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Record W2095707859 · doi:10.1109/newcas.2010.5603720

Noise figure optimization of a noise-cancelling wide-band CMOS LNA

2010· article· en· W2095707859 on OpenAlex
Donuwan Navaratne, Aaron J. Beaulieu, Leonid Belostotski

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicRadio Frequency Integrated Circuit Design
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsNoise figureEffective input noise temperatureLow-noise amplifierNoise temperatureNoise-figure meterFlicker noiseNoise measurementCMOSElectronic engineeringNoise (video)Active noise controlBandwidth (computing)Y-factorTransistorComputer scienceElectrical engineeringAmplifierEngineeringNoise reductionTelecommunicationsPhase noiseArtificial intelligence

Abstract

fetched live from OpenAlex

This paper discusses noise figure optimization techniques for a noise-cancelling single-ended-to-differential CMOS low-noise amplifier (LNA). In [1], a low noise figure was obtained through cancellation of the drain noise of the common-gate transistor. Contour plots demonstrate that drain noise cancellation does not guarantee an optimal noise figure. The optimal noise figure is achieved by minimizing the un-cancelled noise contributions as well, and utilizing LC resonance to maintain common-gate transistor drain noise cancellation. An LNA with a 0.8-1.8 GHz bandwidth is designed with this optimization. Simulations show a noise figure between 2.8dB and 1.3dB, with S <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">21</sub> ≈ 13dB over the bandwidth.

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.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.849
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
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.008
GPT teacher head0.193
Teacher spread0.185 · 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