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Record W2806622402 · doi:10.1109/lmwc.2018.2835139

On the Second-Harmonic Null in Design Space of Power Amplifiers

2018· article· en· W2806622402 on OpenAlexafffund
Tushar Sharma, Damon G. Holmes, Ramzi Darraji, E. R. Srinidhi, Joseph Staudinger, Jeffrey K. Jones, Fadhel M. Ghannouchi

Bibliographic record

VenueIEEE Microwave and Wireless Components Letters · 2018
Typearticle
Languageen
FieldEngineering
TopicAdvanced Power Amplifier Design
Canadian institutionsUniversity of Calgary
FundersCanada Research ChairsAlberta Innovates - Technology Futures
KeywordsNull (SQL)AmplifierGallium nitrideWaveformHarmonicDegradation (telecommunications)Power (physics)TransistorElectronic engineeringElectrical engineeringPhysicsMaterials scienceEngineeringComputer scienceAcousticsQuantum mechanicsCMOS

Abstract

fetched live from OpenAlex

This letter investigates the source of performance degradation and efficiency null that is traditionally observed during second-harmonic load pull of active power transistors. A theory that explains the performance degradation is presented, resulting in a simplified closed-form equation that predicts the location of performance null for different device peripheries and/or design frequencies. Thereafter, the intrinsic drain waveforms for null and maximum efficiency are analyzed to study potential causes for efficiency degradation. The theory is validated using active load-pull measurements of laterally diffused metal-oxide-semiconductor and gallium nitride active devices for different peripheries (0.5-4.8 mm) at various frequencies of operation (2, 2.6, and 3.5 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)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
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 head0.021
GPT teacher head0.212
Teacher spread0.191 · 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 designSimulation or modeling
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

Citations19
Published2018
Admission routes2
Has abstractyes

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