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Record W1526280315 · doi:10.1109/leos.2002.1159599

Determination of the optimum cluster parameters for a clustered free-space optical interconnect

2003· article· en· W1526280315 on OpenAlexaff
Marc Châteauneuf, Andrew G. Kirk

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicSemiconductor Lasers and Optical Devices
Canadian institutionsMcGill University
Fundersnot available
KeywordsOptical interconnectInterconnectionMulti-mode optical fiberVertical-cavity surface-emitting laserOpticsOptoelectronicsCollimated lightWavelengthBandwidth (computing)MicrolensMaterials scienceCMOSLaserFlip chipFree-space optical communicationPhysicsOptical fiberComputer scienceLens (geology)Telecommunications

Abstract

fetched live from OpenAlex

Parallel free-space parallel optical interconnects (FSOIs) have great potential for use as high bandwidth interconnects at the board-to-board and chip-to-chip levels Recent reports of the integration of large (1024) arrays of vertical-cavity surface-emitting laser (VCSEL) arrays to complementary metal-oxide semiconductor (CMOS) suggest that large parallel interconnects should be possible in this technology. We have introduced a technique to determine the optimum cluster dimensions for a free-space optical interconnect which deliver the maximum channel density for a given degree of misalignment tolerance. The sources are assumed to be multimode VCSELs (wavelength 850 nm, mode field diameter 6 /spl mu/m, M2 factor of 1.93 and VCSEL pitch 125 /spl mu/m). They are collimated by microlenses with a focal length of 250 /spl mu/m. The required interconnection distance results in a minilens focal length of 8.5 mm (assuming a single relay block to route the optical channels). This technique will be extended to cover arbitrary focal lengths and source parameters by incorporating an analytical calculation of ray aberrations. This approach has the potential to considerably simplify the design of clustered free-space optical interconnects.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.420
Threshold uncertainty score0.312

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.016
GPT teacher head0.228
Teacher spread0.212 · 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 teacher head, 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

Citations1
Published2003
Admission routes1
Has abstractyes

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