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Record W2139288041 · doi:10.1109/tmtt.2004.835981

Physical Modeling and Frequency-Response Analysis of a High-Temperature Superconducting Terahertz Photomixer

2004· article· en· W2139288041 on OpenAlexaff
A. Hamed Majedi, D. Saeedkia, S.K. Chaudhuri, Safieddin Safavi‐Naeini

Bibliographic record

VenueIEEE Transactions on Microwave Theory and Techniques · 2004
Typearticle
Languageen
FieldEngineering
TopicTerahertz technology and applications
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsTerahertz radiationPhotomixingTransmission lineHeterodyne (poetry)Materials scienceOptoelectronicsFrequency responseOpticsTerahertz gapBiasingElectric power transmissionPhysicsVoltageLaserFar-infrared laserElectrical engineeringAcousticsEngineeringTerahertz metamaterials

Abstract

fetched live from OpenAlex

A physical modeling and a rigorous theoretical analysis consisting of the two-temperature model and the generalized transmission-line model is used to evaluate a photo-induced terahertz continuous-wave voltage from a dc current-biased high-temperature superconducting (HTS) transmission line by optical heterodyne photomixing. The electrical and optical frequency-response analyses show the amplitude of this voltage increases with increasing beat frequency and decreasing optical frequency of laser beams. Its maximum frequency is found to be limited by the gap frequency of the HTS material, which is consistent with the available experiments reported in the literature. The developed model along with our numerical simulation reveal the ways to produce a coherent traveling-wave and high-power terahertz signal with proper choice of an HTS material, bias condition, geometrical configuration, and parameters of the transmission line and characteristics of two laser beams. This HTS photomixer can play an important role in terahertz transceivers as broadly tunable local oscillators with low-noise/low-power consumption characteristics.

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 score0.249
Threshold uncertainty score0.685

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 head0.006
GPT teacher head0.224
Teacher spread0.217 · 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

Citations10
Published2004
Admission routes1
Has abstractyes

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