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Record W3088676073 · doi:10.1364/oe.403702

Terahertz low-pass filter based on cascaded resonators formed by CPS bending on a thin membrane

2020· article· en· W3088676073 on OpenAlexafffund
Levi Smith, Vahid Shiran, Thomas E. Darcie

Bibliographic record

VenueOptics Express · 2020
Typearticle
Languageen
FieldEngineering
TopicMicrowave Engineering and Waveguides
Canadian institutionsUniversity of Victoria
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsStopbandResonatorOpticsPassbandCutoff frequencyMaterials scienceTransmission lineLow-pass filterHFSSStriplineBendingBand-pass filterFrequency responseTerahertz radiationRippleElliptic filterFilter (signal processing)AcousticsPrototype filterPhysicsTelecommunicationsComputer scienceElectrical engineeringEngineering

Abstract

fetched live from OpenAlex

A membrane-based coplanar-stripline (CPS) transmission-line platform has recently enabled implementation of diverse THz system-on-chip (TSoC) components. In this paper, we demonstrate an elliptic-function THz low-pass filter (TLPF) using cascaded λ/4 resonators between the right-angle bending of a CPS transmission line defined on a 1 μm-thin membrane. We investigated the effect of bending the CPS transmission line with different angles that introduces a frequency response similar to a simple LC low-pass filter (LPF) and facilitates the design of a desired roll-off performance using traditional methods. ANSYS HFSS was used to provide a full-wave analysis and characterize the effective parameters of the TLPF with a designed cutoff-frequency around 0.6 THz. Using 7 sections of right-angle CPS bending with total length 1.4 mm, we demonstrate experimentally an elliptic-function TLPF characterized by a low-ripple at passband, a roll-off transition with zero transmission near the cutoff frequency and a wide stopband with -60 dB rejection.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.147
Threshold uncertainty score1.000

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 head0.012
GPT teacher head0.203
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.

Study designSimulation or modeling
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
Published2020
Admission routes2
Has abstractyes

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