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Record W4402495529 · doi:10.3788/col202422.092201

Low-loss and broadband complementary dual-output electro-optic modulator based on thin-film lithium niobate

2024· article· en· W4402495529 on OpenAlexfundno aff
Ting’an Li, Chenglin Shang, Xuanhao Wang, Weiqiang Lü, Zhiyao Zhang, Cheng Zeng, Yong Liu, Jinsong Xia

Bibliographic record

VenueChinese Optics Letters · 2024
Typearticle
Languageen
FieldPhysics and Astronomy
TopicPhotorefractive and Nonlinear Optics
Canadian institutionsnot available
FundersNational Key Research and Development Program of ChinaWuhan National Laboratory for OptoelectronicsNational Natural Science Foundation of ChinaCollege of Family Physicians of Canada
KeywordsLithium niobateMaterials scienceBroadbandOpticsThin filmElectro-optic modulatorInsertion lossOptical modulatorOptoelectronicsPhase modulationPhysicsNanotechnology

Abstract

fetched live from OpenAlex

Broadband, low-drive voltage electro-optic modulators are crucial optoelectronic components in the new-generation microwave photonic links and broadband optical interconnect network applications.In this paper, we fabricate a low-loss thinfilm lithium niobate complementary dual-output electro-optic modulator chip with a 3 dB electro-optic bandwidth of 59 GHz and a half-wave voltage (Vπ) of 2.5 V.The insert-loss of the packaged modulator is 4.2 dB after coupling with polarizationmaintaining fiber.The complementary dual-output modulator also shows a common-mode rejection ratio of 18 dB and a signal enhancement of 6.2 dB when adapted in microwave photonic links, comparable to commercial bulk lithium niobate devices.

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 head0.007
GPT teacher head0.246
Teacher spread0.239 · 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

Citations8
Published2024
Admission routes1
Has abstractno

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