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Recent advances in metamaterial silicon photonic devices and Huygens‘ metawaveguides

2024· article· en· W4402124978 on OpenAlexaff
Pavel Cheben, Jens H. Schmid, Jianhao Zhang, M. Saad Bin-Alam, A. F. Hinestrosa, William D. Fraser, Radovan Korček, José Manuel Luque‐González, Casiano Armenta, Alejandro Sánchez‐Postigo, Alejandro Ortega‐Moñux, J. Gonzalo Wangüemert‐Pérez, Íñigo Molina‐Fernández, Robert Halir, Pablo Ginel‐Moreno, Daniel Benedikovič, Milan Dado, Shahrzad Khajavi, W. N. Ye, Zindine Mokeddem, Daniele Melati, Carlos Alonso‐Ramos, David González‐Andrade, Laurent Vivien, D. Sirmaci, Isabelle Staude, Dan‐Xia Xu, Yuri Grinberg, S. Janz, S. Wang, M. Vachon, Ross Cheriton, Raquel Fernández de Cabo, Aitor V. Velasco, Cameron M. Naraine, Jonathan D. B. Bradley, Andrew P. Knights

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicPhotonic and Optical Devices
Canadian institutionsMcMaster UniversityCarleton UniversityNational Research Council Canada
Fundersnot available
KeywordsMetamaterialPhotonicsSilicon photonicsSiliconOptoelectronicsComputer scienceOpticsPhysics

Abstract

fetched live from OpenAlex

Metamaterial engineering has become established as an essential design tool in silicon photonics. The utilization of state-of-the-art semiconductor manufacturing methods to create metamaterials within optical waveguides has provided unparalleled control over the manipulation of light propagation in silicon photonic chips [1–6]. In this invited presentation, we will review recent breakthroughs in this rapidly advancing field. Additionally, we will introduce a nascent research area of resonant integrated photonics, leveraging Mie resonances in dielectrics for on-chip guidance of optical waves, as exemplified by the recent demonstration of the first Huygens’ metawaveguide [7].

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)0.001
metaresearch head score (Gemma)0.001
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: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.010
GPT teacher head0.246
Teacher spread0.235 · 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
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

Citations0
Published2024
Admission routes1
Has abstractyes

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