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Record W2972037472 · doi:10.1109/lssc.2019.2938677

A 1.41-pJ/b 56-Gb/s PAM-4 Receiver Using Enhanced Transition Utilization CDR and Genetic Adaptation Algorithms in 7-nm CMOS

2019· article· en· W2972037472 on OpenAlexaff
Behzad Dehlaghi, Kerry Tang, Anthony Chan Carusone, David Cassan, Davide Tonietto, Shayan Shahramian, Joshua Liang, Ryan Bespalko, Dustin Dunwell, James Bailey, Bo Wang, Alireza Sharif-Bakhtiar, Michael O'Farrell

Bibliographic record

VenueIEEE Solid-State Circuits Letters · 2019
Typearticle
Languageen
FieldEngineering
TopicAdvancements in PLL and VCO Technologies
Canadian institutionsUniversity of TorontoHuawei Technologies (Canada)
Fundersnot available
KeywordsJitterCMOSElectronic engineeringClock recoveryComputer scienceSignal edgeDetectorBit error ratePhysicsDecoding methodsEngineeringClock signalAlgorithmTelecommunicationsAnalog signalDigital signal processing

Abstract

fetched live from OpenAlex

This letter presents a 56.25-Gb/s analog-mixed signal pulse amplitude modulation (PAM)-4 receiver in 7-nm fin field effect transistor (FinFET) CMOS. The receiver uses an analog front-end (AFE) with extensive programmability and can equalize channels with up to 22.3-dB loss at 14 GHz. AFE settings are optimized using a genetic adaptation algorithm to find the global minima for the bit-error-rate (BER). A PAM-4 clock recovery scheme is proposed that reduces the number of required edge samplers for a PAM-4 bang-bang phase detector without degrading the jitter tolerance of the receiver. Using an AFE as opposed to a decision-feedback equalizer (DFE) along with the proposed clock recovery scheme results in low energy consumption of 1.41 pJ/bit.

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: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.030
GPT teacher head0.255
Teacher spread0.225 · 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

Citations22
Published2019
Admission routes1
Has abstractyes

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Same venueIEEE Solid-State Circuits LettersSame topicAdvancements in PLL and VCO TechnologiesFrench-language works237,207