Synchronization of silicon thermal free-carrier oscillators
Bibliographic record
Abstract
Recent exploration of collective phenomena in oscillator arrays has highlighted the potential to access a range of physical phenomena, from fundamental quantum many-body dynamics to the solution of practical optimization problems using photonic Ising machines. Spontaneous oscillations often arise in these oscillator arrays as an imbalance between gain and loss. Due to coupling between individual arrays, the spontaneous oscillation is constrained and leads to interesting collective behavior, such as synchronized oscillations in optomechanical oscillator arrays, ferromagnetic-like coupling in delay-coupled optical parametric oscillators, and binary phase states in coupled laser arrays. A key aspect of arrays is not only the coupling between the individuals but also their compliance toward neighbor stimuli. One self-sustaining photonic oscillator that can be readily implemented in a scalable foundry-based technology is based on the interaction of free carriers, temperature, and the optical field of a resonant silicon photonic microcavity. Here, we demonstrate that these silicon thermal free-carrier (FC) oscillators are extremely compliant to external excitation and can be synchronized up to their 16th harmonic using a weak seed. Exploring this unprecedented compliance to external stimuli, we also demonstrate robust synchronization between two thermal FC oscillators.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".