MétaCan
Menu
Back to cohort
Record W2003425107 · doi:10.1063/1.2181310

Optical attenuation in defect-engineered silicon rib waveguides

2006· article· en· W2003425107 on OpenAlexafffund
Phil Foster, J. K. Doylend, Peter Mascher, Andrew P. Knights, P. G. Coleman

Bibliographic record

VenueJournal of Applied Physics · 2006
Typearticle
Languageen
FieldEngineering
TopicPhotonic and Optical Devices
Canadian institutionsMcMaster University
FundersNatural Sciences and Engineering Research Council of CanadaMcMaster University
KeywordsAttenuationSiliconMaterials scienceIon implantationSilicon on insulatorOptoelectronicsAbsorption (acoustics)Silicon photonicsPhotonicsRefractive indexWaveguideAttenuation lengthOpticsIonChemistryPhysics

Abstract

fetched live from OpenAlex

The excess optical attenuation at wavelengths around 1550nm induced by subamorphous dose ion implantation of silicon-on-insulator rib waveguides has been quantified. Optical attenuation is related to the introduction of lattice defects such as the silicon divacancy. After 2.8MeV Si+ implantation at a dose of 2.5×1014cm−2, the attenuation is greater than 1000dBcm−1. Using positron annihilation spectroscopy to determine the vacancy-type defect concentration, it is demonstrated that the absorption component of the excess attenuation can be predicted using a simple analytical expression. Additional losses are suggested to result from a defect induced change in the real part of the refractive index of the silicon waveguide. A processing strategy for ensuring that the absorption component dominates the excess attenuation is described, and it is shown that selective implantation of a relatively low dose of inert ions is an efficient method for the reduction of optical cross talk in silicon photonic circuits.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.184
Threshold uncertainty score0.398

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 head0.008
GPT teacher head0.206
Teacher spread0.198 · 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 designSimulation or modeling
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

Citations39
Published2006
Admission routes2
Has abstractyes

Explore more

Same venueJournal of Applied PhysicsSame topicPhotonic and Optical DevicesFrench-language works237,207