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Record W4410950393 · doi:10.1021/acs.nanolett.5c01777

Near-Unity Absorption in Semiconductor Metasurfaces Using Kerker Interference

2025· article· en· W4410950393 on OpenAlexafffund
Sasan V. Grayli, Tarun Patel, Brad van Kasteren, Sathursan Kokilathasan, Burak Tekcan, Man Chun Tam, William Fredrick Losin, Sarah Odinotski, Adam W. Tsen, Z. R. Wasilewski, Michael E. Reimer

Bibliographic record

VenueNano Letters · 2025
Typearticle
Languageen
FieldMaterials Science
TopicMetamaterials and Metasurfaces Applications
Canadian institutionsUniversity of Waterloo
FundersOntario Ministry of Research and InnovationNatural Sciences and Engineering Research Council of CanadaCanada First Research Excellence FundMitacsUniversity of WaterlooCanada Foundation for InnovationOntario Research FoundationInnovation, Science and Economic Development Canada
KeywordsOptoelectronicsPhotodetectorMaterials scienceOpticsAbsorption (acoustics)DetectorInterference (communication)ResonatorSubstrate (aquarium)WavelengthRefractive indexSemiconductorQuantum efficiencyPhotonicsPhysicsTelecommunicationsComputer science

Abstract

fetched live from OpenAlex

The ability to detect light with high efficiency is an important device metric for single-photon detectors and cameras, essential for applications ranging from quantum communication to biomedical imaging. However, these photodetectors have limited detection efficiency in the 850-1100 nm wavelength range, known as the 'valley of death'. Here, we demonstrate a near-perfect absorber in the 'valley of death' using a semiconductor metasurface with spectral and spatial selectivity on a high refractive index substrate. Our design leverages higher order optical modes of InGaAs resonators to generate Kerker interference at the target wavelength of 920 nm, which leads to a measured peak absorption efficiency of ∼94%. In addition, numerical calculations show that our design enables spatial control of the absorption profile within the resonators, which is promising for improving response time. Our approach offers tunability over a desired spectral range and paves the way for development of high-performance photodetectors.

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 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 score0.011
Threshold uncertainty score0.695

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0010.000

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 head0.035
GPT teacher head0.294
Teacher spread0.259 · 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
Published2025
Admission routes2
Has abstractyes

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