Autonomous on-chip interferometry for reconfigurable optical waveform generation
Bibliographic record
Abstract
The generation of user-defined optical temporal waveforms with picosecond resolution is an essential task for many applications, ranging from telecommunications to laser engineering. Realizing this functionality in an on-chip reconfigurable platform remains a significant challenge. Towards this goal, autonomous optimization methods are fundamental to counter fabrication imperfections and environmental variations, as well as to enable a wider range of accessible waveform shapes and durations. In this work, we introduce and demonstrate a self-adjusting on-chip optical pulse-shaper based on the concept of temporal coherence synthesis. The scheme enables on-the-fly reconfigurability of output optical waveforms by using an all-optical sampling technique in combination with an evolutionary optimization algorithm. We further show that particle-swarm optimization can outperform more commonly used algorithms in terms of convergence time. Hence, our system combines all key ingredients for realizing fully on-chip smart optical waveform generators for next-generation applications in telecommunications, laser engineering, and nonlinear optics.
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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 | 0.001 | 0.001 |
| 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".