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Record W3094359902 · doi:10.1109/ims30576.2020.9223972

Behavior of lossy Spiral Inductors and Their Applications to the Design of Tunable Band Reject Filters

2020· article· en· W3094359902 on OpenAlexaff
Huayong Jia, Raafat R. Mansour

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicElectromagnetic Compatibility and Noise Suppression
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsInductorCapacitorMaterials scienceFilter (signal processing)Spiral (railway)CapacitanceInductanceOptoelectronicsConductivitySTRIPSParasitic capacitanceElectronic engineeringElectrical engineeringPhysicsEngineeringVoltageElectrodeMechanical engineering

Abstract

fetched live from OpenAlex

In this paper, we use a unique behavior of spiral inductors designed intentionally to have a large parasitic capacitance in the realization of a tunable band reject filter. It is shown that, regardless of the operating frequency, the conductivity of the metal strips forming the inductor has a significant impact on how the spiral inductor behaves as an inductor or a capacitor. The concept is used to demonstrate a band reject filter made out of niobium operating at 4K, whose conductivity can be changed with the application of a dc current. The same concept can be employed using other materials with conductivity that can be controlled either electrically, thermally or optically.

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 score0.015
Threshold uncertainty score0.169

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 head0.028
GPT teacher head0.222
Teacher spread0.194 · 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

Citations2
Published2020
Admission routes1
Has abstractyes

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