Reliability Scaling of Nanoscale Avalanche Photodiodes in High-Speed Optical Communication
Bibliographic record
Abstract
In the present era of big data and 5G wireless, both microelectronic and photonic components are indispensable building blocks. For microelectronics, device miniaturization has been following Moore’s law to attain higher speed and greater functionality. For photonics, similar device scaling is also evolving in both lasers and photodiodes to transmit high data rates of 25 Gb/s and beyond. However, such device miniaturization may impose challenges such as reliability and fabrication that require careful scientific and engineering studies. In particular, the reliability understanding of photonic device scaling is fairly rudimentary with only scattered reports. In this paper, we study the device and reliability scaling of nanoscale avalanche photodiodes (APDs). The device miniaturization of APDs mainly involves thickness reduction in the charge control and multiplication layers. The layer reduction however causes an increase in breakdown field that may adversely affect reliability in several aspects such as electrical/optical overload and electrostatic discharge (ESD). We present a new reliability degradation model of APDs based on the breakdown field and correlate it with the experimental data. Empirical reliability equations are instituted to establish quantitative formulation. We discuss the overload and ESD performances as a function of breakdown field for both planar-type and mesa-type APD structures.
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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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.000 | 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".