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Record W4390903198 · doi:10.1364/ol.510948

Inverse-designed silicon nitride reflectors

2024· article· en· W4390903198 on OpenAlexafffund
Julián L. Pita, Frédéric Nabki, Michaël Ménard

Bibliographic record

VenueOptics Letters · 2024
Typearticle
Languageen
FieldEngineering
TopicPhotonic and Optical Devices
Canadian institutionsÉcole de Technologie Supérieure
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMaterials scienceOpticsSilicon nitrideWaferOptoelectronicsPhotonicsSiliconLaserPhotonic integrated circuitSilicon photonicsPhysics

Abstract

fetched live from OpenAlex

Reflectors play a pivotal role in silicon photonics since they are used in a wide range of applications, including attenuators, filters, and lasers. This Letter presents six silicon nitride reflectors implemented using the inverse design technique. They vary in footprint, ranging from 4 µm × 3 µm to 4 µm × 8 µm. The smaller device has an average simulated reflectivity of -1.5 dB, whereas the larger one exhibits an average reflectivity of -0.09 dB within the 1530 to 1625 nm range. The latter also presents a 1-dB bandwidth of 172 nm, spanning from 1508 to 1680 nm. Despite their resemblance to circular gratings, these devices are more intricate and compact, particularly due to their non-intuitive features near the input waveguide, which include rough holes and teeth. The roughness of these features significantly contributes to the performance of the devices. The reflectors were fabricated on a silicon nitride multi-project wafer (MPW) through a streamlined process involving only a single etching step. The 4 µm × 8 µm reflector demonstrates a remarkably high reflectivity of -0.26±0.11 dB across the 1530 to 1600 nm range, rendering it suitable for high-quality factor cavities with direct applications in lasers and optical communications.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.850
Threshold uncertainty score0.617

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 head0.225
Teacher spread0.214 · 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

Citations9
Published2024
Admission routes2
Has abstractyes

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