MétaCan
Menu
Back to cohort
Record W2914186718 · doi:10.1109/lmwc.2019.2892028

Design Considerations for Image-Rejection Enhancement of Quadrature Mixers

2019· article· en· W2914186718 on OpenAlexaff
Fang Zhu, Kuangda Wang, Ke Wu

Bibliographic record

VenueIEEE Microwave and Wireless Components Letters · 2019
Typearticle
Languageen
FieldEngineering
TopicRadio Frequency Integrated Circuit Design
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsImage responseHarmonic mixerQuadrature (astronomy)Impedance matchingElectrical impedanceWidebandElectronic engineeringElectronic mixerLocal oscillatorBandwidth (computing)CMOSPhysicsElectrical engineeringEngineeringTopology (electrical circuits)Intermediate frequencyRadio frequencyTelecommunications

Abstract

fetched live from OpenAlex

In this letter, the influences of local oscillator (LO) reflections of the in-phase (I) and quadrature-phase (Q) mixers and limited isolations between the I/Q LO ports on the image-rejection ratio (IRR) performances of quadrature mixers are investigated and analyzed for the first time. The analysis reveals that, in addition to generating well-balanced quadrature LO signals, a good LO impedance matching and/or high electrical isolation between the LO ports of the I and Q mixers are required to obtain a high IRR over a wide bandwidth. To verify the analysis, a quadrature mixer with carefully designed wideband LC-ladder matching networks for the I/Q LO ports is implemented in a 65-nm CMOS technology. Under 1-V supply voltage and 3-dBm LO power, the proposed quadrature mixer exhibits 3.3±1.5 dB conversion gain and better than 30-dBc IRR across the 20-25-GHz band.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.020
GPT teacher head0.212
Teacher spread0.192 · 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 designSimulation or modeling
Domainnot available
GenreMethods

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

Citations11
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueIEEE Microwave and Wireless Components LettersSame topicRadio Frequency Integrated Circuit DesignFrench-language works237,207