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Record W4390692078 · doi:10.1109/tap.2024.3349685

ASIC-Enabled Programmable Metasurfaces—Part 1: Design and Characterization

2024· article· en· W4390692078 on OpenAlexaff
Kypros M. Kossifos, Julius Georgiou, Marco A. Antoniades

Bibliographic record

VenueIEEE Transactions on Antennas and Propagation · 2024
Typearticle
Languageen
FieldMaterials Science
TopicMetamaterials and Metasurfaces Applications
Canadian institutionsToronto Metropolitan UniversityUniversity of Toronto
FundersEuropean Regional Development Fund
KeywordsApplication-specific integrated circuitReconfigurabilityWavefrontPhysicsComputer scienceOpticsComputer hardwareTelecommunications

Abstract

fetched live from OpenAlex

A multifunctional and reconfigurable programmable metasurface (PMSF) is presented in this work that is enabled by an application-specific integrated circuit (ASIC). The enabling ASIC integrates control, digital-to-analog converters, and loading elements (LEs) to programmatically alter the response of the PMSF unit cells while minimizing power consumption and cost. These programmable unit cells subsequently comprise a larger PMSF that provides control of the reflected magnitude and phase for a given incident wave for both transverse electric (TE) and transverse magnetic (TM) polarizations. With this ability, the reflected wave can be set to zero, and both polarizations can be perfectly absorbed simultaneously and independently up to oblique angles of incidence of 60° for TE polarization and 70° for TM polarization. The PMSF finds applications in smart wireless environments by providing the capability to redirect incident waves in a programmable manner, while also enabling the perfect absorption of incident interfering waves and programmatically synthesizing complex and polarization agile wavefronts. The accompanying article, Part 2, focuses on the wavefront synthesis and performance, while in this article, the design and characterization are presented.

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: none
Teacher disagreement score0.556
Threshold uncertainty score0.588

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 communication0.0010.000
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 head0.030
GPT teacher head0.251
Teacher spread0.221 · 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

Citations13
Published2024
Admission routes1
Has abstractyes

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