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Record W4410518734 · doi:10.1021/acsphotonics.5c00505

Scalable Freeform Optimization of Wide-Aperture 3D Metalenses by Zoned Discrete Axisymmetry

2025· article· en· W4410518734 on OpenAlexaff
Mengdi Sun, Arvin Keshvari, Dimitrios Giannakopoulos, Qing Wang, Wei-Ting Chen, Steven G. Johnson, Zin Lin

Bibliographic record

VenueACS Photonics · 2025
Typearticle
Languageen
FieldMaterials Science
TopicMetamaterials and Metasurfaces Applications
Canadian institutionsToronto Metropolitan University
FundersArmy Research OfficeSimons FoundationNaval Air Warfare Center, Aircraft DivisionU.S. Department of Energy
KeywordsMaterials scienceOpticsAperture (computer memory)ScalabilityComputer scienceOptoelectronicsPhysicsAcoustics

Abstract

fetched live from OpenAlex

We introduce a novel framework for the design and optimization of 3D freeform metalenses that attains nearly linear scaling of computational cost with diameter by breaking the lens into a sequence of radial “zones” with n -fold discrete axisymmetry, where n increases with radius. This allows vastly more design freedom than imposing continuous axisymmetry while avoiding the compromises of locally periodic approximation or scalar diffraction theory. Using a GPU-accelerated finite-difference time-domain solver in cylindrical coordinates, we perform full-wave simulation and topology optimization within each supra-wavelength zone. We validate our approach by designing millimeter- and centimeter-scale polyachromatic, 3D freeform metalenses, which outperform the state-of-the-art. By demonstrating the scalability and resulting optical performance enabled by our “zoned discrete axisymmetry” and supra-wavelength domain decomposition, we highlight the potential of our framework to advance large-scale metaoptics and next-generation photonic technologies.

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 score0.082
Threshold uncertainty score0.835

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 head0.007
GPT teacher head0.244
Teacher spread0.237 · 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 routes1
Has abstractyes

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