Resonant cavity enhanced photodetectors (RCE-PDs): structure, material, analysis and optimization
Bibliographic record
Abstract
Resonant cavity enhanced photodetectors (RCE-PDs) are promising candidates for applications in high-speed optical communications and interconnections. In these high-speed photodetectors, both high bandwidth and high external quantum efficiency can be achieved simultaneously because of the multipaths of the incident light due to the presence of the Fabry-Perot cavity into which the photodetector is inserted. In this paper, state-of-the-art RCE-PDs are discussed. Different structures of the RCE-PDs such as RCE-PIN, RCE-APD, and RCE-MSM PDs are presented and discussed. The material requirements for the RCE PDs with different material system compositions for the different structures and different wavelengths of the incident light that the photodetectors are sensitive to, are discussed. An overview of the analysis and a SPICE model of the RCE-PDs will be presented. These analyses include the calculations of quantum efficiency, (QE), impulse response and frequency response of RCE-PDs. These analyses are sensitive to the standing wave effect (SWE) and to carrier diffusion, and both effects are studied. Optimization procedures for the design of ultrafast RCE-PDs will be presented, showing how the QE, bandwidth and speed of these photodetectors can be improved by adjusting the parameters of both the cavity and the photodetector itself. Finally, comparisons to experimental results and a survey of the performance of state-of-the-art of RCE-PDs will be presented.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 | 0.001 |
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".