MétaCan
Menu
Back to cohort
Record W2538565530 · doi:10.1109/imarc.2014.7038969

Mm-wave power-DAC transmitters with transistor and antenna segmentation

2014· article· en· W2538565530 on OpenAlexafffund
Sorin P. Voinigescu, Stefan Shopov, Andreea Balteanu, Ioannis Sarkas

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicRadio Frequency Integrated Circuit Design
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaDefense Advanced Research Projects Agency
KeywordsCMOSTransmitterElectrical engineeringdBmTransistorPower (physics)Modulation (music)Antenna (radio)Electronic engineeringSIGNAL (programming language)Quadrature amplitude modulationComputer scienceEngineeringPhysicsChannel (broadcasting)AcousticsVoltage

Abstract

fetched live from OpenAlex

This paper describes the architecture and transistor-level implementation of nanoscale CMOS mm-wave IQ power-DAC transmitter arrays with direct digital modulation of the carrier signal and free-space formation of QAM signal constellations. Examples of power-DAC lanes are provided in 45nm SOI CMOS at 45, 90, 110 and 138 GHz with measured power-DAC lane output powers between 24 dBm and 13 dBm.

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 score0.002
Threshold uncertainty score0.008

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 science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.168
Teacher spread0.160 · 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

Citations0
Published2014
Admission routes2
Has abstractyes

Explore more

Same topicRadio Frequency Integrated Circuit DesignFrench-language works237,207