Scalable and CMOS compatible silicon photonic physical unclonable functions for supply chain assurance
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.
Bibliographic record
Abstract
Abstract We demonstrate the uniqueness, unclonability and secure authentication of N = 56 physical unclonable functions (PUFs) realized from silicon photonic moiré quasicrystal interferometers. Compared to prior photonic-PUF demonstrations typically limited in scale to only a handful of unique devices and on the order of 10 false authentication attempts, this work examines > 10 3 inter-device comparisons and false authentication attempts. Device fabrication is divided across two separate fabrication facilities, allowing for cross-fab analysis and emulation of a malicious foundry with exact knowledge of the PUF photonic circuit design and process. Our analysis also compares cross-correlation based authentication to the traditional Hamming distance method and experimentally demonstrates an authentication error rate AER = 0%, false authentication rate FAR = 0%, and an estimated probability of cloning below 10 −30 . This work validates the potential scalability of integrated photonic-PUFs which can attractively leverage mature wafer-scale manufacturing and automated contact-free optical probing. Such structures show promise for authenticating hardware in the untrusted supply chain or augmenting conventional electronic-PUFs to enhance system security.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it