MétaCan
Menu
Back to cohort
Record W4206102164 · doi:10.22215/etd/2021-14823

Wideband Tunable Transmission-line N-path Filter on CMOS 130 nm

2021· dissertation· en· W4206102164 on OpenAlexaff
Zeynab Adibifard

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldEngineering
TopicElectromagnetic Compatibility and Noise Suppression
Canadian institutionsCarleton University
Fundersnot available
KeywordsVoltage-controlled filterElectronic engineeringClock generatorHigh-pass filterFilter (signal processing)Bandwidth (computing)Filter designButterworth filterRoot-raised-cosine filterCMOSm-derived filterWidebandElectrical engineeringLow-pass filterComputer scienceEngineeringTelecommunicationsClock signalJitter

Abstract

fetched live from OpenAlex

This research aims to increase the bandwidth of a transmission-line N-path filter around the clock frequency. The proposed filter consists of two four-path parallel stages with series inductors. The filter solves the trade-off between in-band insertion loss and out-of-band rejection of the original N-path filter. The proposed filter is tunable between 0.1 and 1 GHz. Post layout simulation results show that bandwidths of 80 MHz can be achieved when the filter is tuned to 1 GHz. A high-frequency 4-phase non-overlapping clock generator with a 25% duty cycle is designed to drive the proposed filter; an off-chip clock is applied at 4 times the switching frequency. The proposed filter has a die area of 1.5 2 and was fabricated with CMOS 130-nm technology. The post-layout simulation results show that the filter is tunable from 0.1 to 1GHz, bandwidth of 80 MHz can be achieved at 1 GHz and the noise figure of the filter is less than 3.2 dB over the frequency range. Unfortunately, the clock generator is not working properly, which is why measurement results show discrepancies from the simulated results. Several hypotheses are explored to explain the cause of these differences.

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), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.316
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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0090.000

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 head0.008
GPT teacher head0.225
Teacher spread0.217 · 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

Citations0
Published2021
Admission routes1
Has abstractyes

Explore more

Same topicElectromagnetic Compatibility and Noise SuppressionFrench-language works237,207