
Threshold uncertainty score0.886

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 head0.006
GPT teacher head0.223
Teacher spread0.217 · 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

Citations11
Published2005
Admission routes1
Has abstractyes

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