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Record W2766562298 · doi:10.1364/optica.4.001358

Solid-state-biased coherent detection of ultra-broadband terahertz pulses

2017· article· en· W2766562298 on OpenAlexaff
Alessandro Tomasino, Anna Mazhorova, Matteo Clerici, Marco Peccianti, Sze-Phing Ho, Y. Jestin, Alessia Pasquazi, Andrey Markov, Xin Jin, Riccardo Piccoli, S. Delprat, Mohamed Chaker, Alessandro Busacca, Jalil Ali, Luca Razzari, Roberto Morandotti

Bibliographic record

VenueOptica · 2017
Typearticle
Languageen
FieldEngineering
TopicTerahertz technology and applications
Canadian institutionsInstitut National de la Recherche Scientifique
FundersEngineering and Physical Sciences Research Council
KeywordsTerahertz radiationUltrashort pulseBroadbandOptoelectronicsLaserMaterials scienceDetectorOpticsElectronicsMicrofabricationPhysicsElectrical engineeringEngineeringFabrication

Abstract

fetched live from OpenAlex

Significant progress in nonlinear and ultrafast optics has recently opened new and exciting opportunities for terahertz (THz) science and technology, which require the development of reliable THz sources, detectors, and supporting devices. In this work, we demonstrate the first solid-state technique for the coherent detection of ultra-broadband THz pulses (0.1-10 THz), relying on the electric-field-induced second-harmonic generation in a thin layer of ultraviolet fused silica. The proposed CMOS-compatible devices, which can be realized with standard microfabrication techniques, allow us to perform ultra-broadband detection with a high dynamic range by employing probe laser powers and bias voltages much lower than those used in gas-based techniques. Eventually, this may pave the way for the use of high-repetition-rate ultrafast lasers and commercially available electronics for the coherent detection of ultrashort THz pulses.

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)0.001
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
Metaresearch0.0000.001
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 head0.011
GPT teacher head0.263
Teacher spread0.252 · 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

Citations44
Published2017
Admission routes1
Has abstractyes

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