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Record W2098381788 · doi:10.1109/rfic.2008.4561393

A 2.5mW inductorless wideband VGA with dual feedback DC-offset correction in 90nm CMOS technology

2008· article· en· W2098381788 on OpenAlexafffund
Yanjie Wang, Bagher Afshar, Tuan-Yi Cheng, Vincent Gaudet, Ali M. Niknejad

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicRadio Frequency Integrated Circuit Design
Canadian institutionsUniversity of Alberta
FundersFaculty of Graduate Studies and Research, University of AlbertaDefense Advanced Research Projects AgencyUniversity of Alberta
KeywordsVideo Graphics ArrayCMOSWidebandVariable-gain amplifierElectrical engineeringDC biasAmplifierElectronic engineeringChipOffset (computer science)BasebandBandwidth (computing)EngineeringComputer scienceOperational amplifierVoltageTelecommunications

Abstract

fetched live from OpenAlex

A low power inductorless wideband variable gain control amplifier (VGA) for baseband receivers has been designed in a standard digital 90nm CMOS technology. The VGA was implemented using four-stage modified Cherry-Hooper amplifier with a dual feedback DC-offset canceling network, which simultaneously corrects DC offsets and extends bandwidth without a peaking inductor resulting in saving the chip space significantly. The proposed VGA has been measured using on-chip probing and achieves a 3-dB bandwidth of more than 2.2GHz with 60dB gain tuning range. It consumes 2.5mW through a 1V supply (excluding the output buffer), and occupies only 0.01mm2active area.

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: none
Teacher disagreement score0.003
Threshold uncertainty score0.009

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 science0.0020.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.013
GPT teacher head0.189
Teacher spread0.176 · 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".

Quick stats

Citations59
Published2008
Admission routes2
Has abstractyes

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