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Record W4387666681 · doi:10.1364/oe.503072

Investigating the thermal robustness of soliton crystal microcombs

2023· article· en· W4387666681 on OpenAlexaff
Caitlin E. Murray, Mengxi Tan, Chawaphon Prayoonyong, Xiaotian Zhu, Sai T. Chu, Brent E. Little, Roberto Morandotti, Arnan Mitchell, David Moss, Bill Corcoran

Bibliographic record

VenueOptics Express · 2023
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced Fiber Laser Technologies
Canadian institutionsInstitut National de la Recherche Scientifique
FundersAustralian Research Council
KeywordsSolitonOpticsThermalRobustness (evolution)ResonatorPhysicsCrystal (programming language)Mode (computer interface)Materials scienceOptoelectronicsNonlinear systemQuantum mechanicsComputer science

Abstract

fetched live from OpenAlex

Soliton crystals are a novel form of microcomb, with relatively high conversion efficiency, good thermal robustness, and simple initiation among the methods to generate them. Soliton crystals can be easily generated in microring resonators with an appropriate mode-crossing. However, fabrication defects can significantly affect the mode-crossing placement and strength in devices. To enable soliton crystal states to be harnessed for a broader range of microcomb applications, we need a better understanding of the link between mode-crossing properties and the desired soliton crystal properties. Here, we investigate how to generate the same soliton crystal state in two different microrings, how changes in microring temperature change the mode-crossing properties, and how mode-crossing properties affect the generation of our desired soliton crystal state. We find that temperature affects the mode-crossing position in these rings but without major changes in the mode-crossing strength. We find that our wanted state can be generated over a device temperature range of 25 ∘ C, with different mode-crossing properties, and is insensitive to the precise mode-crossing position between resonances.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.249
Threshold uncertainty score0.341

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.018
GPT teacher head0.251
Teacher spread0.234 · 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 teacher head, 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

Citations30
Published2023
Admission routes1
Has abstractyes

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