High‐efficiency and high‐speed germanium photodetector enabled by multiresonant photonic crystal
Bibliographic record
Abstract
Abstract High‐efficiency and high‐speed photodetectors with broadband responses are playing pivotal roles for wavelength‐division multiplexing optical communications. Germanium photodetectors on silicon platforms exhibit potential cost advantage due to the compatibility for monolithic integration with silicon‐based electronic circuits for signal amplification and processing. In this article, we report a normal incidence, germanium photodetector enabled by guided‐mode resonances in photonic crystal, which successfully resolved the compromise between quantum efficiency, wavelength coverage and bandwidth requirement, a drawback usually faced by conventional photodetectors operating at normal incidence. The resonant photonic crystal structure is designed to support multiple resonances in the target wavelength range. With an intrinsic absorption layer thickness of 350 nm, the device achieved a high external quantum efficiency of 50% at 1550 nm, along with an enhancement around 300% for the entire C‐band. Using a mesa diameter of 14 μm, the fabricated device exhibited a 3‐dB bandwidth of 33 GHz and obtained clear eye diagrams at bit rate up to 56 Gbps. This work provides a promising method to design high‐efficiency, high‐speed, normal incidence germanium photodetectors for optical interconnect systems.
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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 | 0.000 |
| 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".