Optoelectronic Physical Unclonable Functions and Reservoir-Inspired Computation with Low Symmetry Integrated Photonics
Bibliographic record
Abstract
Emerging applications of photonics in computing, sensing, and security increasingly demand complex input–output behaviors, including highly nonlinear transformations of optical signals. Traditional photonic systems rely on highly structured components with symmetric geometries and low-entropy modal responses to achieve predictable and analytically describable behavior. To achieve expressive functionality, this paradigm often requires large networks of fabrication-sensitive interferometers or resonators and substantial hardware error correction to restore deterministic operation. Here, we demonstrate an alternative paradigm rooted in low-symmetry, disordered integrated photonic circuits, which provide intrinsically enhanced modal diversity and spectral complexity, enabling highly nonlinear transformations of input signals into information-rich outputs. Our devices, physically unclonable moiré quasicrystal interferometers integrated on a silicon photonics platform, exhibit aperiodic and reconfigurable spectral responses and are characterized by analyticity breaking and erasable mutual information. Using dynamic thermo-optic control to drive their complex spectral dynamics, we demonstrate that these devices function as reconfigurable physical unclonable functions (rPUFs). We also highlight their ability to perform high-dimensional input–output transformations, emulating reservoir-inspired information processing in a compact photonic platform. This work bridges the gap between engineered and natural complexity in photonic systems, revealing new opportunities for scalable, energy-efficient, and information-dense optoelectronics with applications in secure communications, hardware security, advanced sensing, and optical information processing. Our results establish low-symmetry integrated photonics as a powerful resource for complex signal manipulation in photonic systems.
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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 | 0.000 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".