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Record W2294455195

A 0.13μm CMOS low-noise amplifier using resistive feedback current reuse technique for 3.1-10.6 GHz ultra-wideband receivers

2012· article· en· W2294455195 on OpenAlexaff
Nhan Trung Nguyen, Tan Nghia Duong, Ahn Dinh

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicRadio Frequency Integrated Circuit Design
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsLow-noise amplifierWidebandElectronic engineeringAmplifierElectrical engineeringNoise figureCMOSNoise (video)EngineeringPower gainUltra-widebandComputer science
DOInot available

Abstract

fetched live from OpenAlex

This paper presents the design of a 3.1-10.6GHz ultra-wideband (UWB) low noise amplifier (LNA) using resistive feedback and current reuse technique for ultra-wideband receiver. Low noise amplifier is the foremost, challenging and indispensable building block to build a receiver. The current reuse technique is addressed to optimize noise performance and power efficiency while maintaining a good power gain and input/output matching. The LNA was designed and implemented using IBM 0.13μm CMOS technology. Simulation results show a power gain (S21) of 12.5dB, a good noise figure (NF) of 3.05dB and an input return loss (S11) of less than -16.5dB. The LNA occupies an area of 0.11mm2 and consumes 11.08mW of power for the 1.4V power supply. Thus this LNA should be readily useful for ultra-wideband receiver systems.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.955
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 communication0.0000.001
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 head0.035
GPT teacher head0.267
Teacher spread0.232 · 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 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
Published2012
Admission routes1
Has abstractyes

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