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Record W2955192580 · doi:10.22215/etd/2019-13657

A 5GHz Passively Interpolated 5-Bit Time-to-Digital Converter with 8ps Resolution in IBM 130nm CMOS

2019· dissertation· en· W2955192580 on OpenAlexaff
Kiril Kidisyuk

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldEngineering
TopicAdvancements in PLL and VCO Technologies
Canadian institutionsCarleton University
Fundersnot available
KeywordsLinearityTime-to-digital converter12-bitCMOSElectronic engineeringDynamic rangeWide dynamic rangeChipComputer scienceEngineeringElectrical engineeringJitterClock signal

Abstract

fetched live from OpenAlex

This work demonstrates the development of a 5-bit time to digital converter (TDC) using the local passive interpolation (LPI) technique.The TDC architecture achieves a high resolution, while maintaining a low conversion latency, and a good linearity over process variation at multi-GHz rate of operation, which simplifies the calibration process.The time-to-digital converter was fabricated in a 0.13 µm IBM CMOS process (CMRF8SF).At a sampling rate of 100 MHz the maximum frequency of operation was measured to be 1.6 GHz.The uncalibrated resolution of 8.1 psec and a dynamic range of 260 psec were measured.The TDC is compatible with loop counter architectures that can further extend its dynamic range.The raw integral (INL) and differential (DNL) non-linearity of 1.02LSB and 0.52LSB respectively were observed.A correlation with the simulated results confirmed that the proposed LPI-TDC can operate at 5 GHz with some adjustments to measurement setup, input matching, and the on-chip supply integrity.I am deeply

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 score0.005
Threshold uncertainty score0.015

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)0.0050.002

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 head0.004
GPT teacher head0.196
Teacher spread0.193 · 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
Published2019
Admission routes1
Has abstractyes

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Same topicAdvancements in PLL and VCO TechnologiesFrench-language works237,207