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Record W2113051509 · doi:10.1109/ccece.2004.1347667

A 0.18 μm CMOS channel select filter using Q-enhancement technique

2004· article· en· W2113051509 on OpenAlexaff
Jiandong Ge, Anh Dinh

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicRadio Frequency Integrated Circuit Design
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsActive filterElectronic engineeringVoltage-controlled filterVaricapHigh-pass filterLow-pass filterButterworth filterBand-stop filterBandwidth (computing)CMOSPrototype filterm-derived filterInductorComputer scienceElectrical engineeringAll-pass filterFilter (signal processing)CapacitanceEngineeringVoltageTelecommunicationsPhysics

Abstract

fetched live from OpenAlex

A channel select filter is designed in particular for the 3.6 GHz range applications. The main function of the filter is to select a channel via a selecting voltage. It also provides a high gain for the selected bandwidth. The filter consists of an on-chip inductor in conjunction with an on-chip varactor. The varactor acts as a variable capacitor; the selecting voltage changes the junction capacitance of the varactor to shift the center frequency. The filter is implemented as an active BPF and provides about 5 dB gain for the selected bandwidth which operates on 1.8 Vdc. The gain and high passband selectivity are the results of the design of a high quality factor filter (few hundreds). A Q-enhancement circuit generates a negative impedance to compensate the low Q of the onchip inductor and provides further selectivity. The Q of this filter is also a function of an external control voltage (i.e., tunable Q). This option offers bandwidth settings for various applications in the selected frequency range. The filter also provides a simple implementation of the receiver as it replaces the off-chip front-end filter and the LNA.

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 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 score0.976
Threshold uncertainty score0.918

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)0.0010.000

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 head0.031
GPT teacher head0.238
Teacher spread0.208 · 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.

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

Citations4
Published2004
Admission routes1
Has abstractyes

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