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Record W4402448540 · doi:10.1109/lssc.2024.3457775

A 112-Gb/s, -10 dBm Sensitivity, +5 dBm Overload, and SiPh-Based Receiver Frontend in 22-nm FDSOI

2024· article· en· W4402448540 on OpenAlexaff
Mahdi Parvizi, Bahar Jalali, Toshi Omori, John Rogers, Li Chen, Long Chen, Ricardo F. Aroca

Bibliographic record

VenueIEEE Solid-State Circuits Letters · 2024
Typearticle
Languageen
FieldEngineering
TopicPhotonic and Optical Devices
Canadian institutionsCisco Systems (Canada)
Fundersnot available
KeywordsdBmSensitivity (control systems)PhysicsOptoelectronicsElectrical engineeringMaterials scienceCMOSEngineeringElectronic engineeringAmplifier

Abstract

fetched live from OpenAlex

This letter demonstrates a Si-Photonic (SiPh)-based 112 Gb/s PAM4 optical receiver frontend using novel single-ended transimpedance amplifier (TIA) architecture that achieves −10 and +5 dBm input optical modulation amplitude (OMA) sensitivity and overload, respectively. To achieve that an overload mitigation circuit is proposed to break the tradeoff between noise and linearity of the shunt feedback CMOS TIAs. The TIA is optimized to provide the best sensitivity and linearity performance at minimum and maximum input OMA, respectively. Implemented in 22-nm FDSOI technology, and designed for 112 Gb/s PAM4 optical links, the TIA achieves more than +15 dBm OMA range with 11 pA/$\surd $Hz input referred noise while burning only 155 mW from an 1.8-V supply.

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 score0.004

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 science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.010
GPT teacher head0.226
Teacher spread0.216 · 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

Citations4
Published2024
Admission routes1
Has abstractyes

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