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Record W3023085172 · doi:10.1109/lmwc.2020.2988347

A Broadband Millimeter-Wave Continuous-Mode Class-F Power Amplifier Based on the Deembedded Transistor Model

2020· article· en· W3023085172 on OpenAlexaff
Dehan Wang, Wenhua Chen, Xiaofan Chen, Fadhel M. Ghannouchi, Zhenghe Feng

Bibliographic record

VenueIEEE Microwave and Wireless Components Letters · 2020
Typearticle
Languageen
FieldEngineering
TopicAdvanced Power Amplifier Design
Canadian institutionsUniversity of Calgary
FundersNational Key Research and Development Program of China
KeywordsAmplifierExtremely high frequencyTransistorBroadbandBandwidth (computing)Electrical engineeringPhysicsWidebandBiCMOSHeterojunction bipolar transistorHarmonicsHigh-electron-mobility transistorContinuous waveElectronic engineeringOptoelectronicsTopology (electrical circuits)Computer scienceEngineeringTelecommunicationsBipolar junction transistorCMOSOptics

Abstract

fetched live from OpenAlex

In this letter, a broadband millimeter-wave (mm-wave) continuous-mode Class-F power amplifier (PA) based on the deembedded transistor model is proposed for the fifth-generation (5G) applications. To design the output matching network at the intrinsic current-generator plane, an S-parameter fitting method is proposed to extract the parasitic parameters of nonpackaged transistors over a wide frequency range. The PA is implemented in the 0.13-μm SiGe BiCMOS process, which occupies only 0.42 mm2. By controlling the even and odd harmonics impedance, the PA achieves a 78.2% -3-dB small-signal gain fractional bandwidth from 21.1 to 48.2 GHz. The -1-dB Psatbandwidth covers 21-43 GHz (68.8% fractional bandwidth) and the saturated output powers are 17.2-18.1 dBm. Meanwhile, the designed PA provides a 24.1%-32.1% peak power-added efficiency (PAE) over the band.

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: none
Teacher disagreement score0.001
Threshold uncertainty score0.004

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 integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.029
GPT teacher head0.209
Teacher spread0.180 · 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

Citations18
Published2020
Admission routes1
Has abstractyes

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