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Record W4403786979 · doi:10.1038/s41598-024-72922-x

Hardware assurance with silicon photonic physical unclonable functions

2024· article· en· W4403786979 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

VenueScientific Reports · 2024
Typearticle
Languageen
FieldComputer Science
TopicPhysical Unclonable Functions (PUFs) and Hardware Security
Canadian institutionsMcGill University
FundersNational Science Foundation
KeywordsPhysical unclonable functionPhotonicsComputer scienceSiliconOptoelectronicsComputer securityPhysicsCryptography

Abstract

fetched live from OpenAlex

In the modern landscape of optical communication networks, ensuring robust security is increasingly critical, particularly for applications requiring seamless integration and minimal attack surfaces. Photonic Physical Unclonable Functions (PUFs) leverage the response from the photonic devices that are prone to inherent physical variations to generate unique and unpredictable signature identifiers which are then utilized by an authentication system for identification or encryption purposes. These photonic PUFs can be cohesively integrated into systems that use optical communication, whereas using electronic PUFs would introduce additional vulnerabilities due to the need for signal-domain conversions between optical and electronic signals. In this paper, we present the design, fabrication, and experimental evaluation of advanced silicon-photonic-based PUFs utilizing Contra-Directional Coupler (CDC) structures. These structures offer a complex design space and are intrinsically sensitive to fabrication-process variations, making them ideal for creating unique and secure responses. We introduce several innovative design enhancements, including randomized corrugation functions, perforated designs, and ring-assisted CDCs, to increase the complexity and unpredictability of the CDC response. Measurement results from the fabricated CDCs demonstrate their capability to achieve an average Hamming distance threshold of over 0.2, effectively distinguishing between legitimate devices and their copies. We rigorously tested these fabricated designs against three different machine-learning-based attack scenarios. The results showed a Hamming distance of over 0.4 with a standard deviation of less than 0.01 at a quantization level of three, using 10,000 samples of challenge-response pairs. These findings underscore the potential of silicon photonic PUFs in enhancing security for optical communication systems of different scales. The integration of such photonic PUFs offers robust and reliable security solutions for applications where traditional electronic methods introduce additional attack surfaces and fail to provide adequate protection.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.416
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.003
Science and technology studies0.0010.000
Scholarly communication0.0020.002
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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.

Opus teacher head0.009
GPT teacher head0.229
Teacher spread0.219 · 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