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Record W4282823191 · doi:10.1002/adom.202200721

Reconfigurable Parfocal Zoom Metalens

2022· article· en· W4282823191 on OpenAlexaff
Fan Yang, Hung‐I Lin, Mikhail Y. Shalaginov, Katherine Stoll, Sensong An, Clara Rivero‐Baleine, Myungkoo Kang, Anu Agarwal, Kathleen Richardson, Hualiang Zhang, Tian Gu

Bibliographic record

VenueAdvanced Optical Materials · 2022
Typearticle
Languageen
FieldMaterials Science
TopicMetamaterials and Metasurfaces Applications
Canadian institutionsLockheed Martin (Canada)
FundersDivision of Materials ResearchAdvanced Research Projects AgencyDefense Advanced Research Projects AgencySkolkovo Institute of Science and TechnologyHarvard UniversityMassachusetts Institute of TechnologyU.S. Department of Defense
KeywordsZoomOpticsFocal lengthZoom lensDistortion (music)Materials scienceLens (geology)DiffractionPolarization (electrochemistry)MultiplexingComputer scienceOptoelectronicsPhysicsAmplifier

Abstract

fetched live from OpenAlex

Abstract Zoom lenses with variable focal lengths and magnification ratios are essential for many optical imaging applications. Conventional zoom lenses are composed of multiple refractive optics, and optical zoom is attained via translational motion of one or more lens elements, which adds to module size, complexity, and cost. In this paper, a zoom lens design based on multi‐functional optical metasurfaces is presented, which achieves large step zoom ratios, minimal distortion, and diffraction‐limited optical quality without requiring mechanical moving parts. Two embodiments of the concept are experimentally demonstrated based on polarization‐multiplexing in the visible and phase change materials in the mid‐infrared, both yielding 10× parfocal zoom in accordance with the design.

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: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

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)0.0030.001

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 head0.019
GPT teacher head0.261
Teacher spread0.242 · 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

Citations44
Published2022
Admission routes1
Has abstractyes

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