Silicon-Based Integrated Tunable Fractional Order Photonic Temporal Differentiators
Bibliographic record
Abstract
Two integrated fractional-order photonic temporal differentiators based on two Mach-Zehnder interferometer (MZI) structures implemented on a silicon-on-insulator (SOI) platform are designed, fabricated, and experimentally evaluated. The first photonic temporal differentiator employs a multimode interference (MMI) coupler as one of the two 3-dB couplers of the MZI. By changing the polarization state of the input optical signal, the coupling coefficient of the MMI is changed, which leads to the change of the phase shift in the destructive interference wavelength, and a photonic temporal differentiator with a tunable fractional order is implemented. The second photonic temporal differentiator is designed to have two cascaded MZIs, a balanced MZI, and an unbalanced MZI. A phase modulator (PM) is incorporated in one of the two arms of each of the MZIs. The balanced MZI with a PM forms an active tunable coupler, which is used to actively tune the fractional order of the temporal differentiator. The PM in the unbalanced MZI is used to tune the operating wavelength. The two photonic temporal differentiators are designed and fabricated in a CMOS compatible SOI platform, and their performance is evaluated experimentally. The experimental results show that both temporal differentiators can have a tunable fractional order from 0 to 1. In addition, the use of the active temporal differentiator to perform high-speed coding with a data rate of 16 Gb/s is experimentally evaluated.
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How this classification was reachedexpand
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 | 0.000 | 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 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".