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Record W2912467467 · doi:10.1109/mwscas.2018.8623849

Reliability of Physical Unclonable Function under Temperature and Supply Voltage Variations

2018· article· en· W2912467467 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicPhysical Unclonable Functions (PUFs) and Hardware Security
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsReliability (semiconductor)Physical unclonable functionVoltageReliability engineeringCMOSProcess cornersProcess variationCadenceSignature (topology)Power (physics)Electronic engineeringComputer scienceThreshold voltageMaterials scienceElectrical engineeringEngineeringTransistorCryptographyPhysicsMathematics

Abstract

fetched live from OpenAlex

Physically Unclonable Function (PUF) has emerged as a cost-effective building block for crypto cores and security system. The unique signature of a PUF is primarily attributed to the process variations where the effects of other factors such as supply voltage, temperature and aging are considered to be minor. In this work, detail analysis to evaluate supply voltage and temperature effects on PUF reliability is presented. It is shown that the effects of supply voltage and temperature variations on PUF reliability can be comparable to the effects of process variations. It is also shown how temperature variation affects propagation delay of logic cells and consequently undermines PUF reliability. Simulation results using CMOS 0.18 μm technology in Cadence environment with ±10 power supply variations for a temperature range of -400°C to +70°C indicate that these effects can reduce PUF reliability by more than 58%.

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.

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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.642
Threshold uncertainty score0.482

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.007
GPT teacher head0.222
Teacher spread0.215 · 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