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Record W2897418325 · doi:10.1364/ol.43.004997

Terahertz microscopy assisted by semiconductor nonlinearities

2018· article· en· W2897418325 on OpenAlexafffund
F. Blanchard, X. Chai, Tomoko Tanaka, Takashi Arikawa, T. Ozaki, Roberto Morandotti, Kōichiro Tanaka

Bibliographic record

VenueOptics Letters · 2018
Typearticle
Languageen
FieldEngineering
TopicTerahertz technology and applications
Canadian institutionsInstitut National de la Recherche ScientifiqueÉcole de Technologie Supérieure
FundersFonds de recherche du Québec – Nature et technologiesNatural Sciences and Engineering Research Council of Canada
KeywordsOpticsTerahertz radiationSemiconductorMaterials scienceMicroscopyTerahertz spectroscopy and technologyOptoelectronicsNonlinear opticsNear-field scanning optical microscopeOptical microscopeLaserPhysicsScanning electron microscope

Abstract

fetched live from OpenAlex

Terahertz (THz) imaging is currently based on linear effects, but there is great interest on how nonlinear effects induced by terahertz radiation could be exploited to provide extra information that is unobtainable by conventional imaging schemes. In particular, at field strengths on the order of 100  kV cm−1 to 1  MV cm−1, transmission properties inside semiconductor materials are largely affected at the picosecond time-scale, which raise the prospect of interesting nonlinear imaging applications at THz frequencies. Here, we experimentally investigate a method to map the two-dimensional nonlinear near-field distribution of an intense THz pulse passing through a thin film-doped semiconductor. By inserting a metamaterial structure between the electro-optic sensor and the doped film, the nonlinear near-field dynamics shows a different and enhanced contrast of the sample when compared to its linear counterpart.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.427
Threshold uncertainty score0.505

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 head0.007
GPT teacher head0.223
Teacher spread0.216 · 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

Citations13
Published2018
Admission routes2
Has abstractyes

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