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Record W4392748544 · doi:10.1117/12.3000757

Recent advances in integrated photonics with subwavelength and resonant metamaterials

2024· article· en· W4392748544 on OpenAlexaff
Jens H. Schmid, Pavel Cheben, Jianhao Zhang, Maziyar Milanizadeh, Saad Bin-Alam, Dan‐Xia Xu, Ross Cheriton, Martin Vachon, Shurui Wang, Rubin Ma, William D. Fraser, Shahrzad Khajavi, Winnie N. Ye, Pablo Ginel Moreno, Jose-Manuel Luque-González, A. F. Hinestrosa, Alejandro Sánchez‐Postigo, Robert Halir, J. Gonzalo Wangüemert‐Pérez, Alejandro Ortega‐Moñux, Íñigo Molina‐Fernández, Zindine Mokkedem, Daniele Melati, Carlos Alonso‐Ramos, Laurent Vivien, Radovan Korček, Daniel Benedikovič, Cameron M. Naraine, Jonathan D. B. Bradley, Yunus Denizhan Sırmacı, Isabelle Staude

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicPhotonic and Optical Devices
Canadian institutionsMcMaster UniversityCarleton UniversityNational Research Council Canada
Fundersnot available
KeywordsMetamaterialPhotonicsNanophotonicsQuantum opticsSilicon photonicsFocus (optics)Computer sciencePhysicsOptoelectronicsOptics

Abstract

fetched live from OpenAlex

Over the past 15 years since their first demonstration, subwavelength grating metamaterials in silicon photonic devices have become widely used and attracted rapidly growing research interest while also breaking into commercial applications. We will discuss recent advances in this research field, with a focus on novel components and circuits for beam steering applications, on-chip filtering and quantum optics. On-chip optical waveguides comprised of Mie resonant particle chains have only recently been demonstrated and promise to be the foundation of a new and exciting branch of integrated metamaterials research. We will review the early work in this area.

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: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.009

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 science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.007
GPT teacher head0.224
Teacher spread0.216 · 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
GenreReview

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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