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Record W2036097289 · doi:10.1116/1.2186657

Fabrication of lithographically defined optical coupling facets for silicon-on-insulator waveguides by inductively coupled plasma etching

2006· article· en· W2036097289 on OpenAlexafffund
K. P. Yap, B. Lamontagne, A. Delâge, Siegfried Janz, A. L. Bogdanov, M.-J. Picard, E. Post, P. Chow‐Chong, Matthew Malloy, Don J. Roth, Pamela A. Marshall, Kangqi Liu, B. Syrett

Bibliographic record

VenueJournal of Vacuum Science & Technology A Vacuum Surfaces and Films · 2006
Typearticle
Languageen
FieldEngineering
TopicPhotonic and Optical Devices
Canadian institutionsNational Research Council CanadaInstitute for Microstructural SciencesCarleton University
FundersNational Research Council Canada
KeywordsMaterials sciencePolishingInductively coupled plasmaFabricationEtching (microfabrication)Facet (psychology)OptoelectronicsPlasma etchingReactive-ion etchingSilicon on insulatorChemical-mechanical planarizationPhotonicsInsulator (electricity)SiliconOpticsPlasmaNanotechnologyLayer (electronics)

Abstract

fetched live from OpenAlex

We present a technique to lithographically define and fabricate all required optical facets on a silicon-on-insulator photonic integrated circuit by an inductively coupled plasma etch process. This technique offers 1μm positioning accuracy of the facets at any location within the chip and eliminates the need of polishing. Facet fabrication consists of two separate steps to ensure sidewall verticality and minimize attack on the end surfaces of the waveguides. Protection of the waveguides by a thermally evaporated aluminum layer before the 40–70μm deep optical facet etching has been proven essential in assuring the facet smoothness and integrity. Both scanning electron microscopy analysis and optical measurement results show that the quality of the facets prepared by this technique is comparable to the conventional facets prepared by polishing.

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)0.001
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 score0.649
Threshold uncertainty score0.762

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.228
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 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

Citations12
Published2006
Admission routes2
Has abstractyes

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