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Record W2120232294 · doi:10.1109/ccece.2006.277652

24 GHz Low-Noise Amplifiers using High Q Series-Stub Transmission Lines in 0.18μm CMOS

2006· article· en· W2120232294 on OpenAlexafffund
Dustin Dunwell, Brian Frank

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicRadio Frequency Integrated Circuit Design
Canadian institutionsQueen's University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsNoise figureLow-noise amplifierAmplifierElectronic engineeringCMOSCascadeElectrical engineeringEffective input noise temperatureElectric power transmissionTransistorEngineeringComputer science

Abstract

fetched live from OpenAlex

Single-ended and differential low-noise amplifiers (LNAs), designed in 0.18 μm CMOS for operation at 24 GHz, are introduced in this paper. Novel, high-Q series-stub transmission lines (SSTLs) are used in the matching networks of both LNAs. This SSTL structure shows a notable Q factor improvement over the commonly used spiral inductor, which helps to minimize the losses and noise produced in the LNA matching networks, making these topologies suitable for sensitive receiver front ends. The single-ended amplifier uses two cascade stages to help improve gain and reverse isolation when compared to common source stages. Results show that the cascade LNA is able to produce an excellent compromise between ease of design, gain and noise. After optimizing transistor sizes to produce minimum noise, the single-ended amplifier produces a simulated noise figure of only 4.9 dB and a gain of 17.4 dB. The differential LNA uses two capacitive neutralized stages to improve reverse isolation. Chip production has been delayed by the foundry, but simulated results of the differential LNA show an excellent noise figure of 4.2 and a gain of 12.3 dB

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.289
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
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.023
GPT teacher head0.226
Teacher spread0.203 · 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 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

Citations5
Published2006
Admission routes2
Has abstractyes

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