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Record W2123352527 · doi:10.1109/tim.2009.2013685

A Delay Generation Technique for Narrow Time Interval Measurement

2009· article· en· W2123352527 on OpenAlexaff
Rashid Rashidzadeh, Roberto Muscedere, Majid Ahmadi, William C. Miller

Bibliographic record

VenueIEEE Transactions on Instrumentation and Measurement · 2009
Typearticle
Languageen
FieldEngineering
TopicAdvancements in PLL and VCO Technologies
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsCMOSElectronic engineeringTime-to-digital converterDynamic rangeAmplifierInterval (graph theory)Computer scienceChipPicosecondVoltageElectronic circuitElectrical engineeringEngineeringPhysicsMathematicsOptics

Abstract

fetched live from OpenAlex

A new architecture for the on-chip measurement of short-time intervals is proposed in this paper. The measurement method is similar to a typical low-voltage measurement setup where the input signals are first amplified and then measured to relax the dynamic range of the succeeding analog-to-digital converter. In the proposed method, narrow time intervals are first amplified by a time amplifier (TAMP) and then measured by a time-to-digital converter. A delay-locked-loop (DLL) circuit is utilized to design a feedback time amplifier in which the gain is readily programmed by input data to any integer value within a range specified by the number of delay cells in the DLL. The TAMP's gain remains rather unchanged under process and temperature variations due to the inherent negative feedback of the DLL system. The circuit is implemented using complementary metal--oxide semiconductor (CMOS) 0.18- mum technology occupying less than 0.63 mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> of the silicon area. The simulation results show that the proposed scheme can successfully be employed to measure time intervals in the range of a few tens of picoseconds with acceptable accuracy.

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 score0.978
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 head0.047
GPT teacher head0.256
Teacher spread0.209 · 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

Citations31
Published2009
Admission routes1
Has abstractyes

Explore more

Same venueIEEE Transactions on Instrumentation and MeasurementSame topicAdvancements in PLL and VCO TechnologiesFrench-language works237,207