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Record W2315943285 · doi:10.1021/nl202599q

Mapping the “Forbidden” Transverse-Optical Phonon in Single Strained Silicon (100) Nanowire

2011· article· en· W2315943285 on OpenAlexaff
Alvarado Tarun, Norihiko Hayazawa, Hidekazu Ishitobi, Satoshi Kawata, Manfred Reiche, Oussama Moutanabbir

Bibliographic record

VenueNano Letters · 2011
Typearticle
Languageen
FieldEngineering
TopicNanowire Synthesis and Applications
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsNanowirePhononMaterials scienceRaman spectroscopySiliconTransverse planeOptoelectronicsEtching (microfabrication)OpticsNanotechnologyCondensed matter physicsPhysics

Abstract

fetched live from OpenAlex

The accurate manipulation of strain in silicon nanowires can unveil new fundamental properties and enable novel or enhanced functionalities. To exploit these potentialities, it is essential to overcome major challenges at the fabrication and characterization levels. With this perspective, we have investigated the strain behavior in nanowires fabricated by patterning and etching of 15 nm thick tensile strained silicon (100) membranes. To this end, we have developed a method to excite the "forbidden" transverse-optical (TO) phonons in single tensile strained silicon nanowires using high-resolution polarized Raman spectroscopy. Detecting this phonon is critical for precise analysis of strain in nanoscale systems. The intensity of the measured Raman spectra is analyzed based on three-dimensional field distribution of radial, azimuthal, and linear polarizations focused by a high numerical aperture lens. The effects of sample geometry on the sensitivity of TO measurement are addressed. A significantly higher sensitivity is demonstrated for nanowires as compared to thin layers. In-plane and out-of-plane strain profiles in single nanowires are obtained through the simultaneous probe of local TO and longitudinal-optical (LO) phonons. New insights into strained nanowires mechanical properties are inferred from the measured strain profiles.

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 score0.042
Threshold uncertainty score0.555

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 head0.037
GPT teacher head0.196
Teacher spread0.159 · 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

Citations37
Published2011
Admission routes1
Has abstractyes

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