Self-configuring programmable silicon photonic filter for integrated microwave photonic processors
Bibliographic record
Abstract
Reconfigurable photonic filters show great promise as a potential solution to meet the evolving needs of future microwave communication systems. By integrating high-performance filters into programmable microwave photonic processors, they can provide significant benefits for signal processing applications. The development of an algorithm that can automatically characterize and reconfigure the filter using a single optical input and output port is essential for this purpose. This paper presents an optimization technique for a fully tunable ring-assisted Mach–Zehnder interferometer filter. The proposed filter design eliminates the need for monitoring components and employs a novel algorithm that operates independently in each ring by switching between the two arms of the filter. In addition, the filter can be configured to implement different filter architectures, allowing for flexible filtering requirements. Measurements were performed using the device as an interleaver, implementing different types of infinite impulse response filters in the optical and radio frequency domains. Side-coupled integrated spaced sequence of resonator filters were also implemented by reconfiguring the same device. These results demonstrate the exceptional reconfigurability of the filter design proposed herein in terms of bandwidth and central frequency.
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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".