MétaCan
Menu
Back to cohort
Record W2947973071 · doi:10.1109/access.2019.2920071

A Varactor-Less DCO With $7~GHz$ Tuning Range for $77~GHz$ Automotive Radars

2019· article· en· W2947973071 on OpenAlexafffund
Iman Taha, Mitra Mirhassani

Bibliographic record

VenueIEEE Access · 2019
Typearticle
Languageen
FieldEngineering
TopicRadio Frequency Integrated Circuit Design
Canadian institutionsUniversity of Windsor
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsVaricapdBcPhase noiseFigure of meritCMOSCapacitorRadarElectrical engineeringLinearityMicrowaveMaterials scienceInductorSilicon carbideOffset (computer science)Radio frequencyElectronic engineeringCapacitanceComputer scienceOptoelectronicsEngineeringPhysicsVoltageTelecommunications

Abstract

fetched live from OpenAlex

The generation of precise linear frequency modulation is a critical requirement for millimeterwave automotive radars. This paper presents the analysis and design of a CMOS 75.5 - 82.5 GHz monotonically linear digitally controlled oscillator (ML-DCO) fulfilling the requirements of the 77 GHz automotive radar. Non-linear large varactors are avoided. Linear coarse, intermediate, and fine-tuning ranges are achieved by arranging the available minimum Metal-Oxide-Metal (MoM) capacitor in unique configurations. Moreover, a new tuning mechanism derived from a Colpitts topology is discussed to meet the wide tuning requirements without losing linearity. The new arrangement results in a minimal amplitude variation while the ML-DCO is tuned. The coarse, intermediate, and fine-tuning steps from the post-layout simulation are 0.22, 10, and 346 KHz, respectively. The power consumption is 14 mW from a 1.2 V supply. The achieved phase-noise is -98.53 dBc/Hz at a 1 MHz offset when oscillating at 77 GHz. The ML-DCO demonstrates -185 Figure of Merit and -184 Figure of Merit for Tuning.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.411
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
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.026
GPT teacher head0.248
Teacher spread0.222 · 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

Citations5
Published2019
Admission routes2
Has abstractyes

Explore more

Same venueIEEE AccessSame topicRadio Frequency Integrated Circuit DesignFrench-language works237,207