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Record W2971679761 · doi:10.1109/mwsym.2019.8700801

InGaP/GaAs HBT Broadband Power Amplifier IC with 54.3% Fractional Bandwidth Based on Cascode Structure

2019· article· en· W2971679761 on OpenAlexaff
Wooseok Lee, Hyunuk Kang, Hwiseob Lee, Wonseob Lim, Jongseok Bae, Hyungmo Koo, Jangsup Yoon, Youngoo Yang

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicRadio Frequency Integrated Circuit Design
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsCascodeBandwidth (computing)AmplifierElectrical engineeringHeterojunction bipolar transistorMaterials scienceBandwidth extensionBroadbandInductorCapacitanceElectronic engineeringTransistorComputer scienceVoltageEngineeringPhysicsTelecommunicationsBipolar junction transistorElectrode

Abstract

fetched live from OpenAlex

This paper presents the development of a broadband two-stage cascode PAIC with a fractional bandwidth of 54.3% using a 2- m InGaP/GaAs HBT process. The higher supply voltage of the cascode PA results in a lower current, which can be advantageous for broadband operation because of the lower impedance transformation ratio of the output matching network. According to bandwidth analysis based on the power and efficiency contours at the internal plane of the transistor, an optimized shunt inductor for a single L-section load matching network is proposed to increase the bandwidth of the cascode PA by compensating for the output capacitance. The proposed PAIC was designed and implemented for the frequency band from 1.55 to 2.65 GHz. Using an LTE signal with a PAPR of 7.5 dB and a signal bandwidth of 10 MHz, a power gain of more than 23.7 dB, PAE from 30.9 to 38.4%, and average output power from 26.7 to 27.7 dBm were obtained at a given ACLR of -30 dBc.

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 score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
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 head0.005
GPT teacher head0.181
Teacher spread0.177 · 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

Citations5
Published2019
Admission routes1
Has abstractyes

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