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Record W4408504619 · doi:10.1364/ao.555970

Inverse design of polymer-based polarization-insensitive wavelength demultiplexers

2025· article· en· W4408504619 on OpenAlexfundno aff
Leila Mehrvar, Eric Johlin

Bibliographic record

VenueApplied Optics · 2025
Typearticle
Languageen
FieldEngineering
TopicPhotonic and Optical Devices
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsOpticsPolarization (electrochemistry)Materials scienceWavelengthInverseRefractive indexOptoelectronicsPhysicsMathematics

Abstract

fetched live from OpenAlex

The unpredictable polarization of light in network fibers necessitates polarization-independent wavelength demultiplexing for reliable optical communications. This work combines polymer refractive index tunability with topology optimization-based inverse design to develop high-performance two- and four-channel polarization-insensitive wavelength demultiplexers (PIWDMs) for O-band and C-band operation. Optimization studies reveal that a polymer refractive index of 1.7 enables strong mode confinement and minimizes scattering loss, making it ideal for polymer-based PIWDMs. The best-performing two-channel PIWDM achieves an insertion loss (IL) below 1 dB, crosstalk (CT) under −17dB, and polarization-dependent loss (PDL) below 0.32 dB. Fabrication tolerance analysis confirms device robustness within a −10 to +20nm error range. Using optimized structures across different footprints, the four-channel PIWDM achieves an IL between 0.87 and 1.48 dB, a CT from −17.28 to −23.66dB, and a PDL below 0.61 dB. These PIWDMs offer an efficient, easily manufacturable solution for integrated photonic systems.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.855
Threshold uncertainty score0.572

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 head0.010
GPT teacher head0.208
Teacher spread0.198 · 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 designSimulation or modeling
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

Citations1
Published2025
Admission routes1
Has abstractyes

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