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Record W2584415150 · doi:10.1109/icecs.2016.7841284

Single-ended impedance-modulation equalization for low-power differential voltage-mode drivers

2016· article· en· W2584415150 on OpenAlexaff
Badiey Ali, Reza Papi, Navid Rahmanikia, Mohammad Taherzadeh‐Sani, Frédéric Nabki

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvancements in PLL and VCO Technologies
Canadian institutionsÉcole de Technologie Supérieure
Fundersnot available
KeywordsCMOSOutput impedanceElectrical impedanceElectronic engineeringModulation (music)Input impedanceTransistorEqualization (audio)VoltageElectrical engineeringComputer scienceEngineeringChannel (broadcasting)PhysicsAcoustics

Abstract

fetched live from OpenAlex

In this paper, a modified structure for low-swing voltage-mode drivers in high-speed serial links is proposed, which offers lower static power and reduces the number of transistors compared to the conventional design. This structure adopts an impedance-modulated 2-tap equalizer with analog tap control. The impedance modulation technique is applied to only one input of the driver. Therefore, six transistors are eliminated in the driver and their control impedance circuitry is removed. Simulation results of an 8-Gb/s serial link in a 0.13-μm CMOS technology confirm that the proposed structure reduces the width of the transistors and the static power consumption by 20% and 11%, respectively. Also when a 200 mVp-p differential signal is sent over a channel with 10 dB attenuation at 4 GHz, the eye diagram of the received data has a 113.5 mV eye opening.

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)0.001
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 score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.015
GPT teacher head0.258
Teacher spread0.242 · 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
Published2016
Admission routes1
Has abstractyes

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