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Record W1986154263 · doi:10.1117/12.2176219

Group IV mid-infrared photonics

2015· article· en· W1986154263 on OpenAlexaff
Goran Z. Mashanovich, Miloš Nedeljković, Jordi Soler Penadés, C. J. Mitchell, Ali Z. Khokhar, Callum G. Littlejohns, S. Stanković, Benedetto Troia, Yu Wang, Scott A. Reynolds, Vittorio M. N. Passaro, Li Shen, N. Healy, Anna C. Peacock, Carlos Alonso‐Ramos, Alejandro Ortega‐Moñux, J. Gonzalo Wangüemert‐Pérez, Íñigo Molina‐Fernández, David J. Rowe, James S. Wilkinson, Pavel Cheben, Jason J. Ackert, Andrew P. Knights, David J. Thomson, Frédéric Y. Gardes

Bibliographic record

VenueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2015
Typearticle
Languageen
FieldEngineering
TopicPhotonic and Optical Devices
Canadian institutionsMcMaster UniversityNational Research Council Canada
Fundersnot available
KeywordsSilicon on insulatorMaterials scienceCladding (metalworking)OptoelectronicsSilicon photonicsPhotonicsResonatorOpticsWavelengthSiliconModulation (music)InfraredWaveguidePhysics

Abstract

fetched live from OpenAlex

In this paper we present SOI, suspended Si, and Ge-on-Si photonic platforms and devices for the mid-infrared. We demonstrate low loss strip and slot waveguides in SOI and show efficient strip-slot couplers. A Vernier configuration based on racetrack resonators in SOI has been also investigated. Mid-infrared detection using defect engineered silicon waveguides is reported at the wavelength of 2-2.5 μm. In order to extend transparency of Si waveguides, the bottom oxide cladding needs to be removed. We report a novel suspended Si design based on subwavelength structures that is more robust than previously reported suspended designs. We have fabricated record low loss Ge-on-Si waveguides, as well as several other passive devices in this platform. All optical modulation in Ge is also analyzed.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.013
Threshold uncertainty score0.044

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0130.004

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 head0.014
GPT teacher head0.223
Teacher spread0.209 · 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 designNot applicable
Domainnot available
GenreOther

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
Published2015
Admission routes1
Has abstractyes

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Same venueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIESame topicPhotonic and Optical DevicesFrench-language works237,207