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Record W2045087601 · doi:10.1109/jssc.2004.835834

A capacitance-compensation technique for improved linearity in CMOS class-AB power amplifiers

2004· article· en· W2045087601 on OpenAlexaff
Chengzhou Wang, Mani Vaidyanathan, L.E. Larson

Bibliographic record

VenueIEEE Journal of Solid-State Circuits · 2004
Typearticle
Languageen
FieldEngineering
TopicAdvanced Power Amplifier Design
Canadian institutionsUniversity of Alberta
FundersUniversity of California, San Diego
KeywordsAmplifierIntermodulationLinearityPMOS logicCMOSNMOS logicElectrical engineeringElectronic engineeringParasitic capacitanceCapacitanceTransistorCapacitorAdjacent channelMaterials scienceEngineeringPhysicsVoltage

Abstract

fetched live from OpenAlex

A nonlinear capacitance-compensation technique is developed to help improve the linearity of CMOS class-AB power amplifiers. The method involves placing a PMOS device alongside the NMOS device that works as the amplifying unit, such that the overall capacitance seen at the amplifier input is a constant, thus improving linearity. The technique is developed with the help of computer simulations and Volterra analysis. A prototype two-stage amplifier employing the scheme is fabricated using a 0.5-/spl mu/m CMOS process, and the measurements show that an improvement of approximately 8 dB in both two-tone intermodulation distortion (IM3) and adjacent-channel leakage power (ACP1) is obtained for a wide range of output power. The linearized amplifier exhibits an ACP1 of -35 dBc at the designed output power of 24 dBm, with a power-added efficiency of 29% and a gain of 23.9 dB, demonstrating the potential utility of the design approach for 3GPP WCDMA applications.

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)0.001
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: none
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0020.000

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 head0.020
GPT teacher head0.266
Teacher spread0.246 · 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

Citations182
Published2004
Admission routes1
Has abstractyes

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