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Record W4210296454 · doi:10.21203/rs.3.rs-1310958/v1

Integrated subwavelength gratings on a lithium niobate on insulator platform for mode and polarization manipulation

2022· preprint· en· W4210296454 on OpenAlexfundno aff
Xu Han, Li Chen, Yongheng Jiang, Andreas Frigg, Huifu Xiao, Thach G. Nguyen, Andreas Boes, Jianhong Yang, Guanghui Ren, Yikai Su, Arnan Mitchell, Yonghui Tian

Bibliographic record

VenueResearch Square · 2022
Typepreprint
Languageen
FieldEngineering
TopicPhotonic and Optical Devices
Canadian institutionsnot available
FundersFundamental Research Funds for the Central UniversitiesNational Natural Science Foundation of ChinaRMIT UniversityOntario Ministry of Natural Resources and Forestry
KeywordsLithium niobatePolarization (electrochemistry)Materials scienceInsulator (electricity)OptoelectronicsMode (computer interface)OpticsComputer sciencePhysicsChemistryHuman–computer interaction

Abstract

fetched live from OpenAlex

Abstract Lithium niobate on insulator (LNOI) has emerged as a promising platform for photonic integrated circuits, with a fast-growing toolbox of components. In this paper, we propose, design and experimentally demonstrate compact subwavelength grating (SWG) waveguides on a LNOI platform for on-chip mode and polarization manipulation. To overcome the limitation of waveguide fabrication, the SWGs are designed and formed on a silicon nitride thin film deposited onto the surface of LNOI chip. As proof-of-concept devices, the SWG-based spatial mode filters and a TM-pass polarizer are fabricated successfully on the same chip, with the device lengths of only ~50 μm. The measured insertion losses for the devices are lower than 3.1 dB, with high extinction ratio larger than 30 dB, at a wavelength of 1550 nm. The proposed and demonstrated SWGs can serve as important building blocks in a series of mode and polarization handling devices for LNOI integrated photonics.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.062
GPT teacher head0.347
Teacher spread0.286 · 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 source (direct Gemma or distilled Codex), 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

Citations2
Published2022
Admission routes1
Has abstractyes

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