Laser micromachining of contactless RF antenna modules for payment cards and wearable objects
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".