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Record W3034469531 · doi:10.1063/5.0004552

All-optical nanophotonic resonant element for switching and routing applications exploiting graphene saturable absorption

2020· article· en· W3034469531 on OpenAlexaff
Thomas Christopoulos, Vasileios G. Ataloglou, Emmanouil E. Kriezis

Bibliographic record

VenueJournal of Applied Physics · 2020
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced Fiber Laser Technologies
Canadian institutionsUniversity of Toronto
FundersHellenic Foundation for Research and Innovation
KeywordsSaturable absorptionGrapheneMaterials scienceResonatorOptoelectronicsExtinction ratioBandwidth (computing)NanophotonicsOpticsTelecommunicationsNanotechnologyPhysicsComputer scienceWavelength

Abstract

fetched live from OpenAlex

A silicon disk resonator overlaid with a uniform graphene layer in an add-drop configuration is proposed as an all-optical routing element. Operation is based on the saturable absorption effect provided by the graphene layer. The element is thoroughly analyzed as a two-channel device in the context of an appropriate nonlinear framework combining perturbation theory and temporal coupled-mode theory. Taking into consideration the primary nonlinear effect, which is graphene saturable absorption, a design path is carefully developed that eventually leads to a traveling-wave resonant element with low-power requirements, low insertion loss, high extinction ratio, and sufficient bandwidth. In a subsequent step, other important nonlinear effects originating from graphene and the silicon disk, including the Kerr effect and free-carrier effects, are considered and means for counterbalancing their action are demonstrated. A low control power of 9mW together with a bandwidth of 20GHz is shown possible, with the insertion loss of almost 3dB and an extinction ratio over 10dB in both ports (add and drop).

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: none
Teacher disagreement score0.618
Threshold uncertainty score0.538

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 head0.020
GPT teacher head0.255
Teacher spread0.235 · 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

Citations6
Published2020
Admission routes1
Has abstractyes

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