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Record W2290610940 · doi:10.1109/tbcas.2015.2434957

A Low-Power Gateable Vernier Ring Oscillator Time-to-Digital Converter for Biomedical Imaging Applications

2015· article· en· W2290610940 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueIEEE Transactions on Biomedical Circuits and Systems · 2015
Typearticle
Languageen
FieldEngineering
TopicAdvancements in PLL and VCO Technologies
Canadian institutionsHamilton Health SciencesMcMaster University
Fundersnot available
KeywordsVernier scaleRing oscillatorIntegral nonlinearityTime-to-digital converterDifferential nonlinearityRing (chemistry)CMOSChipPower (physics)Resolution (logic)CalibrationElectronic engineeringComputer scienceElectrical engineeringPhysicsElectronic circuitVoltageEngineeringOpticsConvertersArtificial intelligence

Abstract

fetched live from OpenAlex

In this paper, a high resolution, high precision and ultra-low power consumption time-to-digital converter (TDC) is presented. The proposed TDC is based on the gateable Vernier ring oscillator architecture. Fine resolution is achieved through two ring oscillators arranged in the Vernier configuration. This TDC employs a single-transition end-of-conversion detection circuit and turns off the ring oscillators whenever the conversion is completed to reduce power consumption. The prototype chip is fabricated in a standard 130 nm digital CMOS process and its area is only 0.03 mm(2). Using a 1.2 V supply, the TDC achieves a resolution of 7.3 ps, a single-shot precision of 1.0LSB, and an average power consumption of 1.2 mW. A root-mean-square integral nonlinearity (INL) of 1.2 LSB is obtained with the help of an INL look-up-table calibration. Compared to previously reported ring-oscillator based TDCs, the proposed design achieves the lowest power consumption to date.

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.

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.995
Threshold uncertainty score0.730

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)0.0000.000

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 head0.015
GPT teacher head0.233
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