RFID-Based Physical Unclonable Functions (PUFs) Enabled by Laser-Induced Graphene (LIG)
Bibliographic record
Abstract
The growing adoption of the Internet of Things (IoT) has intensified the demand for secure and scalable solutions against counterfeiting and duplication. Physical Unclonable Functions (PUFs) represent a promising security primitive, leveraging intrinsic manufacturing variability for tamper-resistant authentication. This work investigates the use of Laser-Induced Graphene (LIG) as a material platform to realize PUFs integrated into the electromagnetic response of passive RFID tags. A set of ten LIG-based dipole antennas was fabricated and characterized through backscatter measurements. The extracted fingerprints were digitized and evaluated using standard PUF metrics. The analysis revealed approximately 88 degrees of freedom, indicating a significant level of entropy and inter-device distinctiveness. The proposed approach requires no additional cryptographic hardware and remains fully compatible with standard RFID protocols. These findings support the feasibility of a hardwareefficient, scalable, and environmentally sustainable solution for secure identification in IoT and supply chain applications.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".