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Record W2082298115 · doi:10.1109/mwsym.2007.379991

2 GHz Automatically Tuned Q-Enhanced CMOS Bandpass Filter

2007· article· en· W2082298115 on OpenAlexaff
J.K. Nakaska, J.W. Haslett

Bibliographic record

VenueIEEE MTT-S International Microwave Symposium digest · 2007
Typearticle
Languageen
FieldEngineering
TopicRadio Frequency Integrated Circuit Design
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsBand-pass filterVoltage-controlled filterPassbandCenter frequencyHigh-pass filterFilter (signal processing)Band-stop filterm-derived filterElectronic engineeringPrototype filterButterworth filterLow-pass filterAll-pass filterActive filterMaterials scienceComputer scienceElectrical engineeringEngineeringVoltage

Abstract

fetched live from OpenAlex

An automatically tuned 2 GHz 0.18 mum CMOS 3-stage RF filter is presented. The bandpass filter achieves center frequency tuning while maintaining a relatively flat passband. Q-enhanced resonators allow for more than 28 dB of insertion loss compensation in the Alter response. Measured results show that the filter is tunable in frequency by 32.5%, has a 14.7 dB noise figure, a -3.9 dBm 1dB input compression point, a +5.1 dBm IIP3 intercept, and consumes 21 mA from a 1.8 V supply. Automatic tuning of the multistage filter is performed by over-enhancing each stage of the filter until oscillation occurs to set resonant frequency and then backing off the enhancement using a passive Q tuning method, which does not effect the resonant frequency. Automatic frequency and quality factor tuning control were performed using digital logic synthesized in an FPGA. The hardware in-situ automatic tuning of the multi-pole integrated filter eliminates the need for a replica filter.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · 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 communication0.0000.000
Open science0.0010.000
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 head0.008
GPT teacher head0.221
Teacher spread0.212 · 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 designNot applicable
Domainnot available
GenreMethods

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

Citations16
Published2007
Admission routes1
Has abstractyes

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