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Record W1835309263 · doi:10.1109/newcas.2004.1359089

A novel gain boosting technique for design of low power narrow-band RFCMOS LNAs

2004· article· en· W1835309263 on OpenAlex
S. Asgaran, M. Jamal Deen

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

VenueThe 2nd Annual IEEE Northeast Workshop on Circuits and Systems, 2004. NEWCAS 2004. · 2004
Typearticle
Languageen
FieldEngineering
TopicRadio Frequency Integrated Circuit Design
Canadian institutionsMcMaster University
Fundersnot available
KeywordsCascodeNoise figureAmplifierPower gainElectronic engineeringBoosting (machine learning)High-gain antennaElectrical engineeringLow-noise amplifierNegative resistancePower consumptionPower (physics)EngineeringComputer sciencePhysicsCMOSVoltage

Abstract

fetched live from OpenAlex

A novel gain boosting technique to increase the power gain of narrow-band RF low noise amplifiers is developed and verified. This technique is based on utilizing a negative resistance in the cascode configuration that is the most common configuration in designing LNAs. Using this technique, a LNA, called the negative resistance cascode LNA, was designed. The LNA operates at 7 GHz and consumes only 7.2 mW of power. It is shown that the negative resistance technique provides an additional gain of 10 dB at the cost of only 0.6 mW of extra power consumption.

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)0.002
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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.955
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.035
GPT teacher head0.248
Teacher spread0.213 · 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