MétaCan
Menu
Back to cohort
Record W3153945453 · doi:10.1049/cds2.12063

An 8‐bit digital‐to‐time converter with pre‐skewing and time interpolation

2021· article· en· W3153945453 on OpenAlexafffund
Daniel Junehee Lee, Fei Yuan, Gul N. Khan, Yushi Zhou

Bibliographic record

VenueIET Circuits Devices & Systems · 2021
Typearticle
Languageen
FieldEngineering
TopicAdvancements in PLL and VCO Technologies
Canadian institutionsLakehead UniversityToronto Metropolitan University
FundersCMC Microsystems
KeywordsLinearityComputer scienceCMOSElectronic engineeringInterpolation (computer graphics)Latency (audio)InverterCascodeAmplifierVoltageElectrical engineeringEngineeringTelecommunications

Abstract

fetched live from OpenAlex

Abstract This study presents an 8‐bit delay line digital‐to‐time converter (DTC) with pre‐skewing and digital time interpolation. Pre‐skewing that lowers the per‐stage‐delay of delay lines beyond that set by the chosen technology is investigated. A cascode tri‐state inverter is proposed to improve the isolation between the input and output of interpolation cells so as to improve the linearity of the time interpolator. Design considerations that critically affect the linearity of the DTC are examined in detail. The impact of the slope of the inputs of the time interpolator on the latency and linearity of the interpolator is analysed and the maximum slope of the input of interpolators yielding the minimum latency without sacrificing linearity, is obtained. The timing errors of DTC are investigated and the considerations of the layout of the DTC are examined. The DTC is designed in a TSMC 65 nm 1.0 V CMOS technology and analysed using Spectre with BSIM3V3 device models. Post‐layout simulation results show the DTC offers 3.6 ps resolution, 580 MS/s conversion rate, and consumes 383 μ W.

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 score0.004
Threshold uncertainty score0.013

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 communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.001

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 head0.007
GPT teacher head0.210
Teacher spread0.203 · 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

Citations6
Published2021
Admission routes2
Has abstractyes

Explore more

Same venueIET Circuits Devices & SystemsSame topicAdvancements in PLL and VCO TechnologiesFrench-language works237,207