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Record W2507366926 · doi:10.1364/ol.41.004324

Germanium-on-silicon mid-infrared grating couplers with low-reflectivity inverse taper excitation

2016· article· en· W2507366926 on OpenAlexaff
Carlos Alonso‐Ramos, Miloš Nedeljković, Daniel Benedikovič, Jordi Soler Penadés, Callum G. Littlejohns, Ali Z. Khokhar, Diego Pérez‐Galacho, Laurent Vivien, Pavel Cheben, Goran Z. Mashanovich

Bibliographic record

VenueOptics Letters · 2016
Typearticle
Languageen
FieldEngineering
TopicPhotonic and Optical Devices
Canadian institutionsNational Research Council Canada
FundersEngineering and Physical Sciences Research CouncilNational Research Foundation Singapore
KeywordsOpticsGermaniumMaterials scienceGratingSiliconInfraredOptoelectronicsExcitationDiffraction gratingPhysics

Abstract

fetched live from OpenAlex

A broad transparency range of its constituent materials and compatibility with standard fabrication processes make germanium-on-silicon (Ge-on-Si) an excellent platform for the realization of mid-infrared photonic circuits. However, the comparatively large Ge waveguide thickness and its moderate refractive index contrast with the Si substrate hinder the implementation of efficient fiber-chip grating couplers. We report for the first time, to the best of our knowledge, a single-etch Ge-on-Si grating coupler with an inversely tapered access stage, operating at a 3.8 μm wavelength. Optimized grating excitation yields a coupling efficiency of -11 dB (7.9%), the highest value reported for a mid-infrared Ge-on-Si grating coupler, with reflectivity below -15 dB (3.2%). The large periodicity of our higher-order grating design substantially relaxes the fabrication constraints. We also demonstrate that a focusing geometry allows a 10-fold reduction in inverse taper length, from 500 to 50 μm.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.404
Threshold uncertainty score0.535

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 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

Citations55
Published2016
Admission routes1
Has abstractyes

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