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Record W1981433737 · doi:10.1109/rfic.2014.6851684

A dual-mode highly efficient 60 GHz power amplifier in 65 nm CMOS

2014· article· en· W1981433737 on OpenAlexaff
Payam M. Farahabadi, Kambiz Moez

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicRadio Frequency Integrated Circuit Design
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsAmplifierPower-added efficiencyRF power amplifierPower bandwidthDirect-coupled amplifierLinear amplifierElectrical engineeringCascodeElectronic engineeringPower supply rejection ratioCMOSImpedance matchingMaterials scienceEngineeringElectrical impedanceOperational amplifier

Abstract

fetched live from OpenAlex

This paper presents a 60 GHz power amplifier utilizing a novel technique to achieve high efficiency at high output power levels. The proposed topology provides the capability of dual mode operation. The output power of a conventional class A power amplifier will be combined with the power provided by an amplifier operating at a different class to achieve higher efficiency at higher output levels. Driver stages to provide high power gain consist of an enhanced cascode stage followed by a common source amplifier with transformer-coupled impedance matching networks. Fabricated in 65 nm CMOS process, the measured gain of the 0.32 mm2 power amplifier is 17.7 dB at 60 GHz with a wide 3dB band width of 12 GHz while consuming 378 mW from a 1.2V supply. A maximum saturated output power of 16.8 dBm is measured with the 14.5% peak power added efficiency at 60 GHz.

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 score0.002
Threshold uncertainty score0.006

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 communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
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 head0.007
GPT teacher head0.208
Teacher spread0.201 · 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

Citations13
Published2014
Admission routes1
Has abstractyes

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