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Record W4386303960 · doi:10.18280/mmep.100421

An Enhanced Design of Cascaded Single-Stage Distributed Amplifiers Utilizing Quasi-Differential Amplifier Cells

2023· article· en· W4386303960 on OpenAlexvenueno aff
Saeed R. Saeed, Azad R. Kareem, Ashwaq Q. Hameed

Bibliographic record

VenueMathematical Modelling and Engineering Problems · 2023
Typearticle
Languageen
FieldEngineering
TopicEnergy Harvesting in Wireless Networks
Canadian institutionsnot available
Fundersnot available
KeywordsAmplifierStage (stratigraphy)Differential amplifierDifferential (mechanical device)Distributed amplifierDirect-coupled amplifierComputer scienceElectronic engineeringOperational amplifierPhysicsEngineeringTelecommunicationsBiologyBandwidth (computing)

Abstract

fetched live from OpenAlex

Cascaded Single-Stage Distributed Amplifiers (CSSDAs) are instrumental in achieving ultra-wideband amplification for microwave applications due to their significant gainbandwidth products.However, their functionality is often compromised by internal noise, which detrimentally impacts the linearity of the response.An innovative solution to this prevalent issue is presented in this study through the introduction of the Quasi-Differential Distributed Amplifier (QDDA).Implementing the 0.18μm Complementary Metal Oxide Semiconductor (CMOS) technology, a QDDA with a single-stage fourcascade configuration was designed, fabricated, and tested.The empirical results revealed a high gain of 20dB and an extensive bandwidth of 30GHz.Moreover, the noise figure was observed to be 4.809 with a compact chip size of 0.74mm² .This design and the resulting findings were accomplished using the Advanced Design System (ADS) RF simulator.The circuit layout and specifications were subsequently generated using the Cadence tool.This research demonstrates the potential of the QDDA to significantly enhance the performance of CSSDAs, contributing to the advancement of ultra-wideband microwave applications.

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)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.752
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)0.0000.000

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 head0.045
GPT teacher head0.225
Teacher spread0.180 · 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 teacher head, not a consensus.

Study designSimulation or modeling
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

Citations0
Published2023
Admission routes1
Has abstractyes

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