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Record W4401631610 · doi:10.22215/etd/2024-16011

An Integrated GaN Power Amplifier at 5 GHz

2024· dissertation· en· W4401631610 on OpenAlexaff
Abhijit Arun

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldPhysics and Astronomy
TopicGaN-based semiconductor devices and materials
Canadian institutionsCarleton University
Fundersnot available
KeywordsAmplifierdBmGallium nitrideRF power amplifierMaterials sciencePower bandwidthDirect-coupled amplifierPower (physics)Electrical engineeringBandwidth (computing)OptoelectronicsElectronic engineeringEngineeringPhysicsOperational amplifierTelecommunicationsCMOS

Abstract

fetched live from OpenAlex

This thesis presents the design and analysis of a fully integrated Gallium Nitride (GaN) power amplifier operating at 5 GHz, fabricated using the Skyworks 0.25 m GaN process.The proposed amplifier is designed to be fully integrated onto a single die, with an OIP3 over 40 dBm, a maximum instantaneous bandwidth of 100 MHz, and a saturated power of 35 dBm into a 50- load.The layout, extracted using EMX, confirms an OIP3 of 41.5 dBm, a single-tone gain of 9.5 dB, and a saturated power of 35 dBm.Although the final amplifier was not able to be taped-out, previously designed gain and power-matched amplifier test structures were measured and back-fitted to forecast the potential success of the proposed design.greatly enriched this thesis.My gratitude also goes to my manager at Skyworks, Peter Chyurlia, for his understand-

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.433
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0610.002

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 head0.010
GPT teacher head0.277
Teacher spread0.267 · 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; both teacher heads agree on what is shown here.

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

Citations1
Published2024
Admission routes1
Has abstractyes

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