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Millimeter-wave Bandpass Frequency Selective Structure Using Stacked Dielectric Slabs

2020· article· en· W3040750632 on OpenAlexaff
Joseph Botros, Mohamed K. Emara, Rony E. Amaya, Shulabh Gupta

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced Antenna and Metasurface Technologies
Canadian institutionsCarleton University
Fundersnot available
KeywordsBand-pass filterMaterials scienceHFSSCenter frequencyInsertion lossExtremely high frequencyDielectricPrinted circuit boardBroadbandBandwidth (computing)FabricationOptoelectronicsTransmission lineAcousticsElectronic engineeringOpticsElectrical engineeringAntenna (radio)EngineeringTelecommunicationsMicrostrip antennaPhysics

Abstract

fetched live from OpenAlex

A simple stacked dielectric structure is presented to achieve a broadband filtering response at millimeter-wave (mm-wave) frequencies. The structure consists of alternative slabs of different dielectric materials (non-periodic designs possible) to exhibit a bandpass response. The structure is designed using a transmission line model and confirmed with full-wave simulations. Compared to standard printed circuit board (PCB) fabrication, stacked dielectric structures are expected to exhibit better loss performance, due to absence of metallic patterns, and be easier to fabricate at mm-waves because of the absence of constraints from PCB line-width/gap dimension tolerances. An example structure was designed, fabricated, and measured with a center frequency 57 GHz consisting of three dielectric slabs and two air gaps. ANSYS FEM-HFSS simulations of this structure show well-matched bandpass performance, insertion loss of less than 1 dB, and a 3-dB bandwidth of 4.5 GHz. Measurement of the fabricated structure shows excellent performance and correlation with 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 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 score0.125
Threshold uncertainty score0.753

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.030
GPT teacher head0.222
Teacher spread0.191 · 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

Citations1
Published2020
Admission routes1
Has abstractyes

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