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Record W4285083735 · doi:10.1364/ao.465897

Microwave frequency measurement using a silicon integrated microring resonator

2022· article· en· W4285083735 on OpenAlexaff
Ruidong Cao, Yuting He, Ruiqi Zheng, Ziwen He, Yanyan Zhi, Xudong Wang, Jiejun Zhang, Jianping Yao

Bibliographic record

VenueApplied Optics · 2022
Typearticle
Languageen
FieldEngineering
TopicAdvanced Photonic Communication Systems
Canadian institutionsUniversity of Ottawa
FundersNational Key Research and Development Program of ChinaNational Natural Science Foundation of China
KeywordsMicrowaveResonatorSIGNAL (programming language)OpticsOptical Carrier transmission ratesMaterials sciencePhotonicsSilicon photonicsOptoelectronicsSiliconPhysicsTelecommunicationsOptical fiberComputer science

Abstract

fetched live from OpenAlex

Photonics-assisted instantaneous frequency measurement of a microwave signal using a silicon integrated microring resonator (MRR) is proposed and experimentally demonstrated. The frequency of a microwave signal has a unique relationship with the power ratio between the two microwave signals at the outputs of two microwave photonic filters (MPF) with complementary frequency responses. The key device to implement the MPFs is a silicon integrated MMR, which is employed to convert a phase-modulated optical signal to an intensity-modulated optical signal by placing two optical carriers at the complementary slopes of the MRR. For a given frequency measurement range and resolution, an MRR is designed and fabricated, and its use for instantaneous microwave frequency (IMF) measurement is implemented. For the fabricated MRR, an IMF measurement range of 14–25 GHz with a measurement accuracy of ± 0.2 G H z is achieved.

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)0.001
metaresearch head score (Gemma)0.001
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
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.000
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 head0.037
GPT teacher head0.230
Teacher spread0.193 · 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

Citations7
Published2022
Admission routes1
Has abstractyes

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