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Record W4382136790 · doi:10.3390/electronics12132784

Analytical Approach to Improve the Performance of a Fully Integrated Class-F Power Amplifier with 0.13 µm BiCMOS Technology Using Drain–Bulk Capacitor Modulation

2023· article· en· W4382136790 on OpenAlexaff
Mohamed Trabelsi, Ali Bououden, M.C.E. Yagoub

Bibliographic record

VenueElectronics · 2023
Typearticle
Languageen
FieldEngineering
TopicAdvanced Power Amplifier Design
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsAmplifierElectrical engineeringCapacitorPower-added efficiencyRF power amplifierPower factorCascodeTransistorElectronic engineeringPower gainPower (physics)BiCMOSMiniaturizationVoltageCMOSEngineeringPhysics

Abstract

fetched live from OpenAlex

This article reports a novel technique based on drain–bulk capacitor modulation to improve the performance design of a class-F power amplifier (PA) used in low-power transceivers based on the I-Q amplitude modulation technique considering linearity–efficiency–miniaturization trade-offs. This idea is carried out by implementing a tuned capacitor in parallel with a cascode transistor on the output of the power stage to enhance the shape of the voltage–current amplitudes of the class-F PA by creating a new harmonic current component. Simulated results were obtained for the power back-off region of the proposed configuration, with an output power, power gain and power-added efficiency of 8 dBm (+ 5 dBm)B, 19 dB (+ 5 dB)B and 45% (+ 5% to 10%)B, respectively. In addition, post-layout simulations revealed a similar level of output power, a power gain of a 20 dB and a 28% power-added efficiency for an added capacitance equal to 1.3 pF. Class-F PA is implemented on a 732×605 μm2 chip’s surface. (B: indicates the improved values in the power back-off region).

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 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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.250
Threshold uncertainty score0.751

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.011
GPT teacher head0.225
Teacher spread0.214 · 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 teacher head, 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

Citations1
Published2023
Admission routes1
Has abstractyes

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