Memristive Physical Unclonable Functions: The State-of-the-Art Technology
Bibliographic record
Abstract
Internet of Things connected many useful electronic devices to each other through the internet, and sharing private and sensitive data between these devices needs secure access and communication.One of the best solutions for this purpose is hardware security primitives such as Physically Unclonable Functions (PUFs).PUFs are cryptographic primitives that are employed to produce a unique and reliable digital fingerprint for a particular electronic circuit.This digital fingerprint is used in many security applications such as chip identification, authentication, and secret key storage and generation.The emergence of memristors (Memory-Resistor) as new nanotechnologies are utilized extensively in hardware security applications such as Memristive PUFs.Research progress in Memristive PUFs resulted in improved performance metrics of PUFs due to memristors' unique characteristics.This article provides an investigation of different design approaches of Memristive PUFs that were introduced in the literature.Then, provide detailed performance evaluation results obtained by simulation and fabrication processes for different Memristive PUFs designs, and make a comparison between these results.Finally, concluded that most of the circuits are evaluated by simulation, whereas few other circuits were evaluated by fabrication owing to the expensive fabrication process.Since the memristor is a prototype and not commercialized yet, it is expected to be adopted and marketed in the next generation of hardware security.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".