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Record W2086059412 · doi:10.1063/1.2189115

Influence of growth temperature on order within silicon films grown by ultrahigh-vacuum evaporation on silica

2006· article· en· W2086059412 on OpenAlexaff
Li‐Lin Tay, D. J. Lockwood, J.‐M. Baribeau, Mario Noël, Joanne C. Zwinkels, Farida Orapunt, Stephen K. O’Leary

Bibliographic record

VenueApplied Physics Letters · 2006
Typearticle
Languageen
FieldEngineering
TopicThin-Film Transistor Technologies
Canadian institutionsUniversity of ReginaNational Research Council CanadaInstitute for Microstructural Sciences
Fundersnot available
KeywordsRaman spectroscopyVacuum evaporationSiliconEvaporationMaterials scienceAbsorption (acoustics)Thin filmPhononTransverse planeOrder (exchange)Analytical Chemistry (journal)OpticsCondensed matter physicsChemistryNanotechnologyOptoelectronicsComposite materialPhysicsThermodynamics

Abstract

fetched live from OpenAlex

We study the role that the growth temperature plays in determining the amount of order present within silicon films deposited on fused silica substrates through ultrahigh-vacuum evaporation at growth temperatures ranging from 98 to 572°C. Through measurements of the Raman and optical absorption spectra, we quantitatively determine how the growth temperature influences the order present within 11 such films. We employ three disparate measures of order for the purposes of this study: the breadth of the transverse-optic phonon Raman peak, this being related to the amount of short-range order present; the area under the transverse-acoustic Raman peak divided by the area under the corresponding transverse-optic peak, this being related to the amount of intermediate-range order present; and the breadth of the optical absorption tail, which is a general measure of the overall amount of order present. All three measures of order indicate a dramatic increase in the amount of order present for growth temperatures above 400°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 categoriesMeta-epidemiology (narrow)
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 score0.317
Threshold uncertainty score1.000

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 head0.168
Teacher spread0.164 · 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.

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

Citations16
Published2006
Admission routes1
Has abstractyes

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