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Record W2115042098 · doi:10.1109/lpt.2008.919600

Proposal for Compact Optical Filters Using Large Index Step Binary Supergratings

2008· article· en· W2115042098 on OpenAlexafffund
Farzaneh Afshinmanesh, Alireza Marandi, P.P.M. So, Reuven Gordon

Bibliographic record

VenueIEEE Photonics Technology Letters · 2008
Typearticle
Languageen
FieldEngineering
TopicPhotonic and Optical Devices
Canadian institutionsUniversity of Victoria
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsBinary numberOptical filterAperiodic graphPhotolithographyRefractive indexOpticsGratingFilter (signal processing)WavelengthTransmission (telecommunications)Materials scienceComputer scienceOptoelectronicsPhysicsMathematicsTelecommunications

Abstract

fetched live from OpenAlex

Compact optical filters are proposed using an aperiodic grating of fixed element size [i.e., a binary supergrating (BSG)] with a large refractive index step. These filters allow for almost arbitrary wavelength filtering, yet they are more compact than previous demonstrations of BSG. The filters are designed using a combination of Boolean particle swarm optimization (B-PSO) and a one-dimensional transfer matrix method. To demonstrate the compact device size, several 50-mum-long single-wavelength transmission filters are demonstrated theoretically, each having a different wavelength while using the same structural parameters. A multiwavelength filter is also proposed in an 80-mum-long structure to show the versatility of the large refractive index step BSG. A genetic algorithm is substituted for the B-PSO; however, B-PSO shows better performance here. This work may be applied to produce compact optical filters for intrachip optical networks and compact tunable lasers, while using existing single-step photolithography processes.

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 categoriesMeta-epidemiology (narrow)
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 score0.820
Threshold uncertainty score1.000

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 integrity0.0000.001
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 head0.013
GPT teacher head0.238
Teacher spread0.224 · 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.

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

Citations6
Published2008
Admission routes2
Has abstractyes

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