Modeling and optimization of resonant cavity enhanced-separated absorption graded charge multiplication-avalanche photodetector (RCE-SAGCM-APD)
Bibliographic record
Abstract
In this paper, a physical model of the Resonant Cavity Enhanced-Separated Absorption Graded Charge Multiplication-Avalanche Photodetector (RCE-SAGCM-APD) is presented. First, a SPICE model for RCE-SAGCM-APD is presented showing the dependence of the transfer function of this model on the dimensions, the material parameters and the multiplication gain of the photodetector. The results obtained from this SPICE model are compared with published experimental results and good agreement is obtained. The present SPICE model can also be applied to non RCE-APD and to different versions of avalanche photodetectors by modifying its transfer function. The gain-bandwidth characteristic of RCE-APD is studied for different areas and different values of the thicknesses of both the absorption and the multiplication layers. The gain-bandwidth characteristic of RCE-SAGCM-APD is studied for the case of an inductor added in series to the load resistance and better performance is achieved in comparison to the case with no inductance. The photodetector with and without the inductor is optimized to get the best values of thicknesses of both absorption and multiplication layers and also the optimal values of the series inductance. These optimizations are done for different areas of the photodetector, different multiplication gains and also for different load resistances.
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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) | 0.001 | 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 | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".