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Record W2744775842 · doi:10.2351/1.5118613

Laser micromachining of contactless RF antenna modules for payment cards and wearable objects

2016· article· en· W2744775842 on OpenAlexaff
A. Conneely, Gerard M. O’Connor, Matthias John, Max J. Ammann, Darren Molloy, Mustafa Lotya, David Finn

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicSemiconductor Lasers and Optical Devices
Canadian institutionsTrinity College
Fundersnot available
KeywordsSurface micromachiningWearable computerAntenna (radio)Electrical engineeringComputer scienceTelecommunicationsEngineeringEmbedded systemFabrication

Abstract

fetched live from OpenAlex

The use of contactless payment methods for consumer transactions is becoming increasingly popular - 1.1 billion contactless transactions were made by Visa cardholders across Europe in the 12 months to July 2015 (€12.6 billion total value). Typically the contactless payment process uses a Radio Frequency (RF) enabled smartcard or a Near Field Communication (NFC) enabled smartphone. In order to ensure continued market acceptance and repeat usage the contactless operation must be robust, quick and efficient. This paper describes the development of an inductively coupled contactless smartcard utilising UV DPSS laser micromachining to fabricate the novel antenna structures from copper laminated epoxy tape. The design of the antenna modules was supported by device modelling using electromagnetic simulation software. Iterative laser ablated antenna prototypes were tested using a Vector Network Analyser to determine the optimum resonant frequency in the 13.56 MHz RFID range and a commercial automated RF test station to measure contactless functionality to EMVCO and ISO14443 standards. An antenna design toolkit was developed based on parameters such as kerf width, number of antenna loops, track width, pitch, antenna DC resistance, etc. The translation of the laser ablated antenna designs from smartcards to wearable objects, such as wristbands, is also 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)0.000
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 score0.015
Threshold uncertainty score0.224

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.010
GPT teacher head0.215
Teacher spread0.205 · 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

Citations1
Published2016
Admission routes1
Has abstractyes

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