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Record W4248940443 · doi:10.22215/etd/2014-10617

Reconfigurable Matching Networks for High Power GaN Microwave Amplifiers

2014· dissertation· en· W4248940443 on OpenAlexaboutno aff
Alaa Aglan

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldEngineering
TopicAdvanced Power Amplifier Design
Canadian institutionsnot available
Fundersnot available
KeywordsTunerSmith chartMonolithic microwave integrated circuitImpedance matchingAmplifierElectronic engineeringEngineeringTransistorElectrical engineeringPower (physics)Electrical impedanceMicrowaveFabricationRadio frequencyVoltageTelecommunicationsPhysicsCMOS

Abstract

fetched live from OpenAlex

This thesis demonstrates the feasibility of using a GaN monolithic reconfigurable matching network to provide variable load impedance matching coverage for microwave power amplifiers operating in the X-band (8-12GHz).The National Research Council's GaN500 (0.5 micron) HFET process, fabricated at the Canadian Photonics Fabrication Center (CPFC), is employed throughout this work.An initial investigation of various switch topologies is first conducted, showing the advantages and limitations of using single and multi-transistor switch realizations for the development of a multi-stage programmable impedance tuner (PIT).Then, the design, optimization, fabrication and testing of a single stage of the proposed PIT structure are presented.The results show an extensive range of impedance coverage on the Smith Chart can be achieved, although this range is limited by losses.Finally, the co-integration of the resulting programmable tuners within a GaN power amplifier circuit is simulated, and its performance is studied.I would like to thank my supervisor, Dr. Langis Roy for his relentless support and patience; and co-supervisor, Dr. Rony Amaya, for inspiring the topic of this master's thesis and for his generosity with his vast knowledge and insight.I am indebted to Nagui Mikhail, who throughout the years at Carleton University, has always gone above and beyond to ensure both software and hardware in the department of electronics is running smoothly.His efforts to ensure I had all the components and equipment I needed to test my chip will not be forgotten.Also, special thanks are reserved for fellow

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: Other · 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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.008
GPT teacher head0.225
Teacher spread0.217 · 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
GenreOther

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

Citations0
Published2014
Admission routes1
Has abstractyes

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