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Record W2171303456 · doi:10.1109/tmtt.2003.815890

High-performance microwave coplanar bandpass and bandstop filters on Si substrates

2003· article· en· W2171303456 on OpenAlexaff
K.T. Chan, Albert Chin, Mingfu Li, Dim‐Lee Kwong, S. P. McAlister, D.S. Duh, Wuu‐Jyh Lin, Chi‐Yang Chang

Bibliographic record

VenueIEEE Transactions on Microwave Theory and Techniques · 2003
Typearticle
Languageen
FieldEngineering
TopicFull-Duplex Wireless Communications
Canadian institutionsNational Research Council Canada
FundersUniversity of Illinois at Urbana-ChampaignUniversity of Michigan
KeywordsBand-pass filterMaterials scienceStub (electronics)ResonatorCoplanar waveguideInsertion lossMicrowaveBand-stop filterOptoelectronicsGround planePassbandElectrical impedanceEquivalent circuitTransmission lineElectronic engineeringElectrical engineeringFilter (signal processing)Low-pass filterTelecommunicationsEngineering

Abstract

fetched live from OpenAlex

High-performance bandpass and bandstop microwave coplanar filters, which operate from 22 to 91 GHz, have been fabricated on Si substrates. This was achieved using an optimized proton implantation process that converts the standard low-resistivity (/spl sim/10 /spl Omega//spl middot/cm) Si to a semi-insulating state. The bandpass filters consist of coupled lines to form a series resonator, while the bandstop filter was designed in a double-folded short-end stub structure. For the bandpass filters at 40 and 91 GHz, low insertion loss was measured, close to electromagnetic simulation values. We also fabricated excellent bandstop filters with very low transmission loss of /spl sim/1 dB and deep band rejection at both 22 and 50 GHz. The good filter performance was confirmed by the higher substrate impedance to ground, which was extracted from the well-matched S-parameter equivalent-circuit data.

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)0.000
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
Metaresearch0.0000.000
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)0.0010.001

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 head0.008
GPT teacher head0.202
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 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

Citations17
Published2003
Admission routes1
Has abstractyes

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