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Record W2995926144 · doi:10.1364/oe.27.038509

Photonic crystal slab edge directional coupler for deflection sensing

2019· article· en· W2995926144 on OpenAlexafffund
Michael Zylstra, Aref Bakhtazad, Jayshri Sabarinathan

Bibliographic record

VenueOptics Express · 2019
Typearticle
Languageen
FieldEngineering
TopicPhotonic and Optical Devices
Canadian institutionsWestern University
FundersNatural Sciences and Engineering Research Council of CanadaCanada Foundation for InnovationGovernment of OntarioCMC Microsystems
KeywordsMaterials scienceOpticsPower dividers and directional couplersPhotonic crystalWaferOptoelectronicsSilicon on insulatorFabricationSiliconPhysics

Abstract

fetched live from OpenAlex

The design, fabrication, and transmission measurements of a photonic crystal slab edge directional coupler (PCDC) for submicron deflection sensing is presented. The dielectric modes between two structurally isolated photonic crystal edges allows for a directional coupler to be formed with low insertion loss and reduced coupling distances. The output transmission from the coupler can be related to the distance between neighbouring PC edges, thereby allowing it to be used as an intensity-based optomechanical sensor. PCDC sensors were fabricated by selectively etching the buried oxide (BOX) of surface micromachined silicon-on-insulator wafer. Based on transmission measurements, the sensitivity to horizontal separation between the edges of a fabricated PCDC of length 24.3 µm was evaluated to be 1.6 %/nm at 1495 nm. The transmission sensitivity to vertical separation between the PCDC edges of length 12.6 µm was calculated to be 0.25 %/nm, when the PCDC edges were initially displaced vertically by a distance of 300 nm. The PCDC sensors demonstrated here are compatible with broadband sources and do not depend on BOX thickness, reducing the probability of stiction.

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 integrity0.0000.001
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 head0.012
GPT teacher head0.229
Teacher spread0.218 · 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
Published2019
Admission routes2
Has abstractyes

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