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Design and Comparison of Fully Integrated mm-Wave GaAs Power Amplifiers Using Physics-Based Compact Model

2025· article· W4415745939 on OpenAlexafffund
Pilsoon Choi, Yijing Feng, Ujwal Radhakrishna, Eugene A. Fitzgerald, Lan Wei

Bibliographic record

Venuenot available
Typearticle
Language
FieldEngineering
TopicAdvanced Power Amplifier Design
Canadian institutionsUniversity of Waterloo
FundersUniversity of Waterloo
KeywordsdBmAmplifierHigh-electron-mobility transistorBandwidth (computing)Power (physics)Nonlinear systemGallium arsenideRF power amplifier

Abstract

fetched live from OpenAlex

Millimeter-wave (mm-Wave) power amplifiers (PAs) are designed using a physics-based compact model and fabricated in$0.15 \mu \mathrm{m}$GaAs pHEMT technology. The model is calibrated to match transistor-level I-V, C-V, and S-parameter characteristics and is shown to accurately predict the measured large signal circuit-level performance of the PA without further parameter adjustment. The PA consists of combined class-AB and class-C devices with a single-cascode configuration and is compared to a double-cascode configuration by analyzing nonlinear behavior of input capacitances. It performs 19.6 dBm OP1dB, 29.7 dBm OIP3, and 39.8% PAE at 5V supply. Measured error vector magnitude (EVM) is −30.5 dB at 10.1 dBm with 100 MHz bandwidth 256-QAM OFDM modulated signals at 24 GHz. The die size is only 0.3 mm2excluding RF pads area, and the PA shows competitive performance at mm-Wave frequencies.

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: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

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 communication0.0010.001
Open science0.0010.000
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 head0.074
GPT teacher head0.316
Teacher spread0.242 · 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".

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Citations0
Published2025
Admission routes2
Has abstractyes

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