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Record W4319990520 · doi:10.1109/tcsi.2023.3240681

A 16.5-31 GHz Area-Efficient Tapered Tunable Transmission Line Phase Shifter

2023· article· en· W4319990520 on OpenAlexafffund
Ehsan Khodarahmi, Mohammad Elmi, I.M. Filanovsky, Kambiz Moez

Bibliographic record

VenueIEEE Transactions on Circuits and Systems I Regular Papers · 2023
Typearticle
Languageen
FieldEngineering
TopicRadio Frequency Integrated Circuit Design
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaAlberta Innovates
KeywordsPhase shift moduleInsertion lossMaterials scienceCMOSTransmission linePhase (matter)Return lossOptoelectronicsElectronic engineeringElectrical engineeringEngineeringPhysics

Abstract

fetched live from OpenAlex

This paper presents a Tapered Tunable Transmission Line (Tapered TTL) phase shifter that achieves a higher area efficiency than conventional Tunable Transmission Line (TTL) phase shifters while maintaining the same phase shift range with similar insertion losses. A systematic methodology is provided for the optimum design of the proposed phase shifter to maximize its area efficiency while providing the desired phase shift range and satisfying the maximum allowed input/output return and insertion losses. To verify the efficacy of the proposed solution, an eleven-cell phase shifter is fabricated in a standard 65-nm Complementary Metal–Oxide–Semiconductor (CMOS) technology and the measurement results are reported. The fabricated circuit provides a 180-degree phase shift over the frequency range of 16.5 to 31 GHz with an average insertion loss of 7.2 dB. The proposed design presents a 25 percent reduction in the chip area per unit delay in comparison to the conventional design with the same average insertion loss.

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 science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.026
GPT teacher head0.232
Teacher spread0.206 · 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

Citations7
Published2023
Admission routes2
Has abstractyes

Explore more

Same venueIEEE Transactions on Circuits and Systems I Regular PapersSame topicRadio Frequency Integrated Circuit DesignFrench-language works237,207