MétaCan
Menu
Back to cohort
Record W3161299226 · doi:10.3390/nano11051304

High-Performance On-Chip Silicon Beamsplitter Based on Subwavelength Metamaterials for Enhanced Fabrication Tolerance

2021· article· en· W3161299226 on OpenAlexaff
Raquel Fernández de Cabo, David González‐Andrade, Pavel Cheben, Aitor V. Velasco

Bibliographic record

VenueNanomaterials · 2021
Typearticle
Languageen
FieldEngineering
TopicPhotonic and Optical Devices
Canadian institutionsNational Research Council Canada
FundersHorizon 2020Ministerio de Ciencia e InnovaciónComunidad de Madrid
KeywordsFabricationSplitterMaterials scienceBandwidth (computing)OptoelectronicsMetamaterialPhotonicsChipOpticsSiliconComputer sciencePhysicsTelecommunications

Abstract

fetched live from OpenAlex

Efficient power splitting is a fundamental functionality in silicon photonic integrated circuits, but state-of-the-art power-division architectures are hampered by limited operational bandwidth, high sensitivity to fabrication errors or large footprints. In particular, traditional Y-junction power splitters suffer from fundamental mode losses due to limited fabrication resolution near the junction tip. In order to circumvent this limitation, we propose a new type of high-performance Y-junction power splitter that incorporates subwavelength metamaterials. Full three-dimensional simulations show a fundamental mode excess loss below 0.1 dB in an ultra-broad bandwidth of 300 nm (1400-1700 nm) when optimized for a fabrication resolution of 50 nm, and under 0.3 dB in a 350 nm extended bandwidth (1350-1700 nm) for a 100 nm resolution. Moreover, analysis of fabrication tolerances shows robust operation for the fundamental mode to etching errors up to ±20 nm. A proof-of-concept device provides an initial validation of its operation principle, showing experimental excess losses lower than 0.2 dB in a 195 nm bandwidth for the best-case resolution scenario (i.e., 50 nm).

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 score0.010
Threshold uncertainty score0.971

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 head0.011
GPT teacher head0.222
Teacher spread0.211 · 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

Citations26
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueNanomaterialsSame topicPhotonic and Optical DevicesFrench-language works237,207