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Record W3082298087 · doi:10.48550/arxiv.2008.13332

Nonlinear Mid-infrared Metasurface based on a Phase-Change Material

2020· preprint· en· W3082298087 on OpenAlexaff
Fuyong Yue, Riccardo Piccoli, Mikhail Y. Shalaginov, Tian Gu, Kathleen Richardson, Roberto Morandotti, Juejun Hu, Luca Razzari

Bibliographic record

VenuearXiv (Cornell University) · 2020
Typepreprint
Languageen
FieldMaterials Science
TopicMetamaterials and Metasurfaces Applications
Canadian institutionsInstitut National de la Recherche Scientifique
Fundersnot available
KeywordsBroadbandInfraredOptoelectronicsNonlinear systemRefractive indexOpticsSecond-harmonic generationTransparency (behavior)Phase changeMetamaterialMaterials sciencePhysicsComputer scienceEngineering physicsLaser

Abstract

fetched live from OpenAlex

The mid-wave infrared (MWIR) spectral region (3-5 μm) is important to a vast variety of applications in imaging, sensing, spectroscopy, surgery, and optical communications. Efficient third-harmonic generation (THG), converting light from the MWIR range into the near-infrared, a region with mature optical detection and manipulation technologies, offers the opportunity to mitigate a commonly recognized limitation of current MWIR systems. In this work, we present the possibility of boosting THG in the MWIR through a metasurface design. Specifically, we demonstrate a 30-fold enhancement in a highly nonlinear phase change material Ge2Sb2Se4Te1 (GSST), by patterning arrays of subwavelength cylinders supporting a magnetic dipolar resonance. The unprecedented broadband transparency, large refractive index, and remarkably high nonlinear response, together with unique phase-change properties, make GSST-based metasurfaces an appealing solution for reconfigurable and ultra-compact nonlinear devices operating in the MWIR.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
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 score0.184
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.002

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 head0.180
GPT teacher head0.243
Teacher spread0.064 · 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; both teacher heads agree on what is shown here.

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

Citations2
Published2020
Admission routes1
Has abstractyes

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