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Record W2089256951 · doi:10.1155/2015/736291

Fabrication and Characterization of a W-Band Cylindrical Dielectric Resonator Antenna-Coupled Niobium Microbolometer

2015· article· en· W2089256951 on OpenAlexaff
Mohamed Abdel‐Rahman, M. Kamran Saleem, Najeeb Al‐Khalli, Nacer Debbar, Majeed A. S. Alkanhal, Abdel‐Razik Sebak

Bibliographic record

VenueInternational Journal of Antennas and Propagation · 2015
Typearticle
Languageen
FieldEngineering
TopicAdvanced Antenna and Metasurface Technologies
Canadian institutionsConcordia University
FundersKing Abdulaziz City for Science and Technology
KeywordsMicrobolometerMaterials scienceAntenna (radio)ResponsivityOptoelectronicsFabricationDetectorCoplanar waveguideResonatorTerahertz radiationPermittivityDielectricOpticsBolometerMicrowaveElectrical engineeringPhysicsTelecommunicationsEngineeringPhotodetector

Abstract

fetched live from OpenAlex

We report on the fabrication and characterization of a novel antenna-coupled detector configuration for detection at 94 GHz, a coplanar waveguide- (CPW-) fed, slot-excited twin dielectric resonator antenna- (DRA-) coupled niobium (Nb) microbolometer. The antenna is based on two low permittivity cylindrical dielectric resonators (CDRs) excited by rectangular slots placed below the CDRs. The antenna resonant currents are fed to an Nb microbolometer by the means of a CPW feed. The ceramic DRA structure is manufactured using a novel fabrication process that enables patterning an SU-8–Alumina (Al2O3) nanopowder composite using conventional photolithography. The detector measured a voltage responsivity of 0.181 V/W at a modulation frequency of 150 Hz. The detector measured a time constant of 1.94 μs. The antenna radiation pattern of the developed detector configuration was measured and shows a good agreement with the simulation.

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 score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.015
GPT teacher head0.239
Teacher spread0.223 · 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

Citations1
Published2015
Admission routes1
Has abstractyes

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