Monolithically integrated photodetectors for optical signal monitoring in silicon waveguides
Bibliographic record
Abstract
The development of monolithic silicon photonic systems has been the subject of intense research over the last decade. In addition to passive waveguiding structures suitable for DWDM applications, integration of electrical and optical functionality has yielded devices with the ability to dynamically attenuate, switch and modulate optical signals. However, for silicon to dominate as the substrate of choice for the fabrication of photonic circuits, the development of a full range of monolithically integrated functionality is required including detectors capable of signal monitoring at a wavelength around 1550nm. Photodetectors integrated with silicon-on-insulator rib waveguides are here demonstrated. Significant response at infrared wavelengths is shown to be mediated via deliberately introduced deep band-gap levels. This paper describes in detail the device fabrication and the performance of the waveguide photodetectors with regard to photoresponse, bandwidth, polarization sensitivity and thermal stability. Currently typical devices tap between 10-20% of an optical signal from an SOI waveguide and generate a photocurrent of several micro-amps. The most efficient device extracted 19% of the optical signal while exhibiting a responsivity of 3mA/W. We also describe results from the operation of an integrated photonic circuit consisting of a variable optical attenuator (VOA) and a photodetector. The detector monitors the optical signal as it is modulated using the VOA, however there exists a small, systematic offset in response as compared to measurements made with an external detector.
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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 | 0.001 | 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".