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Record W4223630207 · doi:10.1038/s41598-022-09772-y

The first family of application-specific integrated circuits for programmable and reconfigurable metasurfaces

2022· article· en· W4223630207 on OpenAlexaff
Loukas Petrou, Kypros M. Kossifos, Marco A. Antoniades, Julius Georgiou

Bibliographic record

VenueScientific Reports · 2022
Typearticle
Languageen
FieldMaterials Science
TopicMetamaterials and Metasurfaces Applications
Canadian institutionsToronto Metropolitan University
FundersEuropean Regional Development FundHorizon 2020 Framework ProgrammeResearch and Innovation FoundationEuropean Commission
KeywordsControl reconfigurationElectronic circuitComputer scienceField-programmable gate arrayAsynchronous communicationCMOSChipMicrowaveElectronicsIntegrated circuitDigital electronicsPrinted circuit boardHigh impedanceChange control boardElectronic engineeringEmbedded systemElectrical impedanceElectrical engineeringTelecommunicationsEngineering

Abstract

fetched live from OpenAlex

Reconfigurable metasurfaces are man-made surfaces, which consist of sub-wavelength periodic elements-meta-atoms-that can be reconfigured to manipulate incoming electromagnetic waves. However, reconfigurable metasurfaces developed to-date, have limitations in terms of loading impedance range, reconfiguration delay and power consumption. Also, these systems are costly and they require bulky electronics and complex control circuits, which makes them unattractive for commercial use. Here, we report the first family of CMOS application-specific integrated circuits that enable microsecond and microwatt reconfiguration of complex impedances at microwave frequencies. Our approach utilizes asynchronous digital control circuitry with chip-to-chip communication capabilities, allowing simple and fast reconfiguration via digital devices and user-friendly software. Our solution is low-cost and can cover arbitrary board-to-board metasurfaces, with different sizes and shapes.

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)0.006
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.635
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.000
Scholarly communication0.0000.000
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 head0.027
GPT teacher head0.247
Teacher spread0.220 · 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.

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

Citations25
Published2022
Admission routes1
Has abstractyes

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