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Record W4296271179 · doi:10.1364/prj.471534

Digitally tunable optical delay line based on thin-film lithium niobate featuring high switching speed and low optical loss

2022· article· en· W4296271179 on OpenAlexaff
Wei Ke, Yanmei Lin, Mingbo He, Mengyue Xu, Jiaxiang Zhang, Zhongjin Lin, Siyuan Yu, Xinlun Cai

Bibliographic record

VenuePhotonics Research · 2022
Typearticle
Languageen
FieldEngineering
TopicPhotonic and Optical Devices
Canadian institutionsUniversity of British Columbia
FundersNational Key Research and Development Program of China
KeywordsLithium niobateInsertion lossExtinction ratioOptical switchMaterials scienceOpticsInterferometryWaveguideSwitching timeOptical cross-connectOptoelectronicsOptical burst switchingMach–Zehnder interferometerOptical performance monitoringWavelength-division multiplexingOptical fiberPhysicsWavelength

Abstract

fetched live from OpenAlex

A tunable optical delay line (ODL) featuring high switching speed and low optical loss is highly desirable in many fields. Here, based on the thin-film lithium niobate platform, we demonstrate a digitally tunable on-chip ODL that includes five Mach–Zehnder interferometer optical switches, four flip-chip photodetectors, and four delay-line waveguides. The proposed optical switches can achieve a switching speed of 13 ns and an extinction ratio of 34.9 dB. Using a modified Euler-bend-based spiral structure, the proposed delay-line waveguide can simultaneously achieve a small footprint and low optical propagation loss. The proposed ODL can provide a maximum delay time of 150 ps with a resolution of 10 ps and feature a maximum insertion loss of 3.4 dB.

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 score0.004

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 communication0.0000.001
Open science0.0010.000
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 head0.024
GPT teacher head0.284
Teacher spread0.260 · 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

Citations52
Published2022
Admission routes1
Has abstractyes

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