Gain-enabled optical delay readout unit using CMOS-compatible avalanche photodetectors
Bibliographic record
Abstract
A compact time delay unit is fundamental to integrated photonic circuits with applications in, for example, optical beam-forming networks, photonic equalization, and finite and infinite impulse response optical filtering. In this paper, we report a novel gain-enabled delay readout system using a tunable optical carrier, low-frequency RF signal and CMOS-compatible photodetectors, suitable for silicon photonic integration. The characterization method relies on direct phase measurement of an input RF signal and thereafter extraction of the delay profile. Both integrated silicon and germanium photodetectors coupled with low-bandwidth electronics are used to characterize a microring resonator-based, true-time delay unit under distinct ring–bus coupling formats. The detectors, used in both linear and avalanche mode, are shown to be successful as optical-to-electrical converters and RF amplifiers without introducing significant phase distortion. For a Si–Ge separate-absorption-charge-multiplication avalanche detector, an RF amplification of 10 dB is observed relative to a Ge PIN linear detector. An all-silicon defect-mediated avalanche photodetector is shown to have a 3 dB RF amplification compared to the same PIN detector. All ring delay measurement results are validated by full-wave simulation. Additionally, the impact of photodetector biasing and system linearity is analyzed.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 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".