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Record W2969850190 · doi:10.1109/ieee-iws.2019.8803898

High Gain 0.25 μm GaN HEMT Based MMIC LNA for GNSS Applications

2019· article· en· W2969850190 on OpenAlex
Maheen Hafeez, Ahmed M. Elelimy Abounemra, Fadhel M. Ghannouchi

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venue2019 IEEE MTT-S International Wireless Symposium (IWS) · 2019
Typearticle
Languageen
FieldEngineering
TopicRadio Frequency Integrated Circuit Design
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsMonolithic microwave integrated circuitHigh-electron-mobility transistorGNSS applicationsOptoelectronicsGallium nitrideWide-bandgap semiconductorMaterials scienceElectrical engineeringComputer scienceEngineeringTelecommunicationsGlobal Positioning SystemTransistorAmplifierNanotechnologyCMOS

Abstract

fetched live from OpenAlex

In this paper, two-stages low noise amplifier (LNA) based on common-source with feedback topology was designed using ALGAN/GAN 0.25um high electron mobility transistor (HEMT) technology on silicon carbide (SiC) substrate. The LNA is designed to be employed for Global Navigation Satellite System (GNSS) Applications. The feedback circuit topology is used in the design to achieve a noise figure of 1 - 1.2 dB with a small signal gain of 33-34 dB. Post-Layout simulation results demonstrate that the input return loss is -11 to -14 dB and the output return loss is -16 to -18 dB across the entire frequency range of 1.5 GHz to 1.7 GHz. The LNA has an output P1 dB and OIP3 of 18 dBm and 28 dBm, respectively which illustrated high linear performance. The power consumption is 0.7 W using a voltage supply of 5.5 V. The total chip size is 2.6 mm x 2.2 mm including the pads.

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.

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 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 score0.717
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.002

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 head0.007
GPT teacher head0.215
Teacher spread0.209 · 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