Effects of the parasitics on the time response of resonant-cavity-enhanced photodetectors (RCE-PDs)
Bibliographic record
Abstract
Resonant cavity enhanced photodetectors (RCE-PDs) are promising candidates for applications in high-speed optical communications and interconnections. The parasitics effects on these high-speed photodetectors must be carefully considered since they can significantly degrade the performance of the photodetector. In this paper, we will present a complete accurate model for the time response of the RCE-PDs. We will also study the effects of the parasitics of RCE-PDs on their time response and how we can compensate for the performance degradation from these parasitics. This study has been done for both RCE-PIN-PDs and RCE-avalanche photodetectors (RCE-APDs). RCE-separated absorption graded charge multiplication-APD was taken as an example of RCE-APDs. The time response of these RCE-PDs has better performance when compared to those of non RCE-PDs. The parasitics effects include the effects of both of the load resistance and the capacitance of the photodetector. The effects of the inductor that may be added in series with the load are also studied. It is shown that adding an external inductor results in higher performance of the photodetectors and this inductor can compensate some of the degradations resulting from other parasitics. The effects of the parasitics have been investigated for different dimensions of the photodetectors, different values of both the load resistance and the added inductor and also for different multiplication gains for the case of RCE-APDs.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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".