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Record W4414242379 · doi:10.1364/josab.574431

Experimental demonstration of a low-loss low-reflectivity TM-pass polarizer based on tilted subwavelength metamaterials

2025· article· en· W4414242379 on OpenAlexaff
Miguel Barona Ruiz, Carlos Pérez‐Armenta, Alejandro Ortega‐Moñux, J. Gonzalo Wangüemert‐Pérez, Íñigo Molina‐Fernández, Pavel Cheben, Robert Halir

Bibliographic record

VenueJournal of the Optical Society of America B · 2025
Typearticle
Languageen
FieldEngineering
TopicAdvanced Antenna and Metasurface Technologies
Canadian institutionsNational Research Council Canada
FundersMinisterio de Ciencia, Innovación y UniversidadesUniversidad de Málaga
KeywordsPolarizerExtinction ratioInsertion lossMetamaterialPolarization (electrochemistry)Bandwidth (computing)GratingSiliconSilicon photonics

Abstract

fetched live from OpenAlex

The silicon-on-insulator platform exhibits strong birefringence, often requiring single-polarization operation to ensure the optimal performance of integrated photonic systems. On-chip polarizers that suppress unwanted polarization states are key in this scenario. Achieving efficient TE-mode suppression with low-loss TM transmission is particularly challenging on the standard 220 nm silicon platform because of the stronger confinement of the TE mode compared to the TM mode. In this work, we experimentally demonstrate a TM-pass polarizer fabricated on a standard 220 nm silicon platform with a single etch step. By leveraging subwavelength metamaterials, we engineer a Bragg grating that reflects the fundamental TE mode into the first-order TE mode, which is radiated away, while allowing the TM mode to pass with minimal loss. The device achieves a measured extinction ratio exceeding 20 dB and insertion losses below 1 dB across a bandwidth of 170 nm, with a total footprint of 17.84 µm by 1.1 µm. We furthermore demonstrate, for the first time, to our knowledge, in Bragg–based polarizers, measured back-reflections for TE polarization below −18.9dB.

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 score0.023
Threshold uncertainty score0.395

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 head0.008
GPT teacher head0.255
Teacher spread0.246 · 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

Citations1
Published2025
Admission routes1
Has abstractyes

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