MétaCan
Menu
Back to cohort
Record W2135731874 · doi:10.1109/50.939788

256-channel bidirectional optical interconnect using VCSELs and photodiodes on CMOS

2001· article· en· W2135731874 on OpenAlexaff
David V. Plant, Michael B. Venditti, Emmanuelle Laprise, J. Faucher, K.E. Razavi, Marc Châteauneuf, Andrew G. Kirk, J. S. Ahearn

Bibliographic record

VenueJournal of Lightwave Technology · 2001
Typearticle
Languageen
FieldEngineering
TopicSemiconductor Lasers and Optical Devices
Canadian institutionsMcGill University
Fundersnot available
KeywordsPhotodiodeCMOSOptical interconnectVery-large-scale integrationOptoelectronicsElectronic circuitMaterials scienceInterconnectionTransceiverVertical-cavity surface-emitting laserLaserChipElectronic engineeringComputer scienceElectrical engineeringOpticsEngineeringTelecommunicationsPhysics

Abstract

fetched live from OpenAlex

Two-dimensional parallel optical interconnects (2-D-POIs) are capable of providing large connectivity between elements in computing and switching systems. Using this technology we have demonstrated a bidirectional optical interconnect between two printed circuit boards containing optoelectronic (OE) very large scale integration (VLSI) circuits. The OE-VLSI circuits were constructed using vertical cavity surface emitting lasers (VCSELs) and photodiodes (PDs) flip-chip bump-bonded to a 0.35-/spl mu/m complementary metal-oxide-semiconductor (CMOS) chip. The CMOS was comprised of 256 laser driver circuits, 256 receiver circuits, and the corresponding buffering and control circuits required to operate the large transceiver array. This is the first system, to our knowledge, to send bidirectional data optically between OE-VLSI chips that have both VCSELs and photodiodes cointegrated on the same substrate.

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 score0.002
Threshold uncertainty score0.006

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)0.0020.000

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 head0.019
GPT teacher head0.239
Teacher spread0.220 · 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

Citations107
Published2001
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Lightwave TechnologySame topicSemiconductor Lasers and Optical DevicesFrench-language works237,207