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Record W4391777511 · doi:10.1063/5.0187087

Probing the infrared properties of a <i>p</i>-doped Ge0.938Sn0.062 thin film via polarization-dependent FTIR spectroscopy

2024· article· en· W4391777511 on OpenAlexafffund
B. N. Carnio, Basem Y. Shahriar, Anis Attiaoui, Mahmoud R. M. Atalla, Simone Assali, Oussama Moutanabbir, A. Y. Elezzabi

Bibliographic record

VenueApplied Physics Letters · 2024
Typearticle
Languageen
FieldEngineering
TopicPhotonic and Optical Devices
Canadian institutionsPolytechnique MontréalUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaDefence Research and Development Canada
KeywordsPermittivityThin filmDopingMaterials scienceFourier transform infrared spectroscopyFresnel equationsPolarization (electrochemistry)InfraredSemiconductorAttenuated total reflectionSpectroscopyOpticsInfrared spectroscopyBand gapRelative permittivityOptoelectronicsAnalytical Chemistry (journal)Refractive indexChemistryNanotechnologyDielectricPhysical chemistryPhysics

Abstract

fetched live from OpenAlex

The complex relative permittivity of doped Ge1−xSnx thin films (realized using state-of-the-art growth techniques) are obtained by devising a methodology based upon polarization-dependent reflection measurements along with multi-layer Fresnel reflection equations. The developed approach is implemented to acquire the complex relative permittivity of a 170-nm-thick Ge1−xSnx film exhibiting a hole carrier concentration of 3.3 × 1019 cm−3 and x = 6.2%, with this Sn composition suggesting the film is on the cusp of exhibiting a direct bandgap. The investigation conducted on this thin film as well as the developed methodology are expected to further establish Ge1−xSnx as the primary semiconductor for on-chip light emission and sensing devices.

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 score0.003

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)0.0010.000

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 head0.008
GPT teacher head0.188
Teacher spread0.181 · 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

Citations0
Published2024
Admission routes2
Has abstractyes

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