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Record W4387577857 · doi:10.1063/5.0160505

Coupling-gap free integrated microresonator: Theory and experimental analysis

2023· article· en· W4387577857 on OpenAlexaff
Saawan Kumar Bag, Sauradeep Kar, P. Venkatachalam, Rajat Kumar Sinha, Shankar Kumar Selvaraja, Shailendra K. Varshney

Bibliographic record

VenueJournal of Applied Physics · 2023
Typearticle
Languageen
FieldEngineering
TopicPhotonic and Optical Devices
Canadian institutionsUniversity of Toronto
FundersDepartment of Science and Technology, Ministry of Science and Technology, India
KeywordsResonatorCoupling (piping)PhysicsWaveguideOpticsCoupled mode theoryChipOptoelectronicsMaterials scienceTelecommunicationsComputer scienceRefractive index

Abstract

fetched live from OpenAlex

Microring resonators (MRRs), typically comprising straight and ring waveguides, have played pivotal importance in recent years as far as integrated on-chip systems are concerned. The evanescent coupling in such MRR or solid microdisk resonators is very sensitive to the coupling gap between two waveguides, which affects the resonator’s performance. To overcome the stringent requirement of the gap between two waveguides, we propose a coupling-gap-free, on-chip ellipsoid microresonator. The theoretical framework has been deduced to attain the modal properties of such integrated microresonator geometry, which shows an excellent agreement with the finite difference time domain simulations and experimental results. The absence of a coupling region makes the device uniquely compact and robust, with an insertion loss of ∼5 dB. The resonator’s geometrical dimensions can also be conveniently scaled within certain constraints. The proposed device can be a potential alternative to MRRs and could help in applications such as optical filters, delay lines, on-chip sensing, and many more.

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: none
Teacher disagreement score0.001
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
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 head0.009
GPT teacher head0.233
Teacher spread0.224 · 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

Citations1
Published2023
Admission routes1
Has abstractyes

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